World Journal of Future Technologies in Computer Science and Engineering (WJFTCSE) U.S. ISSN: 3070-6203
Title: World Journal of Future Technologies in Computer Science and Engineering (WJFTCSE) Publisher: Wissira Research Lab Publisher Address: Carmel, United States- 46074 E-mail to submit manuscripts: editor@wjftcse.org Publisher Email: editor@wissira.us ISSN (online): 3070-6203 Starting Year: 2025 Main Subject: Computer Science Scope: Future Technologies in Computer Science and Engineering Language: English Publication Format: Online (Open Access) Website: https://wjftcse.org Contact: +91 75359 16555
Machine Learning-Based Resource Allocation for Scalable Cloud REST Services
Cloud computing environments today have an extensive range of distributed applications, which are dependent on the use of RESTful web services. These services are required to accommodate millions of simultaneous calls, as well as be highly performance, availability, and scalable. Conventional resource distribution systems of a cloud system are usually based on fixed policies or threshold-based auto-scaling strategies. Although these approaches offer a minimum scaleability, they are usually not efficient at managing unpredictable workloads and dynamic workloads typical in modern cloud systems. Consequently, the resources can be either underutilized or over-provisioned resulting in higher operational costs and poor performance of the system. Machine learning (ML) can be an effective solution to the problem of cloud resource allocation optimization in scalable REST services. ML models can predict resource utilization in the future by examining past workload data and determine intricate trends in traffic behavior to doom and refer moving computing resources like CPU, memory, and network bandwidth. This predictability enables cloud systems to make proactive resource allocation prior to the deterioration of performance. This paper presents an intelligent resource allocation framework on scalable cloud resource REST architecture based on machine learning algorithms. The framework combines predictive modeling of workload, resource scheduling by reinforcements of learning and real-time performance monitor. The system predicts the patterns of demand of the API using regression models and neural networks, which are supervised learning techniques. An agent of reinforcement learning then identifies ideal policies of resource allocation to achieve balance between system performance, latency and cost-efficiency. Experimental assessment shows that response time, system throughput and infrastructure usage is improved significantly as compared to the conventional rule based scaling mechanisms. The suggested system is more efficient in resources utilization and minimizes system failure and overhead. These findings reveal the possibility of machine learning based resource management in the next generation cloud based infrastructure as well as the high performance REST service ecologies.
Machine Learning, Cloud Computing, Resource Allocation, REST Services, Predictive Scaling, Cloud Infrastructure Optimization, Reinforcement Learning, API Performance Optimization
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
AI has already been used to revolutionize the current business landscape by allowing the application of data-driven decision-making, automated customer engagement, as well as personalized marketing. The existing literature has centered on AI applications in e-commerce settings, where they have been proposed to be used in recommender systems, intelligent chatbots, predictive analytics, and personalized marketing solutions to improve consumer experience and make the business more efficient. Although these studies prove that AI has the potential to streamline customer interactions and digital selling performance, various limitations are still present in the existing literature. Current literature tends to focus on platform-based retail applications and immediate marketing performance, little of which is provided on in-depth commercial ecosystems, like supply chain coordination, procurement judgement, and cross-organizational trade procedures. Moreover, the strategic implications associated with the adoption of AI over the long term, especially in the context of consumer trust, ethical management, transparency of algorithms, and sustainability, are not studied properly. This paper seeks to fill these research gaps by discussing the extended purpose of AI in business other than traditional e-commerce platforms. The study examines the effects of AI-based systems on commercial decisions, operational effectiveness, and management of customer relationships and the issues of transparency, data management, and ethical application. The synthesis of literature as well as analytical analysis of AI-enabled business procedures helps to see the emerging patterns and possibilities regarding the responsible use of AI. The results are projected to offer information on how AI can contribute to sustainable business development and consumer confidence and transparency in the digital marketplaces. Closing the gap between technological and strategic thinking, the research paper adds to a more comprehensive perception of the adoption of AI in business and suggests future research directions in smart commercial environments.
Artificial Intelligence, E-commerce, Machine Learning, Consumer Behavior, Automation, Data Analytics
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
Tactile Feedback Systems for Immersive Blockchain Gaming Ecosystems
Tactile feedback systems represent a pivotal advancement in immersive virtual environments, providing users with physical sensations that correspond to digital events. In blockchain gaming ecosystems—where true digital ownership, provable scarcity, and decentralized transactions define user experience—the integration of tactile feedback can profoundly deepen immersion, reinforce economic trust, and enable novel interactive mechanics. This manuscript presents an expanded examination of wearable haptic technologies and their convergence with blockchain-based gaming platforms. We begin with an in‑depth overview of contemporary haptic devices and blockchain gaming principles, proceed to a comprehensive literature synthesis, and then describe the development and evaluation of a prototype tactile glove integrated with a decentralized collectible‑card game.
Tactile Feedback, Haptics, Blockchain Gaming, Immersive Systems, User Engagement
AI-Driven Avatars in Emotionally Adaptive Virtual Classrooms
The present study investigates the development, integration, and evaluation of AI‑driven avatars within emotionally adaptive virtual classrooms. Over recent years, online education has grown exponentially, distinguishing itself through scalability and accessibility yet often lacking the nuanced emotional support integral to effective learning. To address this gap, we leverage advances in affective computing and pedagogical agent design to create avatars that detect and adapt to learners’ emotional states in real time. Specifically, our system uses multimodal sensors—facial expression analysis, vocal tone recognition, and interaction metrics—to infer discrete emotions such as confusion, frustration, boredom, and engagement. These inferences inform the avatar’s adaptive behaviors, including modulating speech prosody, delivering empathetic dialogue, adjusting pacing, and providing timely encouragement. We implemented this prototype in Unity3D, integrating the OpenFace facial‑action‑unit toolkit and an open‑source speech‑emotion classifier. A mixed‑methods experiment was conducted with 120 undergraduate students, randomly assigned to either an Adaptive Avatar condition or a Control (non‑adaptive) condition. The Adaptive group’s avatars responded dynamically to affective cues, whereas Control avatars presented identical content without adaptation. Quantitative measures included a 10‑item post‑test for knowledge retention, the User Engagement Scale for affective engagement, and interaction logs (time‑on‑task, hint requests). Qualitative data were gathered via semi‑structured interviews exploring perceptions of empathy, support, and social presence. Results indicate that learners in the Adaptive condition achieved significantly higher retention (M = 8.2 vs. 7.1, p < .001), reported greater engagement (M = 4.1 vs. 3.6 on a 5‑point scale, p < .001), requested fewer hints, and spent more time on task.
AI-Driven Avatars, Affective Computing, Virtual Classrooms, Emotional Adaptation, Educational Technology
Personalized VR Learning Environments Using Cognitive AI Agents
Virtual reality (VR) has shown promise as an immersive educational medium, yet its full potential remains unrealized when experiences are one‐size‐fits‐all. This study presents the design, implementation, and evaluation of personalized VR learning environments enhanced by cognitive AI agents, which continuously monitor learner behavior and dynamically tailor instructional support. Leveraging an ACT‑R cognitive architecture integrated with Unity3D, our system tracks learners’ interactions—such as task completion time, error patterns, and navigation choices—to infer conceptual misunderstandings in real time. We conducted a controlled experiment with 60 undergraduate participants, randomly assigned to either a personalized VR condition (with AI agent) or a non‑personalized control VR. Pre‑ and post‑test assessments measured learning gains, while standardized questionnaires captured engagement and usability. Statistical analysis (two‑sample t‑tests, Cohen’s d) revealed that the personalized group achieved significantly higher knowledge gains (M=18.5 vs. 12.1, p<.001, d=1.32) and engagement scores (UEQ mean=4.2 vs. 3.5, p<.01). Complementary computational simulations using synthetic learner models demonstrated a 30% reduction in trials to mastery under adaptive scaffolding. User interviews highlighted the agent’s scaffolding prompts as clear, timely, and motivating. We conclude that embedding cognitive AI agents within VR significantly enhances learning efficacy and learner experience. Implications for scalable deployment, multi‑user collaboration, and longitudinal retention are discussed, alongside challenges in real‑time performance optimization and agent–learner rapport in complex domains.
Personalized VR Learning, Cognitive AI Agents, Adaptive Learning, Virtual Reality, Educational Technology
Gesture-Based Control Interfaces for AI-Powered Smart Environments
Gesture-based control interfaces leverage natural, intuitive human movements to enable touchless interaction within AI-powered smart environments, addressing limitations of traditional input modalities such as touchscreens and voice control. This expanded study presents a comprehensive analysis of system design, algorithmic performance, user experience, and deployment considerations for gesture-recognition solutions integrated into smart-home and smart-building platforms. We develop a unified prototype comprising a depth-sensing camera, preprocessing pipeline, recognition engine (featuring Hidden Markov Models, Convolutional Neural Networks, and Long Short‑Term Memory networks), and a control interface driving real-world IoT devices. A within-subjects evaluation with thirty participants captured five atomic gestures across varied lighting and background conditions. Detailed statistical analysis—anchored by one-way ANOVA and post-hoc testing—confirms that LSTM-based models deliver superior accuracy (M = 94.2 %, SD = 2.9 %) and latency (M = 120 ms) compared to CNN (M = 91.5 %, SD = 3.8 %; M = 135 ms) and HMM (M = 88.7 %, SD = 4.2 %; M = 150 ms). Qualitative feedback highlights critical factors such as calibration ease, feedback mechanisms, and adaptability to individual movement styles. Drawing on these findings, we articulate design guidelines for robust, real-time gesture controls—emphasizing sensor placement, environmental robustness, computational efficiency, and customizable gesture vocabularies. The results demonstrate the feasibility of deploying advanced deep‑learning approaches in consumer-grade smart environments, enhancing accessibility, engagement, and system responsiveness. Future research directions include on-device inference for privacy preservation, multimodal fusion with voice and gaze inputs, and adaptive learning pipelines that evolve with user behavior over time.
Gesture Recognition, Smart Environments, AI, HMM, CNN, LSTM, Human–Machine Interaction
Neural Interfaces for Direct AI-Avatar Collaboration in the Metaverse
Neural interfaces, encompassing both noninvasive and invasive brain–computer interfaces (BCIs), have matured substantially over the past two decades, establishing robust frameworks for translating neural activity into actionable commands. Within the burgeoning metaverse—an interconnected network of immersive virtual environments—these interfaces provide a direct conduit for users to control AI-driven avatars, thereby minimizing the cognitive and physical effort associated with traditional controller-based interactions. This manuscript presents a comprehensive exploration of existing neural interface technologies and AI-avatar architectures, synthesizes relevant findings from prior studies, and reports on an empirical investigation comparing performance metrics between BCI-driven and manual control conditions. Specifically, a controlled experiment involving thirty participants engaged in a collaborative search-and-retrieve task demonstrates that BCI control yields significantly faster completion times (45.2 s vs. 68.7 s), higher command accuracy (87.4% vs. 79.5%), reduced subjective cognitive load (NASA-TLX scores of 48.6 vs. 62.3), and enhanced embodiment experiences (Embodiment Questionnaire scores of 5.8 vs. 4.2) compared to manual joystick control (all p < .001). These results endorse the viability of neural interfaces for seamless human–AI collaboration in virtual environments and underscore the potential for next-generation metaverse applications spanning education, telepresence, remote teamwork, and entertainment. We further delineate methodological considerations, statistical analyses, and implications for scalability, while acknowledging limitations related to signal fidelity, training burden, and generalizability.
Neural Interfaces, Brain–Computer Interface, Metaverse, AI Avatars, Human–AI Collaboration
AI-Powered Governance for Ethics and Compliance Monitoring Systems
Artificial Intelligence (AI) governance for ethics and compliance monitoring systems has become a strategic imperative for organizations deploying AI at scale. As enterprises integrate AI into mission‑critical operations—from credit underwriting and healthcare diagnostics to automated hiring and law enforcement—risks of biased outcomes, unlawful data usage, and opaque decision‑making have escalated. Traditional compliance approaches, which rely on periodic manual audits, are insufficient to address real‑time ethical breaches or evolving regulatory requirements. AI‑powered governance frameworks leverage machine learning (ML), natural language processing (NLP), and anomaly detection to continuously monitor AI pipelines, detect deviations from ethical policies, and trigger human review when necessary. This paper provides a detailed exploration of such governance architectures, grounding the discussion in interdisciplinary scholarship and industry best practices. We first outline the ethical and legal imperatives driving AI governance, then present a systematic literature review of existing frameworks. Our methodology combines qualitative case studies of five leading organizations with quantitative performance analysis of monitoring metrics over a twelve‑month period. Results indicate that AI‑driven monitoring improves violation detection rates by 45% and reduces mean time to resolution by 43% compared to manual audits, although modest increases in false‑positives highlight the need for careful threshold calibration. We conclude by synthesizing design principles—such as “ethics by design,” interpretability, and cross‑functional collaboration—and offer actionable recommendations for practitioners. Finally, we identify open research directions, including automated bias mitigation and scalable audit methodologies, to advance the field toward truly trustworthy AI systems.
AI Governance, Ethics Monitoring, Compliance Systems, Algorithmic Accountability, Regulatory Technology
Secure Execution of AI Pipelines on Confidential Cloud Infrastructure
With the explosive growth of artificial intelligence (AI) services in recent years, organizations are increasingly relying on cloud platforms to execute end-to-end AI pipelines—spanning data ingestion, preprocessing, model training, and inference. While cloud infrastructures offer unparalleled scalability and cost advantages, they also introduce significant risks: untrusted hypervisors, co-tenant attacks, and privileged insider threats can expose sensitive data and proprietary model parameters. Confidential computing, realized via hardware-enforced Trusted Execution Environments (TEEs) such as Intel SGX and AMD SEV, seeks to mitigate these risks by isolating code and data within protected enclaves. Despite the promise of TEEs, integrating them seamlessly into existing AI toolchains presents architectural, performance, and usability challenges. This manuscript presents SecureAI, a comprehensive framework for orchestrating AI workflows on confidential cloud infrastructure. We detail enclave provisioning, secure data ingestion, framework adaptation for TensorFlow and PyTorch, distributed parameter management, and end-to-end attestation. Through rigorous security analysis, we enumerate threat models and countermeasures. Empirical benchmarks on CIFAR-10 training with ResNet-50 quantify overheads: SGX enclaves incur ~25% runtime overhead, while AMD SEV adds ~17%. A Kubernetes-based simulation of mixed SGX/standard nodes highlights scheduling strategies that balance security and throughput. Our results demonstrate that SecureAI achieves strong confidentiality and integrity guarantees with acceptable performance trade-offs, paving the way for practical deployment of secure AI services in the public cloud.
Confidential Computing, Trusted Execution Environments, Intel SGX, AMD SEV, Secure AI Pipelines, Cloud Security
Adversarial Machine Learning Defense in IoT Ecosystems
The rapid expansion of Internet of Things (IoT) devices across consumer, industrial, and critical‑infrastructure domains has delivered unprecedented connectivity and automation. Yet this proliferation has also exposed a pressing security challenge: adversarial machine learning (AML) attacks that exploit subtle input perturbations to mislead or disable embedded intelligence. Such attacks—from single‑step perturbations like the Fast Gradient Sign Method (FGSM) to iterative optimization methods such as Projected Gradient Descent (PGD) and the Carlini & Wagner (C&W) attack—can have severe consequences in IoT contexts, ranging from false alarms in safety‑critical sensors to manipulated decisions in autonomous systems. Traditional AML defenses, while effective in large‑scale datacenter environments, often impose prohibitive computational or latency overheads for resource‑constrained IoT endpoints. In this work, we present a hybrid defense framework specifically tailored to the constrained and heterogeneous nature of IoT ecosystems. Our approach integrates three complementary techniques: (1) adversarial training, which augments the model’s decision boundary by including adversarial examples during offline retraining; (2) randomized smoothing, which adds certified robustness guarantees by averaging predictions over noise‑perturbed inputs at inference time; and (3) feature squeezing, a lightweight preprocessing step that reduces input complexity via bit‑depth reduction and median filtering. By strategically offloading the more intensive randomized smoothing to gateway or cloud nodes, while retaining feature squeezing for on‑device filtering, we achieve a balanced trade‑off between robustness and real‑time responsiveness.
Adversarial Machine Learning, IoT Security, Adversarial Training, Randomized Smoothing, Feature Squeezing, Edge Computing
Cyber-Resilient Protocols for Blockchain Nodes in Decentralized Webs
Blockchain technology has rapidly evolved into a foundational element of decentralized systems, enabling peer‑to‑peer transactions, smart contracts, and novel economic models without centralized intermediaries. Yet, as these networks proliferate, so do sophisticated cyber threats targeting the very nodes that maintain consensus and data integrity. This manuscript presents a comprehensive, multi‑layered framework of cyber‑resilient protocols specifically designed to safeguard blockchain nodes within decentralized webs against Eclipse, Sybil, and Distributed Denial‑of‑Service (DDoS) attacks. Central to our approach is an Adaptive Consensus Controller (ACC) that dynamically tunes consensus parameters based on real‑time network health metrics; a Network Intrusion Detection System (NIDS) that employs anomaly detection on peer‑to‑peer traffic; and a Peer‑Behavior Validator (PBV) leveraging lightweight zero‑knowledge proofs to cryptographically assess node compliance without compromising privacy. We implement this framework as extensions to a Hyperledger Fabric v2.2 prototype and evaluate its resilience across a 50‑node test network under controlled attack scenarios. Experimental results demonstrate a 45 % reduction in successful isolation (Eclipse) attacks, a 62.5 % decrease in consensus skew under Sybil conditions, sustained transaction throughput at 70 %–85 % of baseline during DDoS flooding, and overall node uptime improvements from 75 % to over 92 %.
Blockchain Resilience, Decentralized Web, Consensus Security, Intrusion Detection, Peer Validation, DDoS Mitigation
From Correlation to Causation: Integrating Causal Reasoning Frameworks into Deep Learning Models
Deep learning models have achieved remarkable success across diverse application domains, including computer vision, natural language processing, healthcare, and autonomous systems. Despite these advances, most deep learning systems fundamentally rely on correlational patterns rather than true causal understanding. This limitation poses significant challenges in high-stakes domains where robustness, interpretability, fairness, and generalization under distributional shifts are essential. The inability of conventional deep learning models to distinguish between correlation and causation often leads to spurious associations, biased predictions, and poor performance in unseen environments. This paper provides a comprehensive analysis of the transition from correlation to causation in artificial intelligence, with a particular focus on the integration of causal reasoning frameworks into deep learning architectures. We review foundational concepts of causal inference, including structural causal models, counterfactual reasoning, and intervention-based learning, and examine their relevance to modern deep learning systems. The paper further explores emerging hybrid approaches that combine neural networks with causal graphs, do-calculus, and invariant learning principles. Key challenges, methodological advances, and practical applications are discussed, along with open research questions and future directions. By bridging the gap between statistical learning and causal reasoning, this study aims to highlight pathways toward more reliable, explainable, and human-aligned artificial intelligence systems.
Causal inference, deep learning, correlation vs causation, structural causal models, counterfactuals, explainable AI, robust learning
Copyright (c) 2025 World Journal of Future Technologies in Computer Science and Engineering https://creativecommons.org/licenses/by-nc-nd/4.0
Privacy-Aware AI in Financial Fraud Detection Using Federated Data Lakes
Privacy preservation has emerged as a paramount concern in the deployment of artificial intelligence (AI) systems for financial fraud detection. As financial institutions increasingly rely on machine learning models trained on vast amounts of sensitive customer transaction data, the risk of data exposure—whether through centralized data breaches, insider misuse, or model inversion attacks—has grown commensurately. This manuscript presents a comprehensive, privacy-aware AI framework that integrates federated learning within a federated data lake architecture to detect fraudulent financial activities while ensuring that raw transaction data never leaves its originating institution. We begin by outlining the operational challenges faced by financial consortia in collaborative fraud detection, including regulatory compliance under GDPR, PCI DSS, and similar frameworks. We then detail our federated data lake deployment, leveraging Apache Iceberg for unified data cataloging and Presto SQL for seamless cross-node querying, combined with secure aggregation protocols that cryptographically shield client model updates.
Privacy-Aware AI, Federated Data Lakes, Financial Fraud Detection, Federated Learning, Secure Aggregation
Copyright (c) 2025 World Journal of Future Technologies in Computer Science and Engineering (WJFTCSE) https://creativecommons.org/licenses/by-nc-nd/4.0
Autonomous Cyber Incident Response Using Cognitive Security Agents
Autonomous Cyber Incident Response (ACIR) harnesses the capabilities of cognitive security agents—software entities endowed with perception, reasoning, and learning functions—to detect, analyze, and mitigate cyber threats without continuous human oversight. This manuscript expands on an ACIR framework that integrates real‑time telemetry ingestion, knowledge graph construction, hybrid cognitive architectures (ACT‑R/SOAR), and reinforcement learning (RL) to orchestrate end‑to‑end incident response workflows. The extended abstract details the motivation, architectural components, experimental setup, key performance metrics, results, and implications for cybersecurity operations. Through extensive simulations emulating enterprise networks with cloud and on‑premises assets, ACIR agents demonstrated a 60% reduction in mean time to detect (MTTD) and a 50% reduction in mean time to respond (MTTR) compared to traditional SIEM‑based human workflows. False positive rates remained stable at approximately 5%, illustrating that speed improvements did not compromise accuracy. Importantly, RL‑driven adaptation yielded a 30% improvement in first‑shot remediation success across repeated attack scenarios, evidencing the agents’ ability to learn from past outcomes and refine decision policies. The architecture’s modular design facilitates incremental integration with existing security infrastructures, enabling organizations to adopt ACIR capabilities alongside legacy tools.
Trust Metrics in AI Models for Secure Governmental IT Systems
Ensuring the trustworthiness of artificial intelligence (AI) models deployed within governmental IT infrastructures is an imperative mission-critical concern. As governments worldwide embrace AI-driven automation and decision support for applications ranging from citizen services to national security, they confront unique challenges: adversarial threats designed to deceive or corrupt models, the potential for biased outcomes that undermine fairness, opaque decision processes that erode stakeholder confidence, and the stringent regulatory and ethical obligations inherent in public-sector operations. This enhanced abstract delves into each dimension of trust—technical robustness, predictive accuracy, model explainability, fairness and non‑discrimination, data privacy and security, and governance oversight—highlighting how they interplay to form a holistic trust posture. We outline a composite Trust Score methodology that normalizes and weights individual sub‑metrics drawn from adversarial robustness testing, accuracy benchmarks, explainability indices (e.g., SHAP attributions), fairness audits (e.g., disparate impact ratios), privacy impact assessments, and compliance checklists mapped to governmental regulations. We discuss the methodological framework used to simulate real-world governmental IT deployments—including the generation of synthetic network telemetry, adversarial attack scenarios, data drift episodes, and policy-violation assessments—and present key findings: the Trust Score’s responsiveness to security breaches, its ability to flag fairness anomalies, and its sensitivity to governance lapses.
Trust Metrics, AI Models, Government IT Security, Explainability, Robustness
Digital Twin-Based Cyber Risk Forecasting in Smart Cities
Cyberwarfare has evolved into a sophisticated domain where attackers exploit advanced persistent threats, zero-day exploits, and social engineering to compromise critical systems. Traditional reactive defenses—firewalls, signature-based intrusion detection systems, and static honeypots—are increasingly inadequate against adaptive adversaries who reconnoiter, probe, and pivot within target networks. AI-based deception techniques offer a proactive layer of defense by dynamically generating decoys, obfuscating real assets, and engaging adversaries in controlled environments, thereby gathering actionable threat intelligence and disrupting attack chains. This manuscript presents a comprehensive framework for the design, implementation, and evaluation of an AI-driven deception platform tailored for cyberwarfare defense. We begin by delineating the theoretical underpinnings of deception in cyber defense and surveying existing approaches to honeypots, honeytokens, and dynamic decoys. Building on this foundation, we describe our methodological approach: a modular system architecture comprising a Decoy Generator that fabricates realistic service instances; a Behavior Analyzer that employs machine learning models to classify traffic and predict adversary intent; and an Orchestration Engine that adapts deception strategies in real time. We deploy the platform within a simulated enterprise network and conduct three representative attack scenarios—reconnaissance, credential brute-forcing, and lateral movement—under both static and AI-based configurations. Our results demonstrate a significant increase in detection rates (from 72.5% to 94.2%), extended adversary engagement times (by 133%), and richer intelligence collection, all achieved with acceptable computational overhead. We conclude with a critical analysis of operational considerations, including model maintenance, integration with existing security infrastructures, and adversary-aware countermeasures, and outline future research directions such as federated learning for collaborative deception and advanced generative models for decoy authenticity.
AI-Based Deception, Cyberwarfare Defense, Honeypots, Adversary Engagement, Adaptive Decoys
AI-Based Deception Techniques in Cyberwarfare Defense Systems
Zero Trust Architectures for Edge-Native AI Inference Systems
Edge‑Native AI Inference Systems (ENAIS) are rapidly proliferating across domains such as autonomous vehicles, smart manufacturing, healthcare diagnostics, and critical infrastructure monitoring. By bringing AI inference closer to data sources, ENAIS dramatically reduce latency, conserve bandwidth, and enable real‑time decision‑making. However, the shift from centralized cloud environments to widely distributed, resource‑constrained edge nodes introduces unique security challenges: traditional perimeter defenses become ineffective, attack surfaces multiply, and heterogeneity complicates policy enforcement. To address these concerns, this manuscript presents a comprehensive Zero Trust Architecture (ZTA) tailored for ENAIS. Building on the principle of “never trust, always verify,” our design integrates three core components: identity‑centric access controls, micro‑segmentation of inference pipelines, and continuous telemetry‑driven policy adaptation. We detail the architectural blueprint, describe its implementation within a simulation framework, and conduct a rigorous evaluation under realistic threat scenarios. Statistical analysis of the simulation data—covering metrics such as unauthorized access prevention, inference latency, and resource overhead—reveals that the proposed ZTA blocks over 98% of unauthorized actions while incurring only a modest 16.6% latency penalty and minimal CPU and network overhead. These results demonstrate that ZTA can substantially elevate the security posture of ENAIS without compromising real‑time performance requirements. We conclude with a discussion of deployment considerations, potential integration with federated learning, and directions for future work in securing next‑generation edge AI.
Zero Trust, Edge Computing, AI Inference, Micro Segmentation, Continuous Authentication
AI-Powered Threat Modeling for API Gateways in Microservices
In the rapidly evolving landscape of microservice architectures, API gateways occupy a pivotal role by acting as the primary ingress point for client requests, performing critical functions such as routing, protocol translation, policy enforcement, authentication, rate limiting, and observability. These gateways, however, also represent a concentrated attack surface and have become prime targets for adversaries seeking to exploit misconfigurations, logical flaws, and emergent vulnerabilities. Traditional threat modeling approaches—centered on manual decomposition of system components, application of taxonomies like STRIDE, and expert-driven mitigation planning—are time‑consuming, labor‑intensive, and struggle to keep pace with the dynamism of large‑scale microservice deployments. To address these challenges, we present an AI‑powered threat modeling framework specifically tailored for API gateways within microservices environments. Our approach integrates a three‑stage pipeline: automated architecture ingestion to construct detailed system graphs; generative AI to propose candidate threats and attack scenarios based on engineered STRIDE prompts; and predictive anomaly detection leveraging machine‑learning classifiers trained on historical gateway logs. We evaluated our framework across fifty real‑world deployments spanning finance, healthcare, and e‑commerce sectors. The results demonstrate a precision of 0.87 and recall of 0.82 against expert‑verified threat models, alongside a 45% reduction in analysis time (from 22 hours down to 12 minutes per model). Notably, the system identified eighteen previously unrecognized tampering and information‑disclosure scenarios, subsequently validated by security engineers.
AI Powered Threat Modeling, API Gateway Security, Microservices, Automated Vulnerability Analysis, Generative AI
Blockchain-Enabled Anomaly Detection for Secure CI/CD Pipelines
Blockchain technology has emerged as a transformative force in securing distributed systems, offering tamper‑evident, decentralized ledgers that ensure data integrity and transparency. As software development organizations increasingly adopt Continuous Integration and Continuous Deployment (CI/CD) pipelines to accelerate release cycles, they concurrently expose themselves to sophisticated threats such as supply‑chain attacks, insider tampering, and configuration drifts that traditional security measures struggle to detect in real time. This manuscript presents ChainSec‑CI, a novel framework that marries the immutability guarantees of a permissioned blockchain—specifically Hyperledger Fabric—with AI‑driven anomaly detection to secure CI/CD pipelines comprehensively. Within ChainSec‑CI, every critical pipeline event—ranging from source code commits and build artifacts to test executions and deployment actions—is recorded on‑chain via lightweight smart contracts, creating a verifiable, append‑only audit trail. To address the high‑volume, heterogeneous nature of pipeline metadata, we extract key features (e.g., stage durations, failure frequencies, configuration hash changes, and sequence anomalies) and feed these into an unsupervised isolation forest model, enabling the system to learn “normal” pipeline behavior without requiring labeled attack data. We developed a Jenkins–Hyperledger Fabric integration prototype and evaluated it using 10,000 synthetic pipeline runs containing both benign operations and injected malicious scenarios such as unauthorized config modifications, abnormally prolonged build or test steps, and out‑of‑order stage executions.
CI/CD Pipelines, Blockchain, Anomaly Detection, Hyperledger Fabric, Unsupervised Learning
Privacy-Aware Authentication in Cyber-Physical Industrial Systems
Industrial control environments—spanning manufacturing floors, power grids, and critical infrastructure—now operate as complex cyber‑physical industrial systems (CPIS) that integrate programmable logic controllers (PLCs), sensors, actuators, and supervisory networks. As CPIS increasingly interconnect with enterprise IT and cloud services, they face heightened risks of unauthorized access and privacy breaches. Traditional authentication schemes, often repurposed from IT networks, either impose excessive computational load on resource‑constrained devices or fail to conceal sensitive metadata that can reveal operational characteristics. To address these challenges, we propose a novel privacy‑aware authentication protocol optimized for CPIS. Leveraging elliptic‑curve cryptography (ECC) for lightweight public‑key operations and Schnorr‑style zero‑knowledge proofs (ZKPs) to obfuscate device identities, our scheme achieves mutual authentication in just two communication rounds. We implement the protocol on common industrial controllers (Siemens S7‑1200, Allen‑Bradley CompactLogix, WAGO PFC200) using the TinyCrypt ECC library and Java‑Bouncy Castle on the server side. Over 200 trials, our solution attains an average end‑to‑end latency of 150 ms (±20 ms), a privacy leakage score of 0.15 on a normalized entropy scale (0–1), a false acceptance rate of 0.5%, and a false rejection rate of 1.2%. Compared to representative ECC‑only and ECC+ZKP schemes, we reduce authentication latency by up to 25% and diminish metadata leakage by 40%, while preserving reliability under induced network jitter. We conclude by discussing deployment guidelines—such as hardware‑accelerated cryptographic modules—and outline future research directions toward mesh‑network scalability and post‑quantum resilience.
Privacy-Aware Authentication, Cyber-Physical Industrial Systems, Elliptic-Curve Cryptography, Zero-Knowledge Proofs, Industrial IoT Security
Context-Aware Threat Detection in Multi-Cloud AI Platforms
In contemporary enterprise environments, the strategic adoption of multi-cloud AI platforms has unlocked unprecedented capabilities for scalable, resilient, and cost-effective deployment of artificial intelligence (AI) workloads. However, this architectural evolution has simultaneously introduced a complex and expanding attack surface that defies traditional security paradigms. In particular, threat actors exploit the dynamic interplay between heterogeneous cloud services, AI model telemetry, and contextual environmental variables to orchestrate stealthy, high-impact attacks that often elude conventional detection mechanisms. This manuscript presents a comprehensive context-aware threat detection framework expressly designed for multi-cloud AI ecosystems. By ingesting and fusing telemetry from cloud infrastructure logs (e.g., AWS CloudWatch, Azure Monitor, Google Cloud Operations), AI model inference and training logs, and auxiliary context such as workload schedules and user behavior patterns, the proposed system constructs a unified, high-dimensional representation of the operational environment. A hybrid detection engine—comprising supervised gradient boosting machines (GBMs) and unsupervised deep autoencoders—leverages these fused features to identify anomalies and known malicious patterns in real time. Through extensive experimentation within a simulated multi-cloud AI deployment featuring open-source workloads (TensorFlow Serving, Kubeflow pipelines) and adversarial scenarios (stealthy lateral movement, privilege escalation, data exfiltration), the framework achieves a detection accuracy of 92.0%, a false positive rate reduction of 45% relative to context-agnostic baselines, and maintains average alert latency under 1.5 seconds.
Context-Aware Threat Detection, Multi-Cloud Security, AI Platform Monitoring, Behavioral Analytics, Adaptive Defenses
Quantum-Resistant AI Models for Intrusion Detection
As quantum computing transitions from theoretical constructs to practical implementations, classical cryptographic foundations—long the bedrock of cybersecurity—face unprecedented vulnerability. Shor’s algorithm and related quantum techniques threaten to render public-key schemes like RSA and ECC obsolete, exposing sensitive communications and stored data to adversarial compromise. In this context, Intrusion Detection Systems (IDS) that harness artificial intelligence (AI) must evolve to incorporate quantum-resistant mechanisms to secure both their operational logic and the data they process. This manuscript presents an end-to-end framework for a quantum-resistant AI-based IDS, integrating lattice-based public-key encryption (FrodoKEM) for feature confidentiality and Cheon-Kim-Kim-Song (CKKS) homomorphic encryption for privacy-preserving inference. We develop and optimize a deep neural network with encrypted weights and encrypted input vectors, enabling packet inspection and anomaly detection entirely in the encrypted domain. Experiments on UNSW-NB15 and CIC-IDS2017 datasets demonstrate that our quantum-resistant IDS achieves an average detection accuracy of 97.2%, a true positive rate of 96.0%, and a false positive rate of 3.5%, closely matching plaintext performance while incurring a total latency overhead of approximately 25% (14.8 ms encrypted inference vs. 12.1 ms plaintext, plus 2.9 ms encryption/decryption). We analyze computational and memory trade-offs, explore key-management strategies compliant with NIST PQC guidelines, and discuss scalability considerations for real-world deployment. Our findings confirm that integrating post-quantum cryptography into AI-driven IDS can future-proof network security infrastructures against both classical and quantum adversaries, with acceptable performance overhead for enterprise environments.
Quantum-Resistant, Post-Quantum Cryptography, Machine Learning, Intrusion Detection, Homomorphic Encryption
Multi-Agent Drone Swarms for Agricultural Intelligence Networks
In modern precision agriculture, the use of Unmanned Aerial Vehicles (UAVs) has revolutionized the capabilities for real‑time monitoring of crop health, soil moisture, pest infestations, and resource allocation. However, single‑drone systems face inherent limitations in flight endurance, payload capacity, and operational redundancy, which constrain their effectiveness over expansive agricultural tracts. This study introduces and validates a multi‑agent drone swarm approach—comprising ten coordinated quadcopters—designed to function as an Agricultural Intelligence Network (AIN). Each drone is equipped with high‑resolution RGB and multispectral cameras, onboard processing units for in‑flight data analysis, and mesh‑network communications enabling peer‑to‑peer coordination without reliance on a central controller. We implemented a consensus‑based waypoint allocation algorithm alongside a U‑Net convolutional neural network for automated detection of pests and weeds. Field trials were conducted on a 50‑hectare maize plot characterized by heterogeneous health zones. Performance metrics included coverage rate (ha/min), detection accuracy (%), mission duration (min), and communication latency (ms). Statistical analysis using independent‑samples t‑tests (α = .05) across ten missions per configuration revealed that the swarm system achieved a 36% higher coverage rate and a 22.2% absolute improvement in detection accuracy compared to single‑drone missions, while reducing mission duration by 35%. Communication latency increased modestly but remained operationally acceptable. These results confirm that decentralized multi‑agent swarms significantly enhance both the efficiency and quality of agricultural surveillance. The paper discusses implications for large‑scale precision farming, addresses environmental and regulatory constraints, and outlines future research directions to further optimize swarm scalability and energy management.
Multi Agent Drone Swarm, Precision Agriculture, Decentralized Coordination, Target Detection, Consensus Algorithms
Smart Grippers for Predictive Object Recognition in Warehousing Robots
Smart grippers that integrate predictive object recognition capabilities have emerged as pivotal components in the next generation of autonomous warehousing robots. By anticipating both the identity and grasp affordances of items prior to contact, these systems can significantly reduce mispicks, optimize cycle times, and enhance overall throughput. This paper presents an end-to-end framework combining a soft, sensorized gripper design with a parallel deep learning architecture that fuses visual and tactile modalities. The hardware includes an Ecoflex-based soft gripper embedded with triboelectric nanogenerator (TENG) sensors—both pressure (P‑TENG) and bend (B‑TENG)—that provide rich tactile feedback. On the algorithmic side, we deploy a dual-network pipeline: a YOLOv5 model for rapid object detection and classification, and a GG‑CNN for pixel-wise grasp quality estimation, both trained and fine-tuned on a custom warehouse dataset. A lightweight convolutional network processes the high‑frequency tactile signals to infer object geometric categories. These streams converge in a Bayesian fusion module that dynamically selects optimal grasp parameters (finger spread, approach angle, and force threshold) before actuation. In a 1,000‑trial evaluation across ten item classes in a mock warehouse environment, our system achieved a predictive recognition accuracy of 94.3% and a grasp success rate of 92.1%, reducing mispicks by 37% and improving average pick cycle time by 18% compared to vision‑only baselines. Under challenging conditions—low lighting (< 100 lux) and partial occlusions—the fused approach maintained an 89.7% success rate, whereas vision‑only performance dropped to 78.4%.
Smart Gripper, Predictive Object Recognition, Warehousing Robots, Deep Learning, Sensor Fusion
Energy Harvesting in Micro-Robots for Self-Sustainability
Energy harvesting has emerged as a transformative approach for powering micro‑robotic systems autonomously, obviating the need for bulky batteries and frequent recharging. By converting ambient mechanical, solar, or electromagnetic energy into electrical power, micro‑robots can maintain continuous operation in diverse environments. This study provides an in‑depth comparison of three energy‑harvesting modalities—piezoelectric, photovoltaic, and radio‑frequency (RF)—implemented on identically sized micro‑robot prototypes. We designed and fabricated three sets of 20 × 20 × 10 mm³ micro‑robots, each equipped with either a PZT cantilever, a monocrystalline silicon solar cell, or a dual‑band RF rectenna. Under controlled laboratory conditions—120 Hz, 1 g vibration for piezoelectric trials; 500 lux illumination for photovoltaic trials; and a calibrated 2 W ERP RF field inside a shielded chamber—each harvester’s output was recorded over ten 60‑second runs. Using a precision data‑logging system sampling at 1 kHz, we integrated harvested charge to compute energy yields per trial. A one‑way ANOVA (α=0.05) tested differences among modalities, revealing significant performance disparities: piezoelectric (mean = 120 µJ, SD = 15), photovoltaic (mean = 85 µJ, SD = 12), and RF (mean = 60 µJ, SD = 10). Post‑hoc Tukey tests confirmed that all pairwise differences were statistically significant (p<0.01). These results underscore the critical role of environmental compatibility in harvester selection and motivate hybrid configurations to balance strengths and weaknesses. We discuss implications for micro‑robot design, propose guidelines for selecting and integrating harvesters based on mission profiles, and outline future research paths, including long‑duration field deployments and multimodal integration to achieve true self‑sustainability.
Energy Harvesting, Micro Robots, Self Sustainability, Piezoelectric, Photovoltaic, RF
Underwater Robotic Systems with Real-Time Acoustic AI Mapping
Underwater robotic exploration has been revolutionized by advances in acoustic sensing and artificial intelligence (AI), enabling autonomous underwater vehicles (AUVs) to navigate and map complex seabed environments without reliance on optical systems. Traditional optical approaches fail under turbid conditions and at depth, where light attenuation is severe, whereas acoustic sensors—such as multi‑beam and side‑scan sonar—provide robust, range‐based measurements regardless of visibility. However, raw sonar data are plagued by speckle noise, multipath reflections, and environmental interference, which complicate feature extraction and mapping. To address these challenges, we present Underwater Robotic Systems with Real‑Time Acoustic AI Mapping, a comprehensive framework that integrates a synchronized multi‑modal acoustic sensor suite with onboard deep learning models for semantic feature extraction and simultaneous localization and mapping (SLAM). Our system hardware combines a multi‑beam sonar for broad bathymetric coverage, side‑scan sonar for high-resolution imagery, an inertial measurement unit (IMU) for attitude estimation, and a depth sensor for accurate pressure-based altitude readings. Sensor streams are synchronized using Precision Time Protocol (PTP) to ensure consistent timestamp alignment. At the core of our software pipeline lies a convolutional neural network (CNN) based on the U‑Net architecture, trained to segment seabed structures—such as ridges, pipelines, and wreckage—in acoustic intensity images. The network was trained on a curated dataset of over 5,000 labeled sonar returns, augmented with simulated noise profiles replicating real‑world interference patterns.
Underwater Robotics, Acoustic Mapping, Deep Learning, SLAM, Autonomous Underwater Vehicle
AI-Enhanced Remote Diagnosis in Robotic Maintenance Systems
The ever‑increasing sophistication of robotic maintenance systems in manufacturing and critical‑infrastructure domains has outpaced the capabilities of traditional diagnostic regimes. Conventional approaches—predicated on scheduled inspections, manual symptom recognition, and simple threshold alarms—prove insufficient when confronted with heterogeneous fleets of high‑degree‑of‑freedom manipulators, mobile platforms, and collaborative robots operating under continuous, high‑load conditions. Such systems demand a diagnostic paradigm that not only anticipates failures before catastrophic breakdowns but also adapts dynamically to evolving operating contexts. This manuscript presents an AI‑enhanced remote diagnosis framework tailored for modern robotic maintenance, integrating multi‑modal sensor fusion, edge‑based preprocessing, and cloud‑native machine learning analytics. We detail the system architecture—comprising onboard accelerometers, thermistors, and motor‑current monitors; an MQTT‑based communication layer; and a scalable Spark‑Streaming/XGBoost analytics pipeline—and describe the data‑generation and labeling processes used for model training. A comprehensive simulation in ROS‑Gazebo involving six UR10 arms executing representative pick‑and‑place tasks evaluates the framework’s performance. Statistical analysis on an unseen test set reveals a 90.4% diagnostic accuracy—15.2% higher than a baseline threshold method—alongside a 33.6% reduction in mean time to detection. Through a 72‑hour virtual operation, end‑to‑end latencies averaged 120 ms, demonstrating real‑time feasibility. False‑alarm rates fell from 8.7% to 2.3%, and false negatives from 5.5% to 3.1%, confirming robust sensitivity and specificity. The proposed system therefore offers a scalable, adaptive solution for minimizing unscheduled downtime and maintenance costs in Industry 4.0 deployments. Future work will incorporate motion‑context awareness and real‑world field trials to further refine diagnostic precision and operational resilience.
AI-Enhanced Diagnosis, Robotic Maintenance, Predictive Maintenance, Sensor Fusion, Remote Monitoring
Blockchain-Based Robot Identity and Coordination in Multi-Agent Environments
Blockchain technology, with its decentralized ledger and smart‑contract capabilities, offers a robust framework for addressing critical challenges in multi‑agent robotic systems—namely, trustless identity management, secure peer authentication, and fault‑tolerant task coordination. In traditional architectures, centralized controllers enforce policies and mediate interactions, creating single points of failure, scalability bottlenecks, and opportunities for malicious interference. By contrast, a permissioned blockchain network distributes authority across participating robots, enabling each agent to verify the provenance of messages, enforce consensus‑driven agreements, and record immutable logs of interactions. This study presents a comprehensive examination of blockchain‑based robot identity and coordination in multi‑agent environments. We systematically review existing decentralized identity (DID) schemes and smart‑contract protocols, analyze their performance through statistical comparisons with centralized baselines, and implement a Hyperledger Fabric prototype across ten heterogeneous mobile robots. Our experiments demonstrate that on‑chain identity registration and automated smart‑contract–driven task allocation reduce coordination latency by 35%, improve task completion rates by 9.8%, and shorten fault recovery times by 20%, albeit with a 17% increase in energy consumption due to cryptographic operations.
Blockchain, Robot Identity, Multi Agent Coordination, Decentralized Control, Smart Contracts
AI-Based Terrain Adaptation Algorithms for Walking Robots
Legged robots capable of traversing unstructured and unpredictable environments are increasingly vital for applications such as disaster response, planetary exploration, precision agriculture, and inspection of hazardous zones. Traditional model-based control strategies require precise system identification and assume known, quasi-static terrain profiles; as a result, their performance degrades sharply when confronted with unforeseen surface irregularities, variable compliance, or dynamic obstacles (Zhao & Li, 2020). In contrast, AI-based terrain adaptation algorithms leverage data-driven learning to endow walking robots with the ability to perceive, interpret, and respond to diverse terrain features in real time. This manuscript presents a unified framework integrating deep reinforcement learning (DRL), sensor fusion, and hybrid model-based/data-driven control for bipeds, quadrupeds, and hexapods. We detail the system architecture—comprising a perception module (LIDAR, vision, IMU, and force sensing), a DRL policy trained via Proximal Policy Optimization (PPO), and an impedance-based safety layer—that enables robust foothold selection, gait modulation, and reflexive fallback behaviors. Experimental validation in high-fidelity MuJoCo simulations across flat ground, variable slopes, deformable sand patches, and discrete obstacle fields demonstrates that our hybrid controller reduces slip events by 28% and energy consumption by 12%, while increasing average locomotion speed by 15% compared to standard model predictive control (MPC) baselines. We conclude by discussing practical considerations for real-world deployment, including domain randomization, computational latency, and sensor calibration, and outline future directions for extending terrain taxonomy and minimizing sim-to-real gaps (Kumar et al., 2021; Patel & Singh, 2021).
Terrain Adaptation, Walking Robots, Deep Reinforcement Learning, Sensor Fusion, Hierarchical Control