arXiv subjects

Abdelaziz Amara Korba

Publications and source records attributed to Abdelaziz Amara Korba.

13 recordsLinked to original sources

arXiv2026

DARTIC: Decentralized Anonymous Reputation at Scale for Trustworthy Crowdsourcing

On-chain crowdsourcing leverages blockchain's decentralization, transparency, and tamper-resistance to build trustworthy and verifiable Web3 crowdsourced services. However, existing decentralized reputation frameworks do not reconcile anonymity, reputation binding, and scalability. This paper demonstrates how on-chain crowdsourcing can simultaneously achieve these requirements under a trust-minimized model. We introduce DARTIC, a decentralized, anonymous, and scalable reputation-driven framework for crowdsourcing. DARTIC presents a dual-ledger system that enables requesters and workers to use distinct pseudonyms across interactions, ensuring unlinkability while maintaining accountability. To mitigate Sybil and reputation-reset attacks, we employ zkSNARK-based set membership proofs, cryptographically binding all user pseudonyms to a single access token without revealing the linkage. For scalability, we investigate two aggregation techniques that compress multiple proofs into a single succinct proof to minimize verification overhead. In addition, we design an automated, privacy-preserving reputation model that dynamically evaluates contributions across diverse crowdsourcing contexts. To demonstrate practicality, we instantiate and assess DARTIC in both crowdsensing and federated learning scenarios. Experimental results show that (i) individual proof generation for token spending completes in less than 3s, (ii) aggregation reduces the verification time of 1024 proofs from 8.7s to 0.96s, and (iii) zk-batching lowers gas costs by more than 100x compared to a pure Layer-1 deployment. These results demonstrate that anonymity, robust reputation binding, and scalability can be jointly achieved in fully decentralized crowdsourcing systems.

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arXiv2026

Automated Byzantine-Resilient Clustered Decentralized Federated Learning for Battery Intelligence in Connected EVs

Federated learning (FL) has emerged as a promising paradigm for managing electric vehicle (EV) battery data in intelligent transportation systems (ITS), enabling privacy-preserving tasks such as anomaly detection and capacity estimation. However, most existing frameworks rely on centralized aggregation schemes, which pose critical limitations in terms of security and trust. To address these challenges, we propose ABC-DFL, an automated Byzantine-resilient clustered decentralized federated learning (C-DFL) framework for connected EVs. The proposed incentive-driven C-DFL system replaces the central server with an open-permissioned blockchain, featuring a new dynamic Quorum Byzantine Fault Tolerance (QBFT) protocol and an oracle-based aggregation layer, to enhance trust, security, and automation. At the core of ABC-DFL lies FLECA (Filtered Layered Enhanced Clustering Aggregation), a robust hierarchical aggregation protocol that mitigates Byzantine attacks by having each EV filter malicious updates using an adaptive threshold based on deviations from its reference model update. Oracle nodes, responsible for inter-group aggregation, employ robust clustering to isolate and aggregate model updates from trustworthy EV groups. Comprehensive experimental evaluations demonstrate that FLECA matches FedProx convergence under benign conditions and significantly outperforms existing defenses with attack impact scores below 0.10 in adaptive adversarial scenarios. Furthermore, several learning experiments with multitask models confirm the effectiveness and fairness of the incentive mechanism. Finally, on-chain and off-chain benchmarks validate the practicality of ABC-DFL.

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arXiv2026

User-Centric Phishing Detection: A RAG and LLM-Based Approach

The escalating sophistication of phishing emails necessitates a shift beyond traditional rule-based and conventional machine-learning-based detectors. Although large language models (LLMs) offer strong natural language understanding, using them as standalone classifiers often yields elevated falsepositive (FP) rates, which mislabel legitimate emails as phishing and create significant operational burden. This paper presents a personalized phishing detection framework that integrates LLMs with retrieval-augmented generation (RAG). For each message, the system constructs user-specific context by retrieving a compact set of the user's historical legitimate emails and enriching it with real-time domain and URL reputation from a cyber-threat intelligence platform, then conditions the LLM's decision on this evidence. We evaluate four open-source LLMs (Llama4-Scout, DeepSeek-R1, Mistral-Saba, and Gemma2) on an email dataset collected from public and institutional sources. Results show high performance; for example, Llama4-Scout attains an F1-score of 0.9703 and achieves a 66.7% reduction in FPs with RAG. These findings validate that a RAG-based, user-profiling approach is both feasible and effective for building high-precision, low-friction email security systems that adapt to individual communication patterns.

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arXiv2025

Towards Trustworthy Agentic IoEV: AI Agents for Explainable Cyberthreat Mitigation and State Analytics

The Internet of Electric Vehicles (IoEV) envisions a tightly coupled ecosystem of electric vehicles (EVs), charging infrastructure, and grid services, yet it remains vulnerable to cyberattacks, unreliable battery-state predictions, and opaque decision processes that erode trust and performance. To address these challenges, we introduce a novel Agentic Artificial Intelligence (AAI) framework tailored for IoEV, where specialized agents collaborate to deliver autonomous threat mitigation, robust analytics, and interpretable decision support. Specifically, we design an AAI architecture comprising dedicated agents for cyber-threat detection and response at charging stations, real-time State of Charge (SoC) estimation, and State of Health (SoH) anomaly detection, all coordinated through a shared, explainable reasoning layer; develop interpretable threat-mitigation mechanisms that proactively identify and neutralize attacks on both physical charging points and learning components; propose resilient SoC and SoH models that leverage continuous and adversarial-aware learning to produce accurate, uncertainty-aware forecasts with human-readable explanations; and implement a three-agent pipeline, where each agent uses LLM-driven reasoning and dynamic tool invocation to interpret intent, contextualize tasks, and execute formal optimizations for user-centric assistance. Finally, we validate our framework through comprehensive experiments across diverse IoEV scenarios, demonstrating significant improvements in security and prediction accuracy. All datasets, models, and code will be released publicly.

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arXiv2025

Advancing Autonomous Incident Response: Leveraging LLMs and Cyber Threat Intelligence

Effective incident response (IR) is critical for mitigating cyber threats, yet security teams are overwhelmed by alert fatigue, high false-positive rates, and the vast volume of unstructured Cyber Threat Intelligence (CTI) documents. While CTI holds immense potential for enriching security operations, its extensive and fragmented nature makes manual analysis time-consuming and resource-intensive. To bridge this gap, we introduce a novel Retrieval-Augmented Generation (RAG)-based framework that leverages Large Language Models (LLMs) to automate and enhance IR by integrating dynamically retrieved CTI. Our approach introduces a hybrid retrieval mechanism that combines NLP-based similarity searches within a CTI vector database with standardized queries to external CTI platforms, facilitating context-aware enrichment of security alerts. The augmented intelligence is then leveraged by an LLM-powered response generation module, which formulates precise, actionable, and contextually relevant incident mitigation strategies. We propose a dual evaluation paradigm, wherein automated assessment using an auxiliary LLM is systematically cross-validated by cybersecurity experts. Empirical validation on real-world and simulated alerts demonstrates that our approach enhances the accuracy, contextualization, and efficiency of IR, alleviating analyst workload and reducing response latency. This work underscores the potential of LLM-driven CTI fusion in advancing autonomous security operations and establishing a foundation for intelligent, adaptive cybersecurity frameworks.

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arXiv2025

Fuse and Federate: Enhancing EV Charging Station Security with Multimodal Fusion and Federated Learning

The rapid global adoption of electric vehicles (EVs) has established electric vehicle supply equipment (EVSE) as a critical component of smart grid infrastructure. While essential for ensuring reliable energy delivery and accessibility, EVSE systems face significant cybersecurity challenges, including network reconnaissance, backdoor intrusions, and distributed denial-of-service (DDoS) attacks. These emerging threats, driven by the interconnected and autonomous nature of EVSE, require innovative and adaptive security mechanisms that go beyond traditional intrusion detection systems (IDS). Existing approaches, whether network-based or host-based, often fail to detect sophisticated and targeted attacks specifically crafted to exploit new vulnerabilities in EVSE infrastructure. This paper proposes a novel intrusion detection framework that leverages multimodal data sources, including network traffic and kernel events, to identify complex attack patterns. The framework employs a distributed learning approach, enabling collaborative intelligence across EVSE stations while preserving data privacy through federated learning. Experimental results demonstrate that the proposed framework outperforms existing solutions, achieving a detection rate above 98% and a precision rate exceeding 97% in decentralized environments. This solution addresses the evolving challenges of EVSE security, offering a scalable and privacypreserving response to advanced cyber threats

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arXiv2025

Zero-Day Botnet Attack Detection in IoV: A Modular Approach Using Isolation Forests and Particle Swarm Optimization

The Internet of Vehicles (IoV) is transforming transportation by enhancing connectivity and enabling autonomous driving. However, this increased interconnectivity introduces new security vulnerabilities. Bot malware and cyberattacks pose significant risks to Connected and Autonomous Vehicles (CAVs), as demonstrated by real-world incidents involving remote vehicle system compromise. To address these challenges, we propose an edge-based Intrusion Detection System (IDS) that monitors network traffic to and from CAVs. Our detection model is based on a meta-ensemble classifier capable of recognizing known (Nday) attacks and detecting previously unseen (zero-day) attacks. The approach involves training multiple Isolation Forest (IF) models on Multi-access Edge Computing (MEC) servers, with each IF specialized in identifying a specific type of botnet attack. These IFs, either trained locally or shared by other MEC nodes, are then aggregated using a Particle Swarm Optimization (PSO) based stacking strategy to construct a robust meta-classifier. The proposed IDS has been evaluated on a vehicular botnet dataset, achieving an average detection rate of 92.80% for N-day attacks and 77.32% for zero-day attacks. These results highlight the effectiveness of our solution in detecting both known and emerging threats, providing a scalable and adaptive defense mechanism for CAVs within the IoV ecosystem.

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arXiv2025

BotDetect: A Decentralized Federated Learning Framework for Detecting Financial Bots on the EVM Blockchains

The rapid growth of decentralized finance (DeFi) has led to the widespread use of automated agents, or bots, within blockchain ecosystems like Ethereum, Binance Smart Chain, and Solana. While these bots enhance market efficiency and liquidity, they also raise concerns due to exploitative behaviors that threaten network integrity and user trust. This paper presents a decentralized federated learning (DFL) approach for detecting financial bots within Ethereum Virtual Machine (EVM)-based blockchains. The proposed framework leverages federated learning, orchestrated through smart contracts, to detect malicious bot behavior while preserving data privacy and aligning with the decentralized nature of blockchain networks. Addressing the limitations of both centralized and rule-based approaches, our system enables each participating node to train local models on transaction history and smart contract interaction data, followed by on-chain aggregation of model updates through a permissioned consensus mechanism. This design allows the model to capture complex and evolving bot behaviors without requiring direct data sharing between nodes. Experimental results demonstrate that our DFL framework achieves high detection accuracy while maintaining scalability and robustness, providing an effective solution for bot detection across distributed blockchain networks.

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arXiv2025

BARTPredict: Empowering IoT Security with LLM-Driven Cyber Threat Prediction

The integration of Internet of Things (IoT) technology in various domains has led to operational advancements, but it has also introduced new vulnerabilities to cybersecurity threats, as evidenced by recent widespread cyberattacks on IoT devices. Intrusion detection systems are often reactive, triggered by specific patterns or anomalies observed within the network. To address this challenge, this work proposes a proactive approach to anticipate and preemptively mitigate malicious activities, aiming to prevent potential damage before it occurs. This paper proposes an innovative intrusion prediction framework empowered by Pre-trained Large Language Models (LLMs). The framework incorporates two LLMs: a fine-tuned Bidirectional and AutoRegressive Transformers (BART) model for predicting network traffic and a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model for evaluating the predicted traffic. By harnessing the bidirectional capabilities of BART the framework then identifies malicious packets among these predictions. Evaluated using the CICIoT2023 IoT attack dataset, our framework showcases a notable enhancement in predictive performance, attaining an impressive 98% overall accuracy, providing a powerful response to the cybersecurity challenges that confront IoT networks.

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arXiv2024

Beyond Detection: Leveraging Large Language Models for Cyber Attack Prediction in IoT Networks

In recent years, numerous large-scale cyberattacks have exploited Internet of Things (IoT) devices, a phenomenon that is expected to escalate with the continuing proliferation of IoT technology. Despite considerable efforts in attack detection, intrusion detection systems remain mostly reactive, responding to specific patterns or observed anomalies. This work proposes a proactive approach to anticipate and mitigate malicious activities before they cause damage. This paper proposes a novel network intrusion prediction framework that combines Large Language Models (LLMs) with Long Short Term Memory (LSTM) networks. The framework incorporates two LLMs in a feedback loop: a fine-tuned Generative Pre-trained Transformer (GPT) model for predicting network traffic and a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) for evaluating the predicted traffic. The LSTM classifier model then identifies malicious packets among these predictions. Our framework, evaluated on the CICIoT2023 IoT attack dataset, demonstrates a significant improvement in predictive capabilities, achieving an overall accuracy of 98%, offering a robust solution to IoT cybersecurity challenges.

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arXiv2024

Multi-agent Reinforcement Learning-based Network Intrusion Detection System

Intrusion Detection Systems (IDS) play a crucial role in ensuring the security of computer networks. Machine learning has emerged as a popular approach for intrusion detection due to its ability to analyze and detect patterns in large volumes of data. However, current ML-based IDS solutions often struggle to keep pace with the ever-changing nature of attack patterns and the emergence of new attack types. Additionally, these solutions face challenges related to class imbalance, where the number of instances belonging to different classes (normal and intrusions) is significantly imbalanced, which hinders their ability to effectively detect minor classes. In this paper, we propose a novel multi-agent reinforcement learning (RL) architecture, enabling automatic, efficient, and robust network intrusion detection. To enhance the capabilities of the proposed model, we have improved the DQN algorithm by implementing the weighted mean square loss function and employing cost-sensitive learning techniques. Our solution introduces a resilient architecture designed to accommodate the addition of new attacks and effectively adapt to changes in existing attack patterns. Experimental results realized using CIC-IDS-2017 dataset, demonstrate that our approach can effectively handle the class imbalance problem and provide a fine grained classification of attacks with a very low false positive rate. In comparison to the current state-of-the-art works, our solution demonstrates a significant superiority in both detection rate and false positive rate.

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arXiv2024

Anomaly-based Framework for Detecting Power Overloading Cyberattacks in Smart Grid AMI

The Advanced Metering Infrastructure (AMI) is one of the key components of the smart grid. It provides interactive services for managing billing and electricity consumption, but it also introduces new vectors for cyberattacks. Although, the devastating and severe impact of power overloading cyberattacks on smart grid AMI, few researches in the literature have addressed them. In the present paper, we propose a two-level anomaly detection framework based on regression decision trees. The introduced detection approach leverages the regularity and predictability of energy consumption to build reference consumption patterns for the whole neighborhood and each household within it. Using a reference consumption pattern enables detecting power overloading cyberattacks regardless of the attacker's strategy as they cause a drastic change in the consumption pattern. The continuous two-level monitoring of energy consumption load allows efficient and early detection of cyberattacks. We carried out an extensive experiment on a real-world publicly available energy consumption dataset of 500 customers in Ireland. We extracted, from the raw data, the relevant attributes for training the energy consumption patterns. The evaluation shows that our approach achieves a high detection rate, a low false alarm rate, and superior performances compared to existing solutions.

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arXiv2024

AntibotV: A Multilevel Behaviour-based Framework for Botnets Detection in Vehicular Networks

Connected cars offer safety and efficiency for both individuals and fleets of private vehicles and public transportation companies. However, equipping vehicles with information and communication technologies raises privacy and security concerns, which significantly threaten the user's data and life. Using bot malware, a hacker may compromise a vehicle and control it remotely, for instance, he can disable breaks or start the engine remotely. In this paper, besides in-vehicle attacks existing in the literature, we consider new zeroday bot malware attacks specific to the vehicular context, WSMP-Flood, and Geo-WSMP Flood. Then, we propose AntibotV, a multilevel behaviour-based framework for vehicular botnets detection in vehicular networks. The proposed framework combines two main modules for attack detection, the first one monitors the vehicle's activity at the network level, whereas the second one monitors the in-vehicle activity. The two intrusion detection modules have been trained on a historical network and in-vehicle communication using decision tree algorithms. The experimental results showed that the proposed framework outperforms existing solutions, it achieves a detection rate higher than 97% and a false positive rate lower than 0.14%.

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