Think Fast: Real-Time IoT Intrusion Reasoning Using IDS and LLMs at the Edge Gateway

As the number of connected IoT devices continues to grow, securing these systems against cyber threats remains a major challenge, especially in environments with limited computational and energy resources. This paper presents an edge-centric Intrusion Detection System IDS framework that integrate...

Slice-Aware Spoofing Detection in 5G Networks Using Lightweight Machine Learning

The increasing virtualization of fifth generation 5G networks expands the attack surface of the user plane, making spoofing a persistent threat to slice integrity and service reliability. This study presents a slice-aware lightweight machine-learning framework for detecting spoofing attacks withi...

Enhancing Password Security through a High-Accuracy Scoring Framework Using Random Forests

Password security plays a crucial role in cybersecurity, yet traditional password strength meters, which rely on static rules like character-type requirements, often fail. Such methods are easily bypassed by common password patterns e.g., 'P@ssw0rd1!', giving users a false sense of security. To...

An Explainable Recursive Feature Elimination to Detect Advanced Persistent Threats Using Random Forest Classifier

Intrusion Detection Systems IDS play a vital role in modern cybersecurity frameworks by providing a primary defense mechanism against sophisticated threat actors. In this paper, we propose an explainable intrusion detection framework that integrates Recursive Feature Elimination RFE with Random...

Injecting Falsehoods: Adversarial Man-In-The-Middle Attacks Undermining Factual Recall in LLMs

LLMs are now an integral part of information retrieval. As such, their role as question answering chatbots raises significant concerns due to their shown vulnerability to adversarial man-in-the-middle MitM attacks. Here, we propose the first principled attack evaluation on LLM factual memory unde...

LLM-Based Multi-Class Attack Analysis and Mitigation Framework in IoT/IIoT Networks

The Internet of Things has expanded rapidly, transforming communication and operations across industries but also increasing the attack surface and security breaches. Artificial Intelligence plays a key role in securing IoT, enabling attack detection, attack behavior analysis, and mitigation...

Securing IoT Communications Via Anomaly Traffic Detection: Synergy of Genetic Algorithm and Ensemble Method

The rapid growth of the Internet of Things IoT has transformed industries by enabling seamless data exchange among connected devices. However, IoT networks remain vulnerable to security threats such as denial of service DoS attacks, anomalous traffic, and data manipulation due to decentralized...

Attack-Specialized Deep Learning with Ensemble Fusion for Network Anomaly Detection

The growing scale and sophistication of cyberattacks pose critical challenges to network security, particularly in detecting diverse intrusion types within imbalanced datasets. Traditional intrusion detection systems IDS often struggle to maintain high accuracy across both frequent and rare...

GNN-Enhanced Traffic Anomaly Detection for Next-Generation SDN-Enabled Consumer Electronics

Consumer electronics CE connected to the Internet of Things are susceptible to various attacks, including DDoS and web-based threats, which can compromise their functionality and facilitate remote hijacking. These vulnerabilities allow attackers to exploit CE for broader system attacks while...

Adaptive Deception Framework with Behavioral Analysis for Enhanced Cybersecurity Defense

This paper presents CADL Cognitive-Adaptive Deception Layer, an adaptive deception framework achieving 99.88% detection rate with 0.13% false positive rate on the CICIDS2017 dataset. The framework employs ensemble machine learning Random Forest, XGBoost, Neural Networks combined with behavioral...

Dual-Path Phishing Detection: Integrating Transformer-Based NLP with Structural URL Analysis

Phishing emails pose a persistent and increasingly sophisticated threat, undermining email security through deceptive tactics designed to exploit both semantic and structural vulnerabilities. Traditional detection methods, often based on isolated analysis of email content or embedded URLs, fail t...

A Comparative Analysis of Ensemble-Based Machine Learning Approaches with Explainable AI for Multi-Class Intrusion Detection in Drone Networks

The growing integration of drones into civilian, commercial, and defense sectors introduces significant cybersecurity concerns, particularly with the increased risk of network-based intrusions targeting drone communication protocols. Detecting and classifying these intrusions is inherently...

Hybrid Cryptographic Monitoring System for Side-Channel Attack Detection on PYNQ SoCs

AES-128 encryption is theoretically secure but vulnerable in practical deployments due to timing and fault injection attacks on embedded systems. This work presents a lightweight dual-detection framework combining statistical thresholding and machine learning ML for real-time anomaly detection. B...

Machine Learning-Based AES Key Recovery Via Side-Channel Analysis on the ASCAD Dataset

Cryptographic algorithms like AES and RSA are widely used and they are mathematically robust and almost unbreakable but its implementation on physical devices often leak information through side channels, such as electromagnetic EM emissions, potentially compromising said theoretically secure...

Optimizing IoT Threat Detection with Kolmogorov-Arnold Networks (KANs)

The exponential growth of the Internet of Things IoT has led to the emergence of substantial security concerns, with IoT networks becoming the primary target for cyberattacks. This study examines the potential of Kolmogorov-Arnold Networks KANs as an alternative to conventional machine learning...

Leveraging Machine Learning for Botnet Attack Detection in Edge-Computing Assisted IoT Networks

The increase of IoT devices, driven by advancements in hardware technologies, has led to widespread deployment in large-scale networks that process massive amounts of data daily. However, the reliance on Edge Computing to manage these devices has introduced significant security vulnerabilities, a...

Leveraging Trustworthy AI for Automotive Security in Multi-Domain Operations: Towards a Responsive Human-AI Multi-Domain Task Force for Cyber Social Security

Multi-Domain Operations MDOs emphasize cross-domain defense against complex and synergistic threats, with civilian infrastructures like smart cities and Connected Autonomous Vehicles CAVs emerging as primary targets. As dual-use assets, CAVs are vulnerable to Multi-Surface Threats MSTs,...

Side-Channel Extraction of Dataflow AI Accelerator Hardware Parameters

Dataflow neural network accelerators efficiently process AI tasks on FPGAs, with deployment simplified by ready-to-use frameworks and pre-trained models. However, this convenience makes them vulnerable to malicious actors seeking to reverse engineer valuable Intellectual Property IP through...

Are Trees Really Green? A Detection Approach of IoT Malware Attacks

Nowadays, the Internet of Things IoT is widely employed, and its usage is growing exponentially because it facilitates remote monitoring, predictive maintenance, and data-driven decision making, especially in the healthcare and industrial sectors. However, IoT devices remain vulnerable due to the...

There'S Waldo: PCB Tamper Forensic Analysis Using Explainable AI on Impedance Signatures

The security of printed circuit boards PCBs has become increasingly vital as supply chain vulnerabilities, including tampering, present significant risks to electronic systems. While detecting tampering on a PCB is the first step for verification, forensics is also needed to identify the modified...