Intelligent Detection and Mitigation of Carpet-Bombing DDoS Attacks in SDN Using Retrieval-Augmented Generation and Large Language Models

Software-Defined Networking SDN provides flexible and programmable network management; however, its centralized control architecture remains highly vulnerable to Distributed Denial-of-Service DDoS attacks, particularly Carpet-Bombing DDoS attacks that distribute malicious traffic across multiple...

Smart Contract Security beyond Detection

Smart contract security has progressed from vulnerability detection toward a broader research agenda that includes semantic reasoning, automated repair, adversarial robustness, and real-time exploit detection. This paper develops a capstone-oriented research narrative around four directions:...

A-THENA: Early Intrusion Detection for IoT with Time-Aware Hybrid Encoding and Network-Specific Augmentation

The proliferation of Internet of Things IoT devices has significantly expanded attack surfaces, making IoT ecosystems particularly susceptible to sophisticated cyber threats. To address this challenge, this work introduces A-THENA, a lightweight early intrusion detection system EIDS that...

[SECURITY] Fedora 44 Update: kf6-kidletime-6.25.0-1.fc44

KDE Frameworks 6 Tier 1 integration module for idle time detection...

RansomTrack: A Hybrid Behavioral Analysis Framework for Ransomware Detection

Ransomware poses a serious and fast-acting threat to critical systems, often encrypting files within seconds of execution. Research indicates that ransomware is the most reported cybercrime in terms of financial damage, highlighting the urgent need for early-stage detection before encryption is...

Next-Generation Cyberattack Detection with Large Language Models: Anomaly Analysis across Heterogeneous Logs

This project explores large language models LLMs for anomaly detection across heterogeneous log sources. Traditional intrusion detection systems suffer from high false positive rates, semantic blindness, and data scarcity, as logs are inherently sensitive, making clean datasets rare. We address...

AI-Powered Algorithms for the Prevention and Detection of Computer Malware Infections

The rise in frequency and complexity of malware attacks are viewed as a major threat to modern digital infrastructure, which means that traditional signature-based detection methods are becoming less effective. As cyber threats continue to evolve, there is a growing need for intelligent systems t...

Detecting Malicious Entra OAuth Apps with LLM-Based Permission Risk Scoring

This project presents a unified detection framework that constructs a complete corpus of Microsoft Graph permissions, generates consistent LLM-based risk scores, and integrates them into a real-time detection engine to identify malicious OAuth consent activity...

Rapid7: 7 years of recognition in Gartner® Magic Quadrant™ for SIEM

We’re proud to share that Rapid7 has been recognized in the 2025 Gartner Magic Quadrant for Security Information and Event Management SIEM. This is the seventh year we have been positioned in this report, which means we’ve been recognized in every report following the launch of our SIEM offering,...

CST-AFNet: A Dual Attention-Based Deep Learning Framework for Intrusion Detection in IoT Networks

The rapid expansion of the Internet of Things IoT has revolutionized modern industries by enabling smart automation and real time connectivity. However, this evolution has also introduced complex cybersecurity challenges due to the heterogeneous, resource constrained, and distributed nature of...

A Statistical Method for Attack-Agnostic Adversarial Attack Detection with Compressive Sensing Comparison

Adversarial attacks present a significant threat to modern machine learning systems. Yet, existing detection methods often lack the ability to detect unseen attacks or detect different attack types with a high level of accuracy. In this work, we propose a statistical approach that establishes a...

Collaborative P4-SDN DDoS Detection and Mitigation with Early-Exit Neural Networks

Distributed Denial of Service DDoS attacks pose a persistent threat to network security, requiring timely and scalable mitigation strategies. In this paper, we propose a novel collaborative architecture that integrates a P4-programmable data plane with an SDN control plane to enable real-time DDo...

Enhancing GraphQL Security by Detecting Malicious Queries Using Large Language Models, Sentence Transformers, and Convolutional Neural Networks

GraphQL's flexibility, while beneficial for efficient data fetching, introduces unique security vulnerabilities that traditional API security mechanisms often fail to address. Malicious GraphQL queries can exploit the language's dynamic nature, leading to denial-of-service attacks, data...

Key Takeaways from the Take Command Summit 2025: Inside the SOC – Expert Stories from the Frontlines of Threat Hunting and Malware Detection

What does it really look like to detect, contain, and respond to modern cyber threats in real time? At the Take Command 2025 Virtual Cybersecurity Summit, Inside the SOC session offered a behind-the-scenes look at how security teams are tackling everything from ransomware staging to advanced soci...

ML-Enhanced AES Anomaly Detection for Real-Time Embedded Security

Advanced Encryption Standard AES is a widely adopted cryptographic algorithm, yet its practical implementations remain susceptible to side-channel and fault injection attacks. In this work, we propose a comprehensive framework that enhances AES-128 encryption security through controlled anomaly...

Intelligent ARP Spoofing Detection Using Multi-Layered Machine Learning (ML) Techniques for IoT Networks

Address Resolution Protocol ARP spoofing remains a critical threat to IoT networks, enabling attackers to intercept, modify, or disrupt data transmission by exploiting ARP's lack of authentication. The decentralized and resource-constrained nature of IoT environments amplifies this vulnerability,...

ZKTeco ZKBio Time Detection

Binary data zktecozkbiotimedetect.nbin...

MalGuard: Towards Real-Time, Accurate, and Actionable Detection of Malicious Packages in PyPI Ecosystem

Malicious package detection has become a critical task in ensuring the security and stability of the PyPI. Existing detection approaches have focused on advancing model selection, evolving from traditional machine learning ML models to large language models LLMs. However, as the complexity of the...

Network Threat Detection: Addressing Class Imbalanced Data with Deep Forest

With the rapid expansion of Internet of Things IoT networks, detecting malicious traffic in real-time has become a critical cybersecurity challenge. This research addresses the detection challenges by presenting a comprehensive empirical analysis of machine learning techniques for malware detecti...

SDN-Based False Data Detection with Its Mitigation and Machine Learning Robustness for In-Vehicle Networks

As the development of autonomous and connected vehicles advances, the complexity of modern vehicles increases, with numerous Electronic Control Units ECUs integrated into the system. In an in-vehicle network, these ECUs communicate with one another using an standard protocol called Controller Are...