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,...

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...

Uncover LOTS Attacks Hiding in Trusted Tools — Learn How in This Free Expert Session

Most cyberattacks today don’t start with loud alarms or broken firewalls. They start quietly—inside tools and websites your business already trusts. It’s called “Living Off Trusted Sites” LOTS—and it’s the new favorite strategy of modern attackers. Instead of breaking in, they blend in. Hackers a...

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...

Real-Time Detection of Insider Threats Using Behavioral Analytics and Deep Evidential Clustering

Insider threats represent one of the most critical challenges in modern cybersecurity. These threats arise from individuals within an organization who misuse their legitimate access to harm the organization's assets, data, or operations. Traditional security mechanisms, primarily designed for...

Streamlining HTTP Flooding Attack Detection through Incremental Feature Selection

Applications over the Web primarily rely on the HTTP protocol to transmit web pages to and from systems. There are a variety of application layer protocols, but among all, HTTP is the most targeted because of its versatility and ease of integration with online services. The attackers leverage the...

ChestyBot: Detecting and Disrupting Chinese Communist Party Influence Stratagems

Foreign information operations conducted by Russian and Chinese actors exploit the United States' permissive information environment. These campaigns threaten democratic institutions and the broader Westphalian model. Yet, existing detection and mitigation strategies often fail to identify active...

AttentionGuard: Transformer-Based Misbehavior Detection for Secure Vehicular Platoons

Vehicle platooning, with vehicles traveling in close formation coordinated through Vehicle-to-Everything V2X communications, offers significant benefits in fuel efficiency and road utilization. However, it is vulnerable to sophisticated falsification attacks by authenticated insiders that can...

GPML: Graph Processing for Machine Learning

The dramatic increase of complex, multi-step, and rapidly evolving attacks in dynamic networks involves advanced cyber-threat detectors. The GPML Graph Processing for Machine Learning library addresses this need by transforming raw network traffic traces into graph representations, enabling...

LLM-Based Threat Detection and Prevention Framework for IoT Ecosystems

The increasing complexity and scale of the Internet of Things IoT have made security a critical concern. This paper presents a novel Large Language Model LLM-based framework for comprehensive threat detection and prevention in IoT environments. The system integrates lightweight LLMs fine-tuned on...

Mitigating Backdoor Triggered and Targeted Data Poisoning Attacks in Voice Authentication Systems

Voice authentication systems remain susceptible to two major threats: backdoor triggered attacks and targeted data poisoning attacks. This dual vulnerability is critical because conventional solutions typically address each threat type separately, leaving systems exposed to adversaries who can...