CAFE-GB: Scalable and Stable Feature Selection for Malware Detection Via Chunk-Wise Aggregated Gradient Boosting

High-dimensional malware datasets often exhibit feature redundancy, instability, and scalability limitations, which hinder the effectiveness and interpretability of machine learning-based malware detection systems. Although feature selection is commonly employed to mitigate these issues, many...

Agent Skills in the Wild: An Empirical Study of Security Vulnerabilities at Scale

The rise of AI agent frameworks has introduced agent skills, modular packages containing instructions and executable code that dynamically extend agent capabilities. While this architecture enables powerful customization, skills execute with implicit trust and minimal vetting, creating a...

Better Call Graphs: A New Dataset of Function Call Graphs for Malware Classification

Function call graphs FCGs have emerged as a powerful abstraction for malware detection, capturing the behavioral structure of applications beyond surface-level signatures. Their utility in traditional program analysis has been well established, enabling effective classification and analysis of...

APT-ClaritySet: A Large-Scale, High-Fidelity Labeled Dataset for APT Malware with Alias Normalization and Graph-Based Deduplication

Large-scale, standardized datasets for Advanced Persistent Threat APT research are scarce, and inconsistent actor aliases and redundant samples hinder reproducibility. This paper presents APT-ClaritySet and its construction pipeline that normalizes threat actor aliases reconciling approximately...

Characterizing Large-Scale Adversarial Activities through Large-Scale Honey-Nets

The increasing sophistication of cyber threats demands novel approaches to characterize adversarial strategies, particularly those targeting critical infrastructure and IoT ecosystems. This paper presents a longitudinal analysis of attacker behavior using HoneyTrap, an adaptive honeypot framework...

Hey There! You Are Using WhatsApp: Enumerating Three Billion Accounts for Security and Privacy

WhatsApp, with 3.5 billion active accounts as of early 2025, is the world's largest instant messaging platform. Given its massive user base, WhatsApp plays a critical role in global communication. To initiate conversations, users must first discover whether their contacts are registered on the...

Aggregated Rate Limiting Defends Against Large-Scale and DDoS Attacks

Discover how Akamai’s new aggregated rate limiting strengthens defenses against large-scale, distributed DDoS attacks, and API abuse with smarter detection...

The Dark Side of Flexibility: How Aggregated Cyberattacks Threaten the Power Grid

Flexible energy resources are increasingly becoming common in smart grids. These resources are typically managed and controlled by aggregators that coordinate many resources to provide flexibility services. However, these aggregators and flexible energy resources are vulnerable, which could allow...

Chinese Hackers Use Anthropic's AI to Launch Automated Cyber Espionage Campaign

State-sponsored threat actors from China used artificial intelligence AI technology developed by Anthropic to orchestrate automated cyber attacks as part of a "highly sophisticated espionage campaign" in mid-September 2025. "The attackers used AI's 'agentic' capabilities to an unprecedented degre...

Over 67,000 Fake npm Packages Flood Registry in Worm-Like Spam Attack

Cybersecurity researchers are calling attention to a large-scale spam campaign that has flooded the npm registry with thousands of fake packages since early 2024 as part of a likely financially motivated effort. "The packages were systematically published over an extended period, flooding the npm...

A Large Scale Study of AI-Based Binary Function Similarity Detection Techniques for Security Researchers and Practitioners

Binary Function Similarity Detection BFSD is a foundational technique in software security, underpinning a wide range of applications including vulnerability detection, malware analysis. Recent advances in AI-based BFSD tools have led to significant performance improvements. However, existing...

Secure Retrieval-Augmented Generation against Poisoning Attacks

Large language models LLMs have transformed natural language processing NLP, enabling applications from content generation to decision support. Retrieval-Augmented Generation RAG improves LLMs by incorporating external knowledge but also introduces security risks, particularly from data poisoning...

Benchmarking Fake Voice Detection in the Fake Voice Generation Arms Race

As advances in synthetic voice generation accelerate, an increasing variety of fake voice generators have emerged, producing audio that is often indistinguishable from real human speech. This evolution poses new and serious threats across sectors where audio recordings serve as critical evidence...

Where Have All the Firewalls Gone? Security Consequences of Residential IPv6 Transition

IPv4 NAT has limited the spread of IoT botnets considerably by default-denying bots' incoming connection requests to in-home devices unless the owner has explicitly allowed them. As the Internet transitions to majority IPv6, however, residential connections no longer require the use of NAT. This...

New HTTP/2 'MadeYouReset' Vulnerability Enables Large-Scale DoS Attacks

Multiple HTTP/2 implementations have been found susceptible to a new attack technique called MadeYouReset that could be explored to conduct powerful denial-of-service DoS attacks. "MadeYouReset bypasses the typical server-imposed limit of 100 concurrent HTTP/2 requests per TCP connection from a...

Hot-Swap MarkBoard: an Efficient Black-Box Watermarking Approach for Large-Scale Model Distribution

Recently, Deep Learning DL models have been increasingly deployed on end-user devices as On-Device AI, offering improved efficiency and privacy. However, this deployment trend poses more serious Intellectual Property IP risks, as models are distributed on numerous local devices, making them...

Learning-Based Privacy-Preserving Graph Publishing against Sensitive Link Inference Attacks

Publishing graph data is widely desired to enable a variety of structural analyses and downstream tasks. However, it also potentially poses severe privacy leakage, as attackers may leverage the released graph data to launch attacks and precisely infer private information such as the existence of...

Hyper-Volumetric DDoS Attacks Reach Record 7.3 Tbps, Targeting Key Global Sectors

Cloudflare on Tuesday said it mitigated 7.3 million distributed denial-of-service DDoS attacks in the second quarter of 2025, a significant drop from 20.5 million DDoS attacks it fended off the previous quarter. "Overall, in Q2 2025, hyper-volumetric DDoS attacks skyrocketed," Omer Yoachimik and...

PhreshPhish: a Real-World, High-Quality, Large-Scale Phishing Website Dataset and Benchmark

Phishing remains a pervasive and growing threat, inflicting heavy economic and reputational damage. While machine learning has been effective in real-time detection of phishing attacks, progress is hindered by lack of large, high-quality datasets and benchmarks. In addition to poor-quality due to...

KeyDroid: a Large-Scale Analysis of Secure Key Storage in Android Apps

Most contemporary mobile devices offer hardware-backed storage for cryptographic keys, user data, and other sensitive credentials. Such hardware protects credentials from extraction by an adversary who has compromised the main operating system, such as a malicious third-party app. Since 2011,...