438 matches found
Synthetic Data: AI'S New Weapon against Android Malware
The ever-increasing number of Android devices and the accelerated evolution of malware, reaching over 35 million samples by 2024, highlight the critical importance of effective detection methods. Attackers are now using Artificial Intelligence to create sophisticated malware variations that can...
Federated Anomaly Detection and Mitigation for EV Charging Forecasting under Cyberattacks
Electric Vehicle EV charging infrastructure faces escalating cybersecurity threats that can severely compromise operational efficiency and grid stability. Existing forecasting techniques are limited by the lack of combined robust anomaly mitigation solutions and data privacy preservation...
LogPurge: Log Data Purification for Anomaly Detection Via Rule-Enhanced Filtering
Log anomaly detection, which is critical for identifying system failures and preempting security breaches, detects irregular patterns within large volumes of log data, and impacts domains such as service reliability, performance optimization, and database log analysis. Modern log anomaly detectio...
Adaptive Dual-Layer Web Application Firewall (ADL-WAF) Leveraging Machine Learning for Enhanced Anomaly and Threat Detection
Web Application Firewalls are crucial for protecting web applications against a wide range of cyber threats. Traditional Web Application Firewalls often struggle to effectively distinguish between malicious and legitimate traffic, leading to limited efficacy in threat detection. To overcome these...
An Evaluation Framework for Network IDS/IPS Datasets: Leveraging MITRE ATT&CK and Industry Relevance Metrics
The performance of Machine Learning ML and Deep Learning DL-based Intrusion Detection and Prevention Systems IDS/IPS is critically dependent on the relevance and quality of the datasets used for training and evaluation. However, current AI model evaluation practices for developing IDS/IPS focus...
Toward Autonomous and Efficient Cybersecurity: A Multi-Objective AutoML-Based Intrusion Detection System
With increasingly sophisticated cybersecurity threats and rising demand for network automation, autonomous cybersecurity mechanisms are becoming critical for securing modern networks. The rapid expansion of Internet of Things IoT systems amplifies these challenges, as resource-constrained IoT...
On Selecting Few-Shot Examples for LLM-Based Code Vulnerability Detection
Large language models LLMs have demonstrated impressive capabilities for many coding tasks, including summarization, translation, completion, and code generation. However, detecting code vulnerabilities remains a challenging task for LLMs. An effective way to improve LLM performance is in-context...
Mind the Gap: Missing Cyber Threat Coverage in NIDS Datasets for the Energy Sector
Network Intrusion Detection Systems NIDS developed using publicly available datasets predominantly focus on enterprise environments, raising concerns about their effectiveness for converged Information Technology IT and Operational Technology OT in energy infrastructures. This study evaluates the...
A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection
Anomaly detection is a critical task in cybersecurity, where identifying insider threats, access violations, and coordinated attacks is essential for ensuring system resilience. Graph-based approaches have become increasingly important for modeling entity interactions, yet most rely on homogeneou...
CyberNER: A Harmonized STIX Corpus for Cybersecurity Named Entity Recognition
Extracting structured intelligence via Named Entity Recognition NER is critical for cybersecurity, but the proliferation of datasets with incompatible annotation schemas hinders the development of comprehensive models. While combining these resources is desirable, we empirically demonstrate that...
Adapting Large Language Models to Emerging Cybersecurity Using Retrieval Augmented Generation
Security applications are increasingly relying on large language models LLMs for cyber threat detection; however, their opaque reasoning often limits trust, particularly in decisions that require domain-specific cybersecurity knowledge. Because security threats evolve rapidly, LLMs must not only...
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...
Evaluation of Vision-LLMs in Surveillance Video
The widespread use of cameras in our society has created an overwhelming amount of video data, far exceeding the capacity for human monitoring. This presents a critical challenge for public safety and security, as the timely detection of anomalous or criminal events is crucial for effective...
ThreatIntel-Andro: Expert-Verified Benchmarking for Robust Android Malware Research
The rapidly evolving Android malware ecosystem demands high-quality, real-time datasets as a foundation for effective detection and defense. With the widespread adoption of mobile devices across industrial systems, they have become a critical yet often overlooked attack surface in industrial...
A Comprehensive Survey of Website Fingerprinting Attacks and Defenses in Tor: Advances and Open Challenges
The Tor network provides users with strong anonymity by routing their internet traffic through multiple relays. While Tor encrypts traffic and hides IP addresses, it remains vulnerable to traffic analysis attacks such as the website fingerprinting WF attack, achieving increasingly high...
Distilling Lightweight Language Models for C/C++ Vulnerabilities
The increasing complexity of modern software systems exacerbates the prevalence of security vulnerabilities, posing risks of severe breaches and substantial economic loss. Consequently, robust code vulnerability detection is essential for software security. While Large Language Models LLMs have...
Enhancing Automotive Security with a Hybrid Approach Towards Universal Intrusion Detection System
Security measures are essential in the automotive industry to detect intrusions in-vehicle networks. However, developing a one-size-fits-all Intrusion Detection System IDS is challenging because each vehicle has unique data profiles. This is due to the complex and dynamic nature of the data...
EUVD-2024-39192
Malicious code in bioql PyPI...