State of the Field
Recent developments in cybersecurity research are increasingly focused on enhancing the resilience of systems against sophisticated threats. A notable trend is the evaluation and mitigation of vulnerabilities in large language models (LLMs) when processing malicious URLs, highlighting the urgent need for benchmarks that can assess these risks effectively. Additionally, the integration of decision-aware frameworks in Security Operations Centers aims to improve alert triage by aligning machine learning outputs with human decision-making processes, thereby reducing analyst overload. Automation tools like AEGIS are transforming the creation of cyber defense scenarios, enabling rapid development of attack paths without extensive expert input. Meanwhile, collaborative intrusion detection systems are being optimized for dynamic environments, ensuring efficient threat response across diverse infrastructures. The emergence of comprehensive datasets for phishing detection also underscores the importance of addressing evolving attack vectors. Collectively, these advancements indicate a shift towards more adaptive, user-centric cybersecurity solutions that prioritize both efficiency and effectiveness.
Papers
1–10 of 10MalURLBench: A Benchmark Evaluating Agents' Vulnerabilities When Processing Web URLs
LLM-based web agents have become increasingly popular for their utility in daily life and work. However, they exhibit critical vulnerabilities when processing malicious URLs: accepting a disguised mal...
MI$^2$DAS: A Multi-Layer Intrusion Detection Framework with Incremental Learning for Securing Industrial IoT Networks
The rapid expansion of Industrial IoT (IIoT) systems has amplified security challenges, as heterogeneous devices and dynamic traffic patterns increase exposure to sophisticated and previously unseen c...
Decision-Aware Trust Signal Alignment for SOC Alert Triage
Detection systems that utilize machine learning are progressively implemented at Security Operations Centers (SOCs) to help an analyst to filter through high volumes of security alerts. Practically, s...
AEGIS: White-Box Attack Path Generation using LLMs and Training Effectiveness Evaluation for Large-Scale Cyber Defence Exercises
Creating attack paths for cyber defence exercises requires substantial expert effort. Existing automation requires vulnerability graphs or exploit sets curated in advance, limiting where it can be app...
Resource-Aware Deployment Optimization for Collaborative Intrusion Detection in Layered Networks
Collaborative Intrusion Detection Systems (CIDS) are increasingly adopted to counter cyberattacks, as their collaborative nature enables them to adapt to diverse scenarios across heterogeneous environ...
Connect the Dots: Knowledge Graph-Guided Crawler Attack on Retrieval-Augmented Generation Systems
Retrieval-augmented generation (RAG) systems integrate document retrieval with large language models and have been widely adopted. However, in privacy-related scenarios, RAG introduces a new privacy r...
CIC-Trap4Phish: A Unified Multi-Format Dataset for Phishing and Quishing Attachment Detection
Phishing attacks represents one of the primary attack methods which is used by cyber attackers. In many cases, attackers use deceptive emails along with malicious attachments to trick users into givin...
CAM-LDS: Cyber Attack Manifestations for Automatic Interpretation of System Logs and Security Alerts
Log data are essential for intrusion detection and forensic investigations. However, manual log analysis is tedious due to high data volumes, heterogeneous event formats, and unstructured messages. Ev...
Multi-Targeted Graph Backdoor Attack
Graph neural network (GNN) have demonstrated exceptional performance in solving critical problems across diverse domains yet remain susceptible to backdoor attacks. Existing studies on backdoor attack...
Evaluating Human and Machine Confidence in Phishing Email Detection: A Comparative Study
Identifying deceptive content like phishing emails demands sophisticated cognitive processes that combine pattern recognition, confidence assessment, and contextual analysis. This research examines ho...