Recent advancements in cybersecurity are increasingly focused on enhancing threat detection and response capabilities through innovative frameworks and machine learning techniques. For instance, the integration of multi-agent systems with traditional models is being explored to improve anomaly detection and reduce alert fatigue in threat provenance practices. This shift aims to automate investigations and provide high-fidelity alerts, addressing the limitations of human expertise in processing vast data logs. Additionally, novel embedding methods for DNS queries are being developed to enhance intrusion detection systems, allowing for better generalization across various tasks. The rise of Industrial IoT has prompted the creation of multi-layer intrusion detection frameworks that adapt to evolving threats with minimal labeling, while collaborative systems are being optimized for resource-aware deployment in dynamic environments. These developments not only promise to bolster security measures but also offer practical solutions to pressing commercial challenges, such as reducing operational costs and improving response times to cyber threats.
Advanced Persistent Threats (APTs) pose critical challenges to modern cybersecurity due to their multi-stage and stealthy nature. While provenance-based detection approaches show promise in capturing ...
Network intrusion detection systems play a crucial role in the security strategy employed by organisations to detect and prevent cyberattacks. Such systems usually combine pattern detection signatures...
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...
Advanced Persistent Threats (APTs) evolve through multiple stages, each exhibiting distinct temporal and structural behaviors. Accurate stage estimation is critical for enabling adaptive cyber defense...
The transition to a cloud-native 5G Service-Based Architecture (SBA) improves scalability but exposes control-plane signaling to emerging quantum threats, including Harvest-Now, Decrypt-Later (HNDL) a...
Detecting and responding to cyber attacks is increasingly difficult as high-volume, complex network traffic allows threats to remain concealed. While Intrusion Detection Systems (IDSs) identify anomal...
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...
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...
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...
Considering the rise of cyberattacks incidents worldwide, the need to ensure stronger passwords is necessary. Developing a password strength meter (PSM) can help users create stronger passwords when c...