CVE-2005-0968

Computer Associates CA eTrust Intrusion Detection 3.0 allows remote attackers to cause a denial of service via large size values that are not properly validated before calling the CPImportKey function in the Crypto API...

AI-Driven Dynamic Firewall Optimization Using Reinforcement Learning for Anomaly Detection and Prevention

The growing complexity of cyber threats has rendered static firewalls increasingly ineffective for dynamic, real-time intrusion prevention. This paper proposes a novel AI-driven dynamic firewall optimization framework that leverages deep reinforcement learning DRL to autonomously adapt and update...

Federated Learning-Enhanced Blockchain Framework for Privacy-Preserving Intrusion Detection in Industrial IoT

Industrial Internet of Things IIoT systems have become integral to smart manufacturing, yet their growing connectivity has also exposed them to significant cybersecurity threats. Traditional intrusion detection systems IDS often rely on centralized architectures that raise concerns over data...

CSAGC-IDS: a Dual-Module Deep Learning Network Intrusion Detection Model for Complex and Imbalanced Data

As computer networks proliferate, the gravity of network intrusions has escalated, emphasizing the criticality of network intrusion detection systems for safeguarding security. While deep learning models have exhibited promising results in intrusion detection, they face challenges in managing...

Adaptive Pruning of Deep Neural Networks for Resource-Aware Embedded Intrusion Detection on the Edge

Artificial neural network pruning is a method in which artificial neural network sizes can be reduced while attempting to preserve the predicting capabilities of the network. This is done to make the model smaller or faster during inference time. In this work we analyze the ability of a selection...

A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network

Connected and Autonomous Vehicles CAVs enhance mobility but face cybersecurity threats, particularly through the insecure Controller Area Network CAN bus. Cyberattacks can have devastating consequences in connected vehicles, including the loss of control over critical systems, necessitating robus...

Adaptive Security Policy Management in Cloud Environments Using Reinforcement Learning

The security of cloud environments, such as Amazon Web Services AWS, is complex and dynamic. Static security policies have become inadequate as threats evolve and cloud resources exhibit elasticity 1. This paper addresses the limitations of static policies by proposing a security policy managemen...

Evaluating Explanation Quality in X-IDS Using Feature Alignment Metrics

Explainable artificial intelligence XAI methods have become increasingly important in the context of explainable intrusion detection systems X-IDSs for improving the interpretability and trustworthiness of X-IDSs. However, existing evaluation approaches for XAI focus on model-specific properties...

Self-Supervised Transformer-Based Contrastive Learning for Intrusion Detection Systems

As the digital landscape becomes more interconnected, the frequency and severity of zero-day attacks, have significantly increased, leading to an urgent need for innovative Intrusion Detection Systems IDS. Machine Learning-based IDS that learn from the network traffic characteristics and can...

A Contrastive Federated Semi-Supervised Learning Intrusion Detection Framework for Internet of Robotic Things

In intelligent industry, autonomous driving and other environments, the Internet of Things IoT highly integrated with robotic to form the Internet of Robotic Things IoRT. However, network intrusion to IoRT can lead to data leakage, service interruption in IoRT and even physical damage by...

Intrusion Detection System Using Deep Learning for Network Security

As the number of cyberattacks and their particualr nature escalate, the need for effective intrusion detection systems IDS has become indispensable for ensuring the security of contemporary networks. Adaptive and more sophisticated threats are often beyond the reach of traditional approaches to...

Enable AIDE

Advanced intrusion detection environment AIDE is an intrusion detection tool that checks the integrity of system files and directories and identifies those maliciously tampered with. In principle, the integrity check can be performed only after an AIDE benchmark database is constructed, which...

Bridging Expertise Gaps: the Role of LLMs in Human-AI Collaboration for Cybersecurity

This study investigates whether large language models LLMs can function as intelligent collaborators to bridge expertise gaps in cybersecurity decision-making. We examine two representative tasks-phishing email detection and intrusion detection-that differ in data modality, cognitive complexity,...

Threat Exposure Indicators Detected (Critical)

Intrusion detection events may indicate that the network has been compromised and is exposed to malicious entities. It is important to be aware of any such traffic that may indicate reconnaissance activity, attacks on the network, or propagation of a threat to/from other subnets of the network...

Threat Exposure Indicators Detected (High)

Intrusion detection events may indicate that the network has been compromised and is exposed to malicious entities. It is important to be aware of any such traffic that may indicate reconnaissance activity, attacks on the network, or propagation of a threat to/from other subnets of the network...

Threat Exposure Indicators Detected (Medium)

Intrusion detection events may indicate that the network has been compromised and is exposed to malicious entities. It is important to be aware of any such traffic that may indicate reconnaissance activity, attacks on the network, or propagation of a threat to/from other subnets of the network...

Adversarial Sample Generation for Anomaly Detection in Industrial Control Systems

Machine learning ML-based intrusion detection systems IDS are vulnerable to adversarial attacks. It is crucial for an IDS to learn to recognize adversarial examples before malicious entities exploit them. In this paper, we generated adversarial samples using the Jacobian Saliency Map Attack JSMA...

Threat Exposure Indicators Detected (Low)

Intrusion detection events may indicate that the network has been compromised and is exposed to malicious entities. It is important to be aware of any such traffic that may indicate reconnaissance activity, attacks on the network, or propagation of a threat to/from other subnets of the network...

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability

While machine learning has significantly advanced Network Intrusion Detection Systems NIDS, particularly within IoT environments where devices generate large volumes of data and are increasingly susceptible to cyber threats, these models remain vulnerable to adversarial attacks. Our research...

Machine Learning for Cyber-Attack Identification from Traffic Flows

This paper presents our simulation of cyber-attacks and detection strategies on the traffic control system in Daytona Beach, FL. using Raspberry Pi virtual machines and the OPNSense firewall, along with traffic dynamics from SUMO and exploitation via the Metasploit framework. We try to answer the...