AutoDP-LLM: Automating Data Pre-Processing for Intrusion Detection Systems Using Large Language Models

The increasing complexity and scale of modern cyber-attacks demand intelligent and computationally efficient Intrusion Detection Systems IDS. However, designing effective data pre-processing pipelines traditionally involves substantial trial-and-error effort and repeated evaluation of alternative...

Reliable Federated TinyML Deployment for IoT Security

The growing deployment of Internet of Things IoT devices has increased the need for privacy-preserving intrusion detection systems that operate directly on resource-constrained hardware. Federated Learning enables collaborative model training without sharing raw data, but conventional federated...

PT-2026-92442

Name of the Vulnerable Software and Affected Versions Intrusion detection systems affected versions not specified Description A limited buffer overflow exists in the network security monitoring component. An unauthenticated remote attacker could exploit this flaw to cause a denial-of-service or...

Multi-LLM Consensus Framework for Evaluating Banking-Sector NIDS Dataset Coverage of MITRE ATT&CK Techniques

The systemic criticality of global banking networks has ren-dered them high-priority targets for advanced persistent threats, neces-sitating Network Intrusion Detection Systems NIDS whose operational effectiveness must extend beyond statistical accuracy. However, a signif-icant validation gap...

tcpreplay 4.6.0

tcpreplay is a BSD-style licensed tool to replay saved tcpdump files at arbitrary speeds. It provides a variety of features for replaying traffic for both passive sniffer devices as well as inline devices such as routers, firewalls, and intrusion detection systems. Many NIDSs fare poorly when...

tcpreplay 4.5.3

tcpreplay is a BSD-style licensed tool to replay saved tcpdump files at arbitrary speeds. It provides a variety of features for replaying traffic for both passive sniffer devices as well as inline devices such as routers, firewalls, and intrusion detection systems. Many NIDSs fare poorly when...

Generative AI and Federated Learning for Intrusion Detection Systems: A Survey

Intrusion Detection Systems IDSs are essential for monitoring network traffic and identifying malicious activities in modern cyber-physical, Internet of Things IoT, enterprise, and distributed network environments. However, developing reliable IDS models remains challenging because attack behavio...

FreeBSD -- Incorrect audit records for ptrace(2) syscall requests

Problem Description: When auditing a system call executed via ptracePTSCREMOTE, the kernel passed the return value of an internal setup function to AUDITSYSCALLEXIT rather than the actual result of the executed system call. As a result, committed audit records for system calls which returned an...

A Hybrid Framework for Crypto-Ransomware Detection in Enterprise Shared Storage

Most corporate workplace environments enforce policies and technical controls that limit the storage of sensitive data on client endpoints. Consequently, ransomware operators have evolved variants that expand their attack surface from local systems to network drives and shared storage resources. ...

Evaluating and Combating the Impact of Concept Drift on the Performance of Machine Learning-Based Phishing Detection Systems

The expansion of the digital domain has resulted in a substantial increase in digital communication, with email emerging as one of the most prominent channels. The proliferation of email communication is apparent in both professional and personal contexts, thereby creating numerous vulnerabilitie...

On the Challenges of Holistic Intrusion Detection in ICS

Past attacks against industrial control systems ICS show that adversaries often target both the ICS network and the physical process to achieve potential catastrophic impact. To secure ICS, intrusion detection systems promise timely uncovering of such adversaries. However, as these detection...

The Role of Learning in Attacking Intrusion Detection Systems

Recent work on network attacks have demonstrated that ML-based network intrusion detection systems NIDS can be evaded with adversarial perturbations. However, these attacks rely on complex optimizations that have large computational overheads, making them impractical in many real-world settings. ...

ACORN-IDS: Adaptive Continual Novelty Detection for Intrusion Detection Systems

Intrusion Detection Systems IDS must maintain reliable detection performance under rapidly evolving benign traffic patterns and the continual emergence of cyberattacks, including zero-day threats with no labeled data available. However, most machine learning-based IDS approaches either assume...

Stealth and Evasion in Rogue AP Attacks: An Analysis of Modern Detection and Bypass Techniques

Wireless networks act as the backbone of modern digital connectivity, making them a primary target for cyber adversaries. Rogue Access Point attacks, specifically the Evil Twin variant, enable attackers to clone legitimate wireless network identifiers to deceive users into connecting. Once a...

DDoS Attacks in Cloud Computing: Detection and Prevention

DDoS attacks are one of the most prevalent and harmful cybersecurity threats faced by organizations and individuals today. In recent years, the complexity and frequency of DDoS attacks have increased significantly, making it challenging to detect and mitigate them effectively. The study analyzes...

MAL-2025-4125 Malicious code in sdkss (npm)

--- -= Per source details. Do not edit below this line.=- Source: ghsa-malware f46cdba3fb83ff72cb7127859fb5140d6fc6f8ed207f45919c5c64bc494f3097 Any computer that has this package installed or running should be considered fully compromised. All secrets and keys stored on that computer should be...

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...

IBM QRadar Suite Information Disclosure

The vulnerabilities of event monitoring platforms, threat detection systems, and security analytics tools from IBM QRadar Suite and IBM Cloud Pak for Security are related to information leaks through the process environment. Exploiting these vulnerabilities can allow attackers operating remotely ...

IBM QRadar Suite Security Analytics Unencrypted Credentials

The vulnerabilities of event monitoring platforms, threat detection systems, and security analytics tools from IBM QRadar Suite and IBM Cloud Pak for Security are related to the unencrypted storage of user credentials. Exploiting these vulnerabilities can allow attackers operating remotely to gai...

IBM QRadar Suite Security Analytics Tools Registration Data Access

The vulnerabilities of event monitoring platforms, threat detection systems, and security analytics tools from IBM QRadar Suite and IBM Cloud Pak for Security are related to insufficient protection of registration data. Exploiting these vulnerabilities can allow attackers to access confidential...