AI in Cyberpsychology: A Systematic Literature Review of Cybersecurity Enhancement by Using AI for Analyzing Psychology of Victims, Attackers, and Defenders

Cybersecurity is the practice of protecting systems, networks, and data from digital attacks. Cyberpsychology CPSY is defined as the use of psychology to enhance cybersecurity applications. Since the early 2010s, the evolution of Artificial Intelligence AI has increasingly integrated with CPSY,...

deep-learning-plus (>=0.0.5, <=0.0.7), torchfl (>=0.1.1, <=0.1.5) potentially affected by CVE-2026-31221 via pytorch-lightning (1.5.7)

pytorch-lightning PYPI version 1.5.7 is affected by a known vulnerability. The following packages have a transitive dependency on pytorch-lightning and may be impacted: - deep-learning-plus =0.0.5, =0.1.1, =0.1.5 Source cves: CVE-2026-31221 Source advisory: OSV:PYSEC-2026-3043...

Explaining Intrusion Alert Decisions of Deep Learning-Based Network Intrusion Detection Systems for Security Analysts

In this paper, we present EXP-SEC, a novel framework which can explain the intrusion detection decisions of DL-based NIDS which lead to security alerts in a way that is aligned with the domain knowledge of analysts working in Security Operations Center SOC. We highlight the following features of...

Securing Deep Learning Hardware: A Survey of Side-Channel Vulnerabilities and Countermeasures

As deep learning models are increasingly deployed in critical sectors such as healthcare, finance, and security, ensuring their protection against emerging threats has become crucial. Among these threats, side-channel attacks SCAs represent a particular challenge since they can extract sensitive...

Rise from the Ashes: LLM-Based Static Analysis for Deep Learning Framework Bugs

Deep learning DL frameworks are critical AI infrastructures that often hide bugs with serious security implications. While dynamic approaches such as fuzzing are effective in uncovering these bugs, they require real test execution and incur high computational costs. Static analysis is a natural...

TorchServe Server-Side Request Forgery vulnerability

ImpactRemote Server-Side Request Forgery SSRF Issue: TorchServe default configuration lacks proper input validation, enabling third parties to invoke remote HTTP download requests and write files to the disk. This issue could be taken advantage of to compromise the integrity of the system and...

deep-learning-plus (>=0.0.5, <=0.0.7), torchfl (>=0.1.1, <=0.1.5) potentially affected by CVE-2024-8019 via pytorch-lightning (1.5.7)

pytorch-lightning PYPI version 1.5.7 is affected by a known vulnerability. The following packages have a transitive dependency on pytorch-lightning and may be impacted: - deep-learning-plus =0.0.5, =0.1.1, =0.1.5 Source cves: CVE-2024-8019 Source advisory: OSV:PYSEC-2026-507...

On the Study of Biometric Spoofing Detection Using Deep Learning

Biometric systems are increasingly deployed in security applications; however, they remain vulnerable to spoofing attacks, in which attackers exploit counterfeit biometric data to gain unauthorized access. This research evaluates the effectiveness of state-of-the-art machine learning models,...

The Chronicles of Radio Frequency Fingerprinting

Radio Frequency Fingerprinting RFF has evolved from an early idea for radar emitter identification into a broad research field for wireless device identification and spectrum monitoring for security. Rather than presenting a conventional literature survey, this work provides a critical historical...

Cognitive Threat Intelligence and Explainable Federated Security Analytics for Distributed Infrastructure Systems

The increasing adoption of distributed infrastructure systems, cloud computing, Internet of Things IoT technologies, and edge-based architectures has significantly expanded the cybersecurity attack surface and introduced increasingly sophisticated cyber threats. Conventional centralized intrusion...

From Detection to Response: A Deep Learning and Retrieval-Augmented Generation Framework for Network Intrusion Mitigation

Machine-learning-based Intrusion Detection Systems IDS have achieved impressive accuracy in classifying network attacks, yet they consistently fall short on the question that matters most to a security analyst: what should I do next? This paper presents a unified, end-to-end framework that closes...

deep-learning-plus (>=0.0.5, <=0.0.7), torchfl (>=0.1.1, <=0.1.5) potentially affected by CVE-2026-31221 via pytorch-lightning (1.5.7)

pytorch-lightning PYPI version 1.5.7 is affected by a known vulnerability. The following packages have a transitive dependency on pytorch-lightning and may be impacted: - deep-learning-plus =0.0.5, =0.1.1, =0.1.5 Source cves: CVE-2026-31221 Source advisory: OSV:GHSA-75M9-98V2-HJPM...

Convolutional-Neural-Networks for Deanonymisation of I2P Traffic

This study investigates the potential for deanonymizing services within the Invisible Internet Project I2P network through passive traffic analysis and machine learning techniques. The primary objective is to identify distinctive patterns in I2P traffic despite the encryption of its payload. To...

EDySec: A Deep Learning-Based Explainable Dynamic Analysis Framework for Detecting Malicious Packages in PyPI Ecosystem

The security of open-source software repositories is increasingly threatened by next-gen software supply chain attacks. These attacks include multiphase malware execution, remote access activation, and dynamic payload generation. Traditional Machine Learning ML detectors struggle to detect these...

CVE-2025-69893

A side-channel vulnerability exists in the implementation of BIP-39 mnemonic processing, as observed in Trezor One v1.13.0 to v1.14.0, Trezor T v1.13.0 to v1.14.0, and Trezor Safe v1.13.0 to v1.14.0 hardware wallets. This originates from the BIP-39 standard guidelines, which induce non-constant...

EUVD-2025-209448

A side-channel vulnerability exists in the implementation of BIP-39 mnemonic processing, as observed in Trezor One v1.13.0 to v1.14.0, Trezor T v1.13.0 to v1.14.0, and Trezor Safe v1.13.0 to v1.14.0 hardware wallets. This originates from the BIP-39 standard guidelines, which induce non-constant...

CVE-2025-69893

A side-channel vulnerability exists in the implementation of BIP-39 mnemonic processing, as observed in Trezor One v1.13.0 to v1.14.0, Trezor T v1.13.0 to v1.14.0, and Trezor Safe v1.13.0 to v1.14.0 hardware wallets. This originates from the BIP-39 standard guidelines, which induce non-constant...

CVE-2025-69893

A side-channel vulnerability exists in the BIP-39 mnemonic processing implementation of Trezor One (v1.13.0 to v1.14.0), Trezor T (v1.13.0 to v1.14.0), and Trezor Safe (v1.13.0 to v1.14.0) hardware wallets. The root cause is derived from BIP-39 standard guidelines that cause non-constant time exe...

CVE-2025-69893

A side-channel vulnerability exists in the implementation of BIP-39 mnemonic processing, as observed in Trezor One v1.13.0 to v1.14.0, Trezor T v1.13.0 to v1.14.0, and Trezor Safe v1.13.0 to v1.14.0 hardware wallets. This originates from the BIP-39 standard guidelines, which induce non-constant...

PT-2026-32627

A side-channel vulnerability exists in the implementation of BIP-39 mnemonic processing, as observed in Trezor One v1.13.0 to v1.14.0, Trezor T v1.13.0 to v1.14.0, and Trezor Safe v1.13.0 to v1.14.0 hardware wallets. This originates from the BIP-39 standard guidelines, which induce non-constant...