Gravity Falls: A Comparative Analysis of Domain-Generation Algorithm (DGA) Detection Methods for Mobile Device Spearphishing

Mobile devices are frequent targets of eCrime threat actors through SMS spearphishing smishing links that leverage Domain Generation Algorithms DGA to rotate hostile infrastructure. Despite this, DGA research and evaluation largely emphasize malware C2 and email phishing datasets, leaving limited...

AMDS: Attack-Aware Multi-Stage Defense System for Network Intrusion Detection with Two-Stage Adaptive Weight Learning

Machine learning based network intrusion detection systems are vulnerable to adversarial attacks that degrade classification performance under both gradient-based and distribution shift threat models. Existing defenses typically apply uniform detection strategies, which may not account for...

ai-security-toolkit

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SafePickle: Robust and Generic ML Detection of Malicious Pickle-Based ML Models

Model repositories such as Hugging Face increasingly distribute machine learning artifacts serialized with Python's pickle format, exposing users to remote code execution RCE risks during model loading. Recent defenses, such as PickleBall, rely on per-library policy synthesis that requires comple...

Detecting PowerShell-Based Fileless Cryptojacking Attacks Using Machine Learning

With the emergence of remote code execution RCE vulnerabilities in ubiquitous libraries and advanced social engineering techniques, threat actors have started conducting widespread fileless cryptojacking attacks. These attacks have become effective with stealthy techniques based on PowerShell-bas...

Security Bulletin: IBM Watson Machine Learning Accelerator for IBM Cloud Pak for Data is affected by multiple vulnerabilities.

Summary Multiple vulnerabilities were addressed in IBM Watson Machine Learning Accelerator for IBM Cloud Pak for Data. Follow the instructions in this document to address them. Vulnerability Details CVEID:CVE-2021-38291 DESCRIPTION: FFmpeg version git commit de8e6e67e7523e48bb27ac224a0b446df05e16...

Empirical Evaluation of SMOTE in Android Malware Detection with Machine Learning: Challenges and Performance in CICMalDroid 2020

Malware, malicious software designed to damage computer systems and perpetrate scams, is proliferating at an alarming rate, with thousands of new threats emerging daily. Android devices, prevalent in smartphones, smartwatches, tablets, and IoTs, represent a vast attack surface, making malware...

One RNG to Rule Them All: How Randomness Becomes an Attack Vector in Machine Learning

Machine learning relies on randomness as a fundamental component in various steps such as data sampling, data augmentation, weight initialization, and optimization. Most machine learning frameworks use pseudorandom number generators as the source of randomness. However, variations in design choic...

Evasion of IoT Malware Detection Via Dummy Code Injection

The Internet of Things IoT has revolutionized connectivity by linking billions of devices worldwide. However, this rapid expansion has also introduced severe security vulnerabilities, making IoT devices attractive targets for malware such as the Mirai botnet. Power side-channel analysis has...

A Systematic Literature Review on LLM Defenses against Prompt Injection and Jailbreaking: Expanding NIST Taxonomy

The rapid advancement and widespread adoption of generative artificial intelligence GenAI and large language models LLMs has been accompanied by the emergence of new security vulnerabilities and challenges, such as jailbreaking and other prompt injection attacks. These maliciously crafted inputs...

Hardware-Triggered Backdoors

Machine learning models are routinely deployed on a wide range of computing hardware. Although such hardware is typically expected to produce identical results, differences in its design can lead to small numerical variations during inference. In this work, we show that these variations can be...

CVE-2025-15469

Issue summary: The 'openssl dgst' command-line tool silently truncates input data to 16MB when using one-shot signing algorithms and reports success instead of an error. Impact summary: A user signing or verifying files larger than 16MB with one-shot algorithms such as Ed25519, Ed448, or ML-DSA m...

Benchmarking Machine Learning Models for IoT Malware Detection under Data Scarcity and Drift

The rapid expansion of the Internet of Things IoT in domains such as smart cities, transportation, and industrial systems has heightened the urgency of addressing their security vulnerabilities. IoT devices often operate under limited computational resources, lack robust physical safeguards, and...

Constructing Multi-Label Hierarchical Classification Models for MITRE ATT&CK Text Tagging

MITRE ATT&CK is a cybersecurity knowledge base that organizes threat actor and cyber-attack information into a set of tactics describing the reasons and goals threat actors have for carrying out attacks, with each tactic having a set of techniques that describe the potential methods used in these...

Techniques of Modern Attacks

The techniques used in modern attacks have become an important factor for investigation. As we advance further into the digital age, cyber attackers are employing increasingly sophisticated and highly threatening methods. These attacks target not only organizations and governments but also extend...

Hybrid IDS Using Signature-Based and Anomaly-Based Detection

Intrusion detection systems IDS are essential for protecting computer systems and networks against a wide range of cyber threats that continue to evolve over time. IDS are commonly categorized into two main types, each with its own strengths and limitations, such as difficulty in detecting...

CVE-2026-22705

RustCrypto: Signatures offers support for digital signatures, which provide authentication of data using public-key cryptography. Prior to version 0.1.0-rc.2, a timing side-channel was discovered in the Decompose algorithm which is used during ML-DSA signing to generate hints for the signature...

EUVD-2026-1867

RustCrypto: Signatures has timing side-channel in ML-DSA decomposition...

CVE-2026-22705 RustCrypto: Signatures has timing side-channel in ML-DSA decomposition

RustCrypto: Signatures offers support for digital signatures, which provide authentication of data using public-key cryptography. Prior to version 0.1.0-rc.2, a timing side-channel was discovered in the Decompose algorithm which is used during ML-DSA signing to generate hints for the signature...

Behavioral Analytics for Continuous Insider Threat Detection in Zero-Trust Architectures

Insider threats are a particularly tricky cybersecurity issue, especially in zero-trust architectures ZTA where implicit trust is removed. Although the rule of thumb is never trust, always verify, attackers can still use legitimate credentials and impersonate the standard user activity. In...