A Comparison of Selected Image Transformation Techniques for Malware Classification

Recently, a considerable amount of malware research has focused on the use of powerful image-based machine learning techniques, which generally yield impressive results. However, before image-based techniques can be applied to malware, the samples must be converted to images, and there is no...

Microsoft Launches Project Ire to Autonomously Classify Malware Using AI Tools

Microsoft on Tuesday announced an autonomous artificial intelligence AI agent that can analyze and classify software without assistance in an effort to advance malware detection efforts. The large language model LLM-powered autonomous malware classification system, currently a prototype, has been...

Microsoft Launches Project Ire to Autonomously Classify Malware Using AI Tools

Microsoft on Tuesday announced an autonomous artificial intelligence AI agent that can analyze and classify software without assistance in an effort to advance malware detection efforts. The large language model LLM-powered autonomous malware classification system, currently a prototype, has been...

Adaptive Malware Detection Using Sequential Feature Selection: a Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification

Traditional malware detection methods exhibit computational inefficiency due to exhaustive feature extraction requirements, creating accuracy-efficiency trade-offs that limit real-time deployment. We formulate malware classification as a Markov Decision Process with episodic feature acquisition a...

Dynamic Malware Classification of Windows PE Files Using CNNs and Greyscale Images Derived from Runtime API Call Argument Conversion

Malware detection and classification remains a topic of concern for cybersecurity, since it is becoming common for attackers to use advanced obfuscation on their malware to stay undetected. Conventional static analysis is not effective against polymorphic and metamorphic malware as these change...

Aurora: Are Android Malware Classifiers Reliable under Distribution Shift?

The performance figures of modern drift-adaptive malware classifiers appear promising, but does this translate to genuine operational reliability? The standard evaluation paradigm primarily focuses on baseline performance metrics, neglecting confidence-error alignment and operational stability...

MalVis: a Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification

As technology advances, Android malware continues to pose significant threats to devices and sensitive data. The open-source nature of the Android OS and the availability of its SDK contribute to this rapid growth. Traditional malware detection techniques, such as signature-based, static, and...

OpCode-Based Malware Classification Using Machine Learning and Deep Learning Techniques

This technical report presents a comprehensive analysis of malware classification using OpCode sequences. Two distinct approaches are evaluated: traditional machine learning using n-gram analysis with Support Vector Machine SVM, K-Nearest Neighbors KNN, and Decision Tree classifiers; and a deep...

Fedora: Security Advisory for yara (FEDORA-2022-21cf5402fc)

The remote host is missing an update for the SPDX-FileCopyrightText: 2022 Greenbone AG Some text descriptions might be excerpted from a referenced sources, and are Copyright C by the respective right holders. SPDX-License-Identifier: GPL-2.0-only ifdescription...

Using EM Waves to Detect Malware

I dont even know what I think about this. Researchers have developed a malware detection system that uses EM waves: "Obfuscation Revealed: Leveraging Electromagnetic Signals for Obfuscated Malware Classification." Abstract: The Internet of Things IoT is constituted of devices that are exponential...

Combing through the fuzz: Using fuzzy hashing and deep learning to counter malware detection evasion techniques

Today’s cybersecurity threats continue to find ways to fly and stay under the radar. Cybercriminals use polymorphic malware because a slight change in the binary code or script could allow the said threats to avoid detection by traditional antivirus software. Threat actors customize their wares...

Fedora: Security Advisory for yara (FEDORA-2021-f41d5fc954)

The remote host is missing an update for the Copyright C 2021 Greenbone Networks GmbH Some text descriptions might be excerpted from a referenced sources, and are Copyright C by the respective right holders. SPDX-License-Identifier: GPL-2.0-or-later This program is free software; you can...

Microsoft researchers work with Intel Labs to explore new deep learning approaches for malware classification

The opportunities for innovative approaches to threat detection through deep learning, a category of algorithms within the larger framework of machine learning, are vast. Microsoft Threat Protection today uses multiple deep learning-based classifiers that detect advanced threats, for example,...

Unprotect Project: Classify Malwares Based on Known Evasion Techniques

PenTestIT RSS Feed One of the first steps in learning about a malware is to see if it is evasive in any sense and then proceed accordingly. The Unprotect Project helps you do this easily. It is an open source project in Python that proposes a malware classification techniques based on their evasi...

Malware Network Communication Provides Better Early Warning Signal

Research is expected to be unveiled today that challenges the industry’s current reliance on dynamic malware analysis as the best means of early detection of infections. Instead, researchers from the Georgia Institute of Technology, the IMDEA Software Institute and EURECOM posit that a better...

[SECURITY] Fedora 24 Update: yara-3.5.0-7.fc24

YARA is a tool aimed at but not limited to helping malware researchers to identify and classify malware samples. With YARA you can create descriptions of malware families or whatever you want to describe based on textual or binary patterns. Each description, a.k.a rule, consists of a set of strin...

Adobe releases open source malware classification tool

Adobe releases open source malware classification tool Adobe Systems has released a malware classification tool in order to help security incident first responders, malware analysts and security researchers more easily identify malicious binary files. The 'Adobe Malware Classifier' tool uses...