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The paper describes how to detect malicious executable files based on static analysis of their binary content. The stages of pre-processing and cleaning data extracted from different areas of executable files are analyzed. Methods of…

Malicious software, or malware, presents a continuously evolving challenge in computer security. These embedded snippets of code in the form of malicious files or hidden within legitimate files cause a major risk to systems with their…

Successful malware attacks on information technology systems can cause millions of dollars in damage, the exposure of sensitive and private information, and the irreversible destruction of data. Anti-virus systems that analyze a file's…

Analysing malware is important to understand how malicious software works and to develop appropriate detection and prevention methods. Dynamic analysis can overcome evasion techniques commonly used to bypass static analysis and provide…

It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by…

The constant growth in the number of malware - software or code fragment potentially harmful for computers and information networks - and the use of sophisticated evasion and obfuscation techniques have seriously hindered classic…

Behavioral malware detection aims to improve on the performance of static signature-based techniques used by anti-virus systems, which are less effective against modern polymorphic and metamorphic malware. Behavioral malware classification…

A serious threat today is malicious executables. It is designed to damage computer system and some of them spread over network without the knowledge of the owner using the system. Two approaches have been derived for it i.e. Signature Based…

Perimeter-based detection is no longer sufficient for mitigating the threat posed by malicious software. This is evident as antivirus (AV) products are replaced by endpoint detection and response (EDR) products, the latter allowing…

The number of malicious software (malware) is growing out of control. Syntactic signature based detection cannot cope with such growth and manual construction of malware signature databases needs to be replaced by computer learning based…

Malware analysis and detection techniques have been evolving during the last decade as a reflection to development of different malware techniques to evade network-based and host-based security protections. The fast growth in variety and…

Machine-learning methods have already been exploited as useful tools for detecting malicious executable files. They leverage data retrieved from malware samples, such as header fields, instruction sequences, or even raw bytes, to learn…

Malware ascription is a relatively unexplored area, and it is rather difficult to attribute malware and detect authorship. In this paper, we employ various Static and Dynamic features of malicious executables to classify malware based on…

In recent years malware has become increasingly sophisticated and difficult to detect prior to exploitation. While there are plenty of approaches to malware detection, they all have shortcomings when it comes to identifying malware…

In dynamic malware analysis, programs are classified as malware or benign based on their execution logs. We propose a concept of applying monotonic classification models to the analysis process, to make the trained model's predictions…

Existing research on malware detection focuses almost exclusively on the detection rate. However, in some cases, it is also important to understand the results of our algorithm, or to obtain more information, such as where to investigate in…

The continuous increase in malware samples, both in sophistication and number, presents many challenges for organizations and analysts, who must cope with thousands of new heterogeneous samples daily. This requires robust methods to quickly…

Malware represents a significant security concern in today's digital landscape, as it can destroy or disable operating systems, steal sensitive user information, and occupy valuable disk space. However, current malware detection methods,…

Machine learning has proven to be a useful tool for automated malware detection, but machine learning models have also been shown to be vulnerable to adversarial attacks. This article addresses the problem of generating adversarial malware…

The increasingly sophisticated environment in which attackers operate makes software security an even greater challenge in open-source projects, where malicious packages are prevalent. Static analysis tools, such as Malcontent, are highly…