相关论文: Behavioral Malware Classification using Convolutio…

Most behavioral detectors of malware remain specific to a given language and platform, mostly PE executables for Windows. The objective of this paper is to define a generic approach for behavioral detection based on two layers respectively…

Managing the threat posed by malware requires accurate detection and classification techniques. Traditional detection strategies, such as signature scanning, rely on manual analysis of malware to extract relevant features, which is labor…

Machine learning based solutions have been very helpful in solving problems that deal with immense amounts of data, such as malware detection and classification. However, deep neural networks have been found to be vulnerable to adversarial…

Classification of malware families is crucial for a comprehensive understanding of how they can infect devices, computers, or systems. Thus, malware identification enables security researchers and incident responders to take precautions…

The popularity of Windows attracts the attention of hackers/cyber-attackers, making Windows devices the primary target of malware attacks in recent years. Several sophisticated malware variants and anti-detection methods have been…

Malicious software (malware) classification offers a unique challenge for continual learning (CL) regimes due to the volume of new samples received on a daily basis and the evolution of malware to exploit new vulnerabilities. On a typical…

The use of Machine Learning has become a significant part of malware detection efforts due to the influx of new malware, an ever changing threat landscape, and the ability of Machine Learning methods to discover meaningful distinctions…

In response to the volume and sophistication of malicious software or malware, security investigators rely on dynamic analysis for malware detection to thwart obfuscation and packing issues. Dynamic analysis is the process of executing…

The proliferation of malware variants poses a significant challenges to traditional malware detection approaches, such as signature-based methods, necessitating the development of advanced machine learning techniques. In this research, we…

A novel approach to malware classification is introduced based on analysis of instruction traces that are collected dynamically from the program in question. The method has been implemented online in a sandbox environment (i.e., a security…

As machine-learning (ML) based systems for malware detection become more prevalent, it becomes necessary to quantify the benefits compared to the more traditional anti-virus (AV) systems widely used today. It is not practical to build an…

The use of machine learning and intelligent systems has become an established practice in the realm of malware detection and cyber threat prevention. In an environment characterized by widespread accessibility and big data, the feasibility…

Driven by the high profit, Portable Executable (PE) malware has been consistently evolving in terms of both volume and sophistication. PE malware family classification has gained great attention and a large number of approaches have been…

The continued evolution and diversity of malware constitutes a major threat in modern systems. It is well proven that security defenses currently available are ineffective to mitigate the skills and imagination of cyber-criminals…

The proliferation of malwares have been attributed to the alternations of a handful of original malware source codes. The malwares alternated from the same origin share some intrinsic behaviors and form a malware family. Expediently,…

Malicious software (malware) poses an increasing threat to the security of communication systems as the number of interconnected mobile devices increases exponentially. While some existing malware detection and classification approaches…

For efficient malware removal, determination of malware threat levels, and damage estimation, malware family classification plays a critical role. In this paper, we extract features from malware executable files and represent them as images…

In malware behavioral analysis, the list of accessed and created files very often indicates whether the examined file is malicious or benign. However, malware authors are trying to avoid detection by generating random filenames and/or…

Providing security for information is highly critical in the current era with devices enabled with smart technology, where assuming a day without the internet is highly impossible. Fast internet at a cheaper price, not only made…

Artificial neural networks have been successfully used for many different classification tasks including malware detection and distinguishing between malicious and non-malicious programs. Although artificial neural networks perform very…