Related papers: An effective approach for classification of advanc…
Over the last decade, machine learning has been extensively applied to identify malicious Android applications. However, such approaches remain vulnerable against adversarial examples, i.e., examples that are subtly manipulated to fool a…
Malware remains a serious problem for corporations, government agencies, and individuals, as attackers continue to use it as a tool to effect frequent and costly network intrusions. Machine learning holds the promise of automating the work…
Numerous metamorphic and polymorphic malicious variants are generated automatically on a daily basis by mutation engines that transform the code of a malicious program while retaining its functionality, in order to evade signature-based…
As the number and complexity of malware attacks continue to increase, there is an urgent need for effective malware detection systems. While deep learning models are effective at detecting malware, they are vulnerable to adversarial…
Numerous open-source and commercial malware detectors are available. However, their efficacy is threatened by new adversarial attacks, whereby malware attempts to evade detection, e.g., by performing feature-space manipulation. In this…
Malware authors apply different techniques of control flow obfuscation, in order to create new malware variants to avoid detection. Existing Siamese neural network (SNN)-based malware detection methods fail to correctly classify different…
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…
In the past decade, the cyber-crime related to mobile devices has increased. Mobile devices, especially the ones running on Android operating system are particularly interesting to malware creators, as the users often keep the biggest…
Our computer systems for decades have been threatened by various types of hardware and software attacks of which Malwares have been one of them. This malware has the ability to steal, destroy, contaminate, gain unintended access, or even…
Malicious software attacks are having an increasingly significant economic impact. Commercial malware detection software can be costly, and tools that attribute malware to the specific software vulnerabilities it exploits are largely…
This paper summarizes the research conducted for a malware detection project using the Canadian Institute for Cybersecurity's MalMemAnalysis-2022 dataset. The purpose of the project was to explore the effectiveness and efficiency of machine…
Android malware has become an increasingly critical threat to organizations, society and individuals, posing significant risks to privacy, data security and infrastructure. As malware continues to evolve in terms of complexity and…
Malware analysis involves analyzing suspicious software to detect malicious payloads. Static malware analysis, which does not require software execution, relies increasingly on machine learning techniques to achieve scalability. Although…
Cyber security can be enhanced through application of machine learning by recasting network attack data into an image format, then applying supervised computer vision and other machine learning techniques to detect malicious specimens.…
National security is threatened by malware, which remains one of the most dangerous and costly cyber threats. As of last year, researchers reported 1.3 billion known malware specimens, motivating the use of data-driven machine learning (ML)…
Malware analysis is a complex process of examining and evaluating malicious software's functionality, origin, and potential impact. This arduous process typically involves dissecting the software to understand its components, infection…
With the growth of mobile devices and applications, the number of malicious software, or malware, is rapidly increasing in recent years, which calls for the development of advanced and effective malware detection approaches. Traditional…
Malware evolves over time and antivirus must adapt to such evolution. Hence, it is critical to detect those points in time where malware has evolved so that appropriate countermeasures can be undertaken. In this research, we perform a…
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…
Criminals use malware to disrupt cyber-systems. The number of these malware-vulnerable systems is increasing quickly as common systems, such as vehicles, routers, and lightbulbs, become increasingly interconnected cyber-systems. To address…