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In the case of malware analysis, categorization of malicious files is an essential part after malware detection. Numerous static and dynamic techniques have been reported so far for categorizing malware. This research presents a deep…
Dynamic analysis enables detecting Windows malware by executing programs in a controlled environment and logging their actions. Previous work has proposed training machine learning models, i.e., convolutional and long short-term memory…
The JavaScript programming language, which began as a simple scripting language for the Web, has become ubiquitous, spanning desktop, mobile, and server applications. This increase in usage has made JavaScript an attractive target for…
The increasing prevalence of malicious Portable Document Format (PDF) files necessitates robust and comprehensive feature extraction techniques for effective detection and analysis. This work presents a unified framework that integrates…
In this paper, a new approach called HDNA (HTML DNA) is introduced for analyzing and comparing Document Object Model (DOM) trees in order to detect differences in HTML pages. This method assigns an identifier to each HTML page based on its…
Machine learning (ML)-based malware detection systems are becoming increasingly important as malware threats increase and get more sophisticated. PDF files are often used as vectors for phishing attacks because they are widely regarded as…
Software vulnerabilities represent one of the most pressing threats to computing systems. Identifying vulnerabilities in source code is crucial for protecting user privacy and reducing economic losses. Traditional static analysis tools rely…
In this paper, we analyze existing feature selection methods to identify the key elements of network traffic data that allow intrusion detection. In addition, we propose a new feature selection method that addresses the challenge of…
Malware is a fast-growing threat to the modern computing world and existing lines of defense are not efficient enough to address this issue. This is mainly due to the fact that many prevention solutions rely on signature-based detection…
Machine learning algorithms can effectively classify malware through dynamic behavior but are susceptible to adversarial attacks. Existing attacks, however, often fail to find an effective solution in both the feature and problem spaces.…
The performance of machine learning models depends on the quality of the underlying data. Malicious actors can attack the model by poisoning the training data. Current detectors are tied to either specific data types, models, or attacks,…
Thousands of malicious applications targeting mobile devices, including the popular Android platform, are created every day. A large number of those applications are created by a small number of professional under-ground actors, however…
We consider the problem of detecting malware with deep learning models, where the malware may be combined with significant amounts of benign code. Examples of this include piggybacking and trojan horse attacks on a system, where malicious…
Malware can greatly compromise the integrity and trustworthiness of information and is in a constant state of evolution. Existing feature fusion-based detection methods generally overlook the correlation between features. And mere…
The rise of ransomware attacks has necessitated the development of effective strategies for identifying and mitigating these threats. This research investigates the utilization of a feature selection algorithm for distinguishing…
Malicious software is abundant in a world of innumerable computer users, who are constantly faced with these threats from various sources like the internet, local networks and portable drives. Malware is potentially low to high risk and can…
Digital systems find it challenging to keep up with cybersecurity threats. The daily emergence of more than 560,000 new malware strains poses significant hazards to the digital ecosystem. The traditional malware detection methods fail to…
Motivated by the transformative impact of deep neural networks (DNNs) in various domains, researchers and anti-virus vendors have proposed DNNs for malware detection from raw bytes that do not require manual feature engineering. In this…
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…
This paper proposes and studies a detection technique for adversarial scenarios (dubbed deterministic detection). This technique provides an alternative detection methodology in case the usual stochastic methods are not applicable: this can…