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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)…
In dynamic Windows malware detection, deep learning models are extensively deployed to analyze API sequences. Methods based on API sequences play a crucial role in malware prevention. However, due to the continuous updates of APIs and the…
In multiple domains such as malware detection, automated driving systems, or fraud detection, classification algorithms are susceptible to being attacked by malicious agents willing to perturb the value of instance covariates to pursue…
Modern ransomware often generate and manage cryptographic keys on the victim's machine, giving defenders an opportunity to capture exposed keys and recover encrypted data without paying the ransom. However, recent work has raised the…
Ransomware attacks are increasing at an alarming rate, leading to large financial losses, unrecoverable encrypted data, data leakage, and privacy concerns. The prompt detection of ransomware attacks is required to minimize further damage,…
This study examines machine learning techniques like Decision Trees, Support Vector Machines, Logistic Regression, Neural Networks, and ensemble methods to detect Android malware. The study evaluates these models on a dataset of Android…
Ransomware attacks have caused billions of dollars in damages in recent years, and are expected to cause billions more in the future. Consequently, significant effort has been devoted to ransomware detection and mitigation. Behavioral-based…
Entropy-based detection methodologies have gained significant attention due to their ability to analyze structural irregularities within executable files, particularly in the identification of malicious software employing advanced…
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…
Botnets, which consist of thousands of compromised machines, can cause significant threats to other systems by launching Distributed Denial of Service (SSoS) attacks, keylogging, and backdoors. In response to these threats, new effective…
Android is currently the most extensively used smartphone platform in the world. Due to its popularity and open source nature, Android malware has been rapidly growing in recent years, and bringing great risks to users' privacy. The malware…
The applications of Artificial Intelligence (AI) methods especially machine learning techniques have increased in recent years. Classification algorithms have been successfully applied to different problems such as requirement…
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…
Using runtime execution artifacts to identify malware and its associated family is an established technique in the security domain. Many papers in the literature rely on explicit features derived from network, file system, or registry…
Despite being the most popular privacy-enhancing network, Tor is increasingly adopted by cybercriminals to obfuscate malicious traffic, hindering the identification of malware-related communications between compromised devices and Command…
Recurrent Neural Networks (RNNs) yield attractive properties for constructing Intrusion Detection Systems (IDSs) for network data. With the rise of ubiquitous Machine Learning (ML) systems, malicious actors have been catching up quickly to…
The ransomware threat has loomed over our digital life since 1989. Criminals use this type of cyber attack to lock or encrypt victims' data, often coercing them to pay exorbitant amounts in ransom. The damage ransomware causes ranges from…
Machine learning models are increasingly being adopted across various fields, such as medicine, business, autonomous vehicles, and cybersecurity, to analyze vast amounts of data, detect patterns, and make predictions or recommendations. In…
Modern commercial antivirus systems increasingly rely on machine learning to keep up with the rampant inflation of new malware. However, it is well-known that machine learning models are vulnerable to adversarial examples (AEs). Previous…
Modern cybersecurity landscapes increasingly demand sophisticated detection frameworks capable of identifying evolving threats with precision and adaptability. The proposed Zero-Space Detection framework introduces a novel approach that…