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Cyber-crimes have become a multi-billion-dollar industry in the recent years. Most cybercrimes/attacks involve deploying some type of malware. Malware that viciously targets every industry, every sector, every enterprise and even…

Current malware detection and classification approaches generally rely on time consuming and knowledge intensive processes to extract patterns (signatures) and behaviors from malware, which are then used for identification. Moreover, these…

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,…

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…

Machine learning methods can detect Android malware with very high accuracy. However, these classifiers have an Achilles heel, concept drift: they rapidly become out of date and ineffective, due to the evolution of malware apps and benign…

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…

Mobile malware has continued to grow at an alarming rate despite on-going efforts towards mitigating the problem. This has been particularly noticeable on Android due to its being an open platform that has subsequently overtaken other…

Machine learning is becoming increasingly popular as a go-to approach for many tasks due to its world-class results. As a result, antivirus developers are incorporating machine learning models into their products. While these models improve…

Machine learning (ML) has been widely used to analyze API call sequences in malware analysis, which typically requires the expertise of domain specialists to extract relevant features from raw data. The extracted features play a critical…

In this chapter, readers will explore how machine learning has been applied to build malware detection systems designed for the Windows operating system. This chapter starts by introducing the main components of a Machine Learning pipeline,…

We design a classifier for transactional datasets with application in malware detection. We build the classifier based on the minimum description length (MDL) principle. This involves selecting a model that best compresses the training…

The tremendous growth in smart devices has uplifted several security threats. One of the most prominent threats is malicious software also known as malware. Malware has the capability of corrupting a device and collapsing an entire network.…

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…

In the current cybersecurity landscape, protecting military devices such as communication and battlefield management systems against sophisticated cyber attacks is crucial. Malware exploits vulnerabilities through stealth methods, often…

The damage caused by crypto-ransomware, due to encryption, is difficult to revert and cause data losses. In this paper, a machine learning (ML) classifier was built to early detect ransomware (called crypto-ransomware) that uses…

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…

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…

Malware poses a significant security risk to individuals, organizations, and critical infrastructure by compromising systems and data. Leveraging memory dumps that offer snapshots of computer memory can aid the analysis and detection of…

Active learning for classification seeks to reduce the cost of labeling samples by finding unlabeled examples about which the current model is least certain and sending them to an annotator/expert to label. Bayesian theory can provide a…

The variety of services and functionality offered by various cloud service providers (CSP) have exploded lately. Utilizing such services has created numerous opportunities for enterprises infrastructure to become cloud-based and, in turn,…