Unveiling the Veiled: Unmasking Fileless Malware through Memory Forensics and Machine Learning

In recent times, significant advancements within the realm of malware development have dramatically reshaped the entire landscape. The reasons for targeting a system have undergone a complete transformation, shifting from file-based to fileless malware.Fileless malware poses a significant cybersecurity threat, challenging traditional detection methods. This research introduces an innovative approach that combines memory forensics and machine learning to effectively detect and mitigate fileless malware. By analyzing volatile memory and leveraging machine learning algorithms, our system automates detection.We employ virtual machines to capture memory snapshots and conduct thorough analysis using the Volatility framework.  Among various algorithms, we have determined that the Random Forest algorithm is the most effective, achieving an impressive overall accuracy rate of 93.33%. Specifically, it demonstrates a True Positive Rate (TPR) of 87.5% while maintaining a zero False Positive Rate (FPR) when applied to fileless malware obtained from HatchingTriage, AnyRun, VirusShare, PolySwarm, and JoESandbox datasets. To enhance user interaction, a user-friendly graphical interface is provided, and scalability and processing capabilities are optimized through Amazon Web Services.Experimental evaluations demonstrate high accuracy and efficiency in detecting fileless malware. This framework contributes to the advancement of cybersecurity, providing practical tools for detecting against evolving fileless malware threats

Malware Static Analysis on Microsoft Macro Attack

In the 21st century, technology is increasing rapidly, the increase in technology is the potential for cyber attacks on today's technological infrastructure. Malware that is designed to damage computer systems without the owner's knowledge at a considerable cost becomes a cyber crime. This macro malware analysis is to study the code and behavior of malware when run on an operating system. To analyze this malware, this study uses a static analysis method by analyzing malware without running the program

Machine Learning Interpretability in Malware Detection

The ever increasing processing power of modern computers, as well as the increased availability of large and complex data sets, has led to an explosion in machine learning research. This has led to increasingly complex machine learning algorithms, such as Convolutional Neural Networks, with increasingly complex applications, such as malware detection. Recently, malware authors have become increasingly successful in bypassing traditional malware detection methods, partly due to advanced evasion techniques such as obfuscation and server-side polymorphism. Further, new programming paradigms such as fileless malware, that is malware that exist only in the main memory (RAM) of the infected host, add to the challenges faced with modern day malware detection. This has led security specialists to turn to machine learning to augment their malware detection systems. However, with this new technology comes new challenges. One of these challenges is the need for interpretability in machine learning. Machine learning interpretability is the process of giving explanations of a machine learning model\u27s predictions to humans. Rather than trying to understand everything that is learnt by the model, it is an attempt to find intuitive explanations which are simple enough and provide relevant information for downstream tasks. Cybersecurity analysts always prefer interpretable solutions because of the need to fine tune these solutions. If malware analysts can\u27t interpret the reason behind a misclassification, they will not accept the non-interpretable or black box detector. In this thesis, we provide an overview of machine learning and discuss its roll in cyber security, the challenges it faces, and potential improvements to current approaches in the literature. We showcase its necessity as a result of new computing paradigms by implementing a proof of concept fileless malware with JavaScript. We then present techniques for interpreting machine learning based detectors which leverage n-gram analysis and put forward a novel and fully interpretable approach for malware detection which uses convolutional neural networks. We also define a novel approach for evaluating the robustness of a machine learning based detector

Advances in Cybercrime Prediction: A Survey of Machine, Deep, Transfer, and Adaptive Learning Techniques

Cybercrime is a growing threat to organizations and individuals worldwide, with criminals using increasingly sophisticated techniques to breach security systems and steal sensitive data. In recent years, machine learning, deep learning, and transfer learning techniques have emerged as promising tools for predicting cybercrime and preventing it before it occurs. This paper aims to provide a comprehensive survey of the latest advancements in cybercrime prediction using above mentioned techniques, highlighting the latest research related to each approach. For this purpose, we reviewed more than 150 research articles and discussed around 50 most recent and relevant research articles. We start the review by discussing some common methods used by cyber criminals and then focus on the latest machine learning techniques and deep learning techniques, such as recurrent and convolutional neural networks, which were effective in detecting anomalous behavior and identifying potential threats. We also discuss transfer learning, which allows models trained on one dataset to be adapted for use on another dataset, and then focus on active and reinforcement Learning as part of early-stage algorithmic research in cybercrime prediction. Finally, we discuss critical innovations, research gaps, and future research opportunities in Cybercrime prediction. Overall, this paper presents a holistic view of cutting-edge developments in cybercrime prediction, shedding light on the strengths and limitations of each method and equipping researchers and practitioners with essential insights, publicly available datasets, and resources necessary to develop efficient cybercrime prediction systems.Comment: 27 Pages, 6 Figures, 4 Table