Machine Learning Aided Static Malware Analysis: A Survey and Tutorial

Malware analysis and detection techniques have been evolving during the last decade as a reflection to development of different malware techniques to evade network-based and host-based security protections. The fast growth in variety and number of malware species made it very difficult for forensics investigators to provide an on time response. Therefore, Machine Learning (ML) aided malware analysis became a necessity to automate different aspects of static and dynamic malware investigation. We believe that machine learning aided static analysis can be used as a methodological approach in technical Cyber Threats Intelligence (CTI) rather than resource-consuming dynamic malware analysis that has been thoroughly studied before. In this paper, we address this research gap by conducting an in-depth survey of different machine learning methods for classification of static characteristics of 32-bit malicious Portable Executable (PE32) Windows files and develop taxonomy for better understanding of these techniques. Afterwards, we offer a tutorial on how different machine learning techniques can be utilized in extraction and analysis of a variety of static characteristic of PE binaries and evaluate accuracy and practical generalization of these techniques. Finally, the results of experimental study of all the method using common data was given to demonstrate the accuracy and complexity. This paper may serve as a stepping stone for future researchers in cross-disciplinary field of machine learning aided malware forensics.Comment: 37 Page

Authorship Attribution Through Words Surrounding Named Entities

In text analysis, authorship attribution occurs in a variety of ways. The field of computational linguistics becomes more important as the need of authorship attribution and text analysis becomes more widespread. For this research, pre-existing authorship attribution software, Java Graphical Authorship Attribution Program (JGAAP), implements a named entity recognizer, specifically the Stanford Named Entity Recognizer, to probe into similar genre text and to aid in extricating the correct author. This research specifically examines the words authors use around named entities in order to test the ability of these words at attributing authorshi

Designing and Evaluating an Automatic Forensic Model for Fast Response of Cross-Border E-Commerce Security Incidents

[[abstract]]The rapid development of cross-border e-commerce over the past decade has accelerated the integration of the global economy. At the same time, cross-border e-commerce has increased the prevalence of cybercrime, and the future success of e-commerce depends on enhanced online privacy and security. However, investigating security incidents is time- and cost-intensive as identifying telltale anomalies and the source of attacks requires the use of multiple forensic tools and technologies and security domain knowledge. Prompt responses to cyber-attacks are important to reduce damage and loss and to improve the security of cross-border e-commerce. This article proposes a digital forensic model for first incident responders to identify suspicious system behaviors. A prototype system is developed and evaluated by incident response handlers. The model and system are proven to help reduce time and effort in investigating cyberattacks. The proposed model is expected to enhance security incident handling efficiency for cross-border e-commerce.[[notice]]補正完

Integrating Python with leading computer forensics platforms

Integrating Python with Leading Computer Forensic Platforms takes a definitive look at how and why the integration of Python advances the field of digital forensics. In addition, the book includes practical, never seen Python examples that can be immediately put to use. Noted author Chet Hosmer demonstrates how to extend four key Forensic Platforms using Python, including EnCase by Guidance Software, MPE+ by AccessData, The Open Source Autopsy/SleuthKit by Brian Carrier and WetStone Technologies, and Live Acquisition and Triage Tool US-LATT. This book is for practitioners, forensic investigators, educators, students, private investigators, or anyone advancing digital forensics for investigating cybercrime. Additionally, the open source availability of the examples allows for sharing and growth within the industry. This book is the first to provide details on how to directly integrate Python into key forensic platforms.Print version record.Integrating Python with Leading Computer Forensic Platforms takes a definitive look at how and why the integration of Python advances the field of digital forensics. In addition, the book includes practical, never seen Python examples that can be immediately put to use. Noted author Chet Hosmer demonstrates how to extend four key Forensic Platforms using Python, including EnCase by Guidance Software, MPE+ by AccessData, The Open Source Autopsy/SleuthKit by Brian Carrier and WetStone Technologies, and Live Acquisition and Triage Tool US-LATT. This book is for practitioners, forensic investigators, educators, students, private investigators, or anyone advancing digital forensics for investigating cybercrime. Additionally, the open source availability of the examples allows for sharing and growth within the industry. This book is the first to provide details on how to directly integrate Python into key forensic platforms.Includes bibliographical references at the end of each chapters and index.Elsevie