835 research outputs found
Android Anti-forensics: Modifying CyanogenMod
Mobile devices implementing Android operating systems inherently create
opportunities to present environments that are conducive to anti-forensic
activities. Previous mobile forensics research focused on applications and data
hiding anti-forensics solutions. In this work, a set of modifications were
developed and implemented on a CyanogenMod community distribution of the
Android operating system. The execution of these solutions successfully
prevented data extractions, blocked the installation of forensic tools, created
extraction delays and presented false data to industry accepted forensic
analysis tools without impacting normal use of the device. The research
contribution is an initial empirical analysis of the viability of operating
system modifications in an anti-forensics context along with providing the
foundation for future research.Comment: Karlsson, K.-J. and W.B. Glisson, Android Anti-forensics: Modifying
CyanogenMod in Hawaii International Conference on System Sciences (HICSS-47).
2014, IEEE Computer Society Press: Hawai
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
D2WFP: A Novel Protocol for Forensically Identifying, Extracting, and Analysing Deep and Dark Web Browsing Activities
The use of the un-indexed web, commonly known as the deep web and dark web,
to commit or facilitate criminal activity has drastically increased over the
past decade. The dark web is an in-famously dangerous place where all kinds of
criminal activities take place [1-2], despite advances in web forensics
techniques, tools, and methodologies, few studies have formally tackled the
dark and deep web forensics and the technical differences in terms of
investigative techniques and artefacts identification and extraction. This
research proposes a novel and comprehensive protocol to guide and assist
digital forensics professionals in investigating crimes committed on or via the
deep and dark web, The protocol named D2WFP establishes a new sequential
approach for performing investigative activities by observing the order of
volatility and implementing a systemic approach covering all browsing related
hives and artefacts which ultimately resulted into improv-ing the accuracy and
effectiveness. Rigorous quantitative and qualitative research has been
conducted by assessing D2WFP following a scientifically-sound and comprehensive
process in different scenarios and the obtained results show an apparent
increase in the number of artefacts re-covered when adopting D2WFP which
outperform any current industry or opensource browsing forensics tools. The
second contribution of D2WFP is the robust formulation of artefact correlation
and cross-validation within D2WFP which enables digital forensics professionals
to better document and structure their analysis of host-based deep and dark web
browsing artefacts
A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection Frameworks
Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware’s unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection
Trends in Android Malware Detection
This paper analyzes different Android malware detection techniques from several research papers, some of these techniques are novel while others bring a new perspective to the research work done in the past. The techniques are of various kinds ranging from detection using host based frameworks and static analysis of executable to feature extraction and behavioral patterns. Each paper is reviewed extensively and the core features of each technique are highlighted and contrasted with the others. The challenges faced during the development of such techniques are also discussed along with the future prospects for Android malware detection. The findings of the review have been well documented in this paper to aid those making an effort to research in the area of Android malware detection by understanding the current scenario and developments that have happened in the field thus far
Forensic investigation of small-scale digital devices: a futuristic view
Small-scale digital devices like smartphones, smart toys, drones, gaming consoles, tablets, and other personal data assistants have now become ingrained constituents in our daily lives. These devices store massive amounts of data related to individual traits of users, their routine operations, medical histories, and financial information. At the same time, with continuously evolving technology, the diversity in operating systems, client storage localities, remote/cloud storages and backups, and encryption practices renders the forensic analysis task multi-faceted. This makes forensic investigators having to deal with an array of novel challenges. This study reviews the forensic frameworks and procedures used in investigating small-scale digital devices. While highlighting the challenges faced by digital forensics, we explore how cutting-edge technologies like Blockchain, Artificial Intelligence, Machine Learning, and Data Science may play a role in remedying concerns. The review aims to accumulate state-of-the-art and identify a futuristic approach for investigating SSDDs
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