The Amorphous Nature of Hackers: An Exploratory Study

In this work, we aim to better understand outsider perspectives of the hacker community through a series of situation based survey questions. By doing this, we hope to gain insight into the overall reputation of hackers from participants in a wide range of technical and non-technical backgrounds. This is important to digital forensics since convicted hackers will be tried by people, each with their own perception of who hackers are. Do cyber crimes and national security issues negatively affect people’s perceptions of hackers? Does hacktivism and information warfare positively affect people’s perception of hackers? Do individual personality factors affect one’s perception of hackers? To answer these questions in a systematic manner, we created two hypotheses. The first hypothesis tested participants’ response in 9 scenarios whereas the second hypothesis tested the participants’ response based on their scores on the Neuroticism-Extraversion-Openness Inventory (NEO) personality subscale. In brief, our results were indicative of how personality traits could influence perceptions of hackers and hacktivism. Possibilities for future research and implications for legal and criminal justice policy are discussed

CuFA: A More Formal Definition for Digital Forensic Artifacts

The term “artifact” currently does not have a formal definition within the domain of cyber/ digital forensics, resulting in a lack of standardized reporting, linguistic understanding between professionals, and efficiency. In this paper we propose a new definition based on a survey we conducted, literature usage, prior definitions of the word itself, and similarities with archival science. This definition includes required fields that all artifacts must have and encompasses the notion of curation. Thus, we propose using a new term e curated forensic artifact (CuFA) e to address items which have been cleared for entry into a CuFA database (one implementation, the Artifact Genome Project, abbreviated as AGP, is under development and briefly outlined). An ontological model encapsulates these required fields while utilizing a lower-level taxonomic schema. We use the Cyber Observable eXpression (CybOX) project due to its rising popularity and rigorous classifications of forensic objects. Additionally, we suggest some improvements on its integration into our model and identify higher-level location categories to illustrate tracing an object from creation through investigative leads. Finally, a step-wise procedure for researching and logging CuFAs is devised to accompany the model

Network and device forensic analysis of Android social-messaging applications

In this research we forensically acquire and analyze the device-stored data and network traffic of 20 popular instant messaging applications for Android. We were able to reconstruct some or the entire message content from 16 of the 20 applications tested, which reflects poorly on the security and privacy measures employed by these applications but may be construed positively for evidence collection purposes by digital forensic practitioners. This work shows which features of these instant messaging applications leave evidentiary traces allowing for suspect data to be reconstructed or partially reconstructed, and whether network forensics or device forensics permits the reconstruction of that activity. We show that in most cases we were able to reconstruct or intercept data such as: passwords, screenshots taken by applications, pictures, videos, audio sent, messages sent, sketches, profile pictures and more

If I Had a Million Cryptos: Cryptowallet Application Analysis and A Trojan Proof-of-Concept

Cryptocurrencies have gained wide adoption by enthusiasts and investors. In this work, we examine seven different Android cryptowallet applications for forensic artifacts, but we also assess their security against tampering and reverse engineering. Some of the biggest benefits of cryptocurrency is its security and relative anonymity. For this reason it is vital that wallet applications share the same properties. Our work, however, indicates that this is not the case. Five of the seven applications we tested do not implement basic security measures against reverse engineering. Three of the applications stored sensitive information, like wallet private keys, insecurely and one was able to be decrypted with some effort. One of the applications did not require root access to retrieve the data. We were also able to implement a proof-of-concept trojan which exemplifies how a malicious actor may exploit the lack of security in these applications and exfiltrate user data and cryptocurrency

User relationship classification of facebook messenger mobile data using WEKA

© Springer Nature Switzerland AG 2018. Mobile devices are a wealth of information about its user and their digital and physical activities (e.g. online browsing and physical location). Therefore, in any crime investigation artifacts obtained from a mobile device can be extremely crucial. However, the variety of mobile platforms, applications (apps) and the significant size of data compound existing challenges in forensic investigations. In this paper, we explore the potential of machine learning in mobile forensics, and specifically in the context of Facebook messenger artifact acquisition and analysis. Using Quick and Choo (2017)’s Digital Forensic Intelligence Analysis Cycle (DFIAC) as the guiding framework, we demonstrate how one can acquire Facebook messenger app artifacts from an Android device and an iOS device (the latter is, using existing forensic tools. Based on the acquired evidence, we create 199 data-instances to train WEKA classifiers (i.e. ZeroR, J48 and Random tree) with the aim of classifying the device owner’s contacts and determine their mutual relationship strength