I'd like to pay with your Visa Card : an illustration of illicit online trading activity in the underground economy

With the growing use and financial importance of the Internet, cyber criminals increasingly perceive computer systems, network architectures, and databases storing transaction- and personal-related data as assets and profitable targets. As illicit activities have become more organized and monetary-driven, a digital underground economy for hacking-related goods and services has evolved. In this paper, we outline the infrastructure and modes of operation of said economy with the help of real world samples captured in a communication channel on an IRC network. Thereby, we are able to gain a better understanding of the dynamics and interactions on this market

The Extreme Risk of Personal Data Breaches & The Erosion of Privacy

Personal data breaches from organisations, enabling mass identity fraud, constitute an \emph{extreme risk}. This risk worsens daily as an ever-growing amount of personal data are stored by organisations and on-line, and the attack surface surrounding this data becomes larger and harder to secure. Further, breached information is distributed and accumulates in the hands of cyber criminals, thus driving a cumulative erosion of privacy. Statistical modeling of breach data from 2000 through 2015 provides insights into this risk: A current maximum breach size of about 200 million is detected, and is expected to grow by fifty percent over the next five years. The breach sizes are found to be well modeled by an \emph{extremely heavy tailed} truncated Pareto distribution, with tail exponent parameter decreasing linearly from 0.57 in 2007 to 0.37 in 2015. With this current model, given a breach contains above fifty thousand items, there is a ten percent probability of exceeding ten million. A size effect is unearthed where both the frequency and severity of breaches scale with organisation size like $s^{0.6}$. Projections indicate that the total amount of breached information is expected to double from two to four billion items within the next five years, eclipsing the population of users of the Internet. This massive and uncontrolled dissemination of personal identities raises fundamental concerns about privacy.Comment: 16 pages, 3 sets of figures, and 4 table

Honeynet design and implementation

Over the past decade, webcriminality has become a real issue. Because they allow the botmasters to control hundreds to millions of machines, botnets became the first-choice attack platform for the network attackers, to launch distributed denial of service attacks, steal sensitive information and spend spam emails. This work aims at designing and implementing a honeynet, specific to IRC bots. Our system works in 3 phasis: (1) binaries collection, (2) simulation, and (3) activity capturing and monitoring. Our phase 2 simulation uses an IRC redirection to extract the connection information thanks to a IRC redirection (using a DNS redirection and a "fakeserver"). In phase 3, we use the information previously extracted to launch our honeyclient, which will capture and monitor the traffic on the C&C channel. Thanks to our honeynet, we create a database of the activity of IRC botnets (their connection characteristics, commands on the C&C ), and hope to learn more about their behavior and the underground market they create.M.S.Committee Chair: Wenke Lee; Committee Member: Jonathon Giffin; Committee Member: Mustaque Ahama

Characterizing eve: Analysing cybercrime actors in a large underground forum

Underground forums contain many thousands of active users, but the vast majority will be involved, at most, in minor levels of deviance. The number who engage in serious criminal activity is small. That being said, underground forums have played a significant role in several recent high-profile cybercrime activities. In this work we apply data science approaches to understand criminal pathways and characterize key actors related to illegal activity in one of the largest and longest- running underground forums. We combine the results of a logistic regression model with k-means clustering and social network analysis, verifying the findings using topic analysis. We identify variables relating to forum activity that predict the likelihood a user will become an actor of interest to law enforcement, and would therefore benefit the most from intervention. This work provides the first step towards identifying ways to deter the involvement of young people away from a career in cybercrime.Alan Turing Institut

Analysis of Credential Stealing Attacks in an Open Networked Environment

Abstract. This paper analyses the forensic data on credential stealing incidents over a period of 5 years across 5000 machines monitored at the National Center for Supercomputing Applications at the University of Illinois. The analysis conducted is the first attempt in an open operational environment (i) to evaluate the intricacies of carrying out SSH-based credential stealing attacks, (ii) to highlight and quantify key characteristics of such attacks, and (iii) to provide the system level characterization of such incidents in terms of distribution of alerts and incident consequences. Keywords-Incident analysis;Credential stealing; Intrusion detectio