Threshold-Dependent Camouflaged Cells to Secure Circuits Against Reverse Engineering Attacks

May 2, 2016

Maria I. Mera Collantes, Mohamed El Massad, and Siddharth Garg

With current tools and technology, someone who has physical access to a chip can extract the detailed layout of the integrated circuit (IC). By using advanced visual imaging techniques, reverse engineering can reveal details that are meant to be kept secret, such as a secure protocol or novel implementation that offers a competitive advantage.

The Cybersecurity Landscape in Industrial Control Systems

May 1, 2016

Stephen McLaughlin, Charalambos Konstantinou, Xueyang Wang, Lucas Davi, Ahmad-Reza Sadeghi, Michail Maniatakos, and Ramesh Karri

Industrial control systems (ICSs) are transitioning from legacy-electromechanical-based systems to modern information and communication technology (ICT)-based systems creating a close coupling between cyber and physical components. In this paper, we explore the ICS cybersecurity landscape including: 1) the key principles and unique aspects of ICS operation; 2) a brief history of cyberattacks on ICS; 3) an overview of ICS security assessment; 4) a survey of “uniquely-ICS” testbeds that capture the interactions between the various layers of an ICS; and 5) current trends in ICS attacks and defenses.

Compliance signaling games: toward modeling the deterrence of insider threats

April 28, 2016

William Casey, Jose Andre Morales, Evan Wright, Quanyan Zhu, Bud Mishra

The authors form a signaling game model to address the controllable risks acting within an organization whether they are expressed from malicious, unwitting, or benign insiders who are trusted to operate within an organization.

Educating Tomorrow’s Lawyers to Handle New Digital Problems

April 18, 2016


Zachary K. Goldman

Tomorrow’s lawyers—today’s law students—need to be better equipped to understand the underlying technical systems that will push the law in new directions. In no area of law is this dynamic more apparent than cyber security.

Characterizing Long-tail SEO Spam on Cloud Web Hosting Services

April 11, 2016

Xiaojing Liao, Chang Liu, Damon McCoy, Elaine Shi, Shuang Hao. Raheem Beyah

In this paper, the authors take the first step toward understanding how long-tail SEO spam is implemented on cloud hosting platforms.

Building trustworthy systems using untrusted components: A High-level synthesis approach

April 11, 2016

Jeyavijayan (JV) Rajendran, Ozgur Sinanoglu, and Ramesh Karri

Trustworthiness of system-on-chip designs is undermined by malicious logic (Trojans) in third-party intellectual properties (3PIPs). In this paper, duplication, diversity, and isolation principles have been extended to detect build trustworthy systems using untrusted, potentially Trojan-infected 3PIPs.

Stress Testing the Booters: Understanding and Undermining the Business of DDoS Services

April 11, 2016

Mohammad Karami, Youngsam Park, and Damon McCoy

DDoS-for-hire services, also known as booters, have commoditized DDoS attacks and enabled abusive subscribers of these services to cheaply extort, harass and intimidate businesses and people by taking them offline. However, due to the underground nature of these booters, little is known about their underlying technical and business structure.

Hardware Performance Counter-Based Malware Identification and Detection with Adaptive Compressive Sensing

April 1, 2016

Xueyang Wang, Sek Chai , Michael Isnardi , Sehoon Lim , and Ramesh Karri

Hardware Performance Counter-based (HPC) runtime checking is an effective way to identify malicious behaviors of malware and detect malicious modifications to a legitimate program’s control flow. To reduce the overhead in the monitored system which has limited storage and computing resources, we present a “sample-locally-analyze-remotely” technique. The sampled HPC data are sent to a remote server for further analysis. To minimize the I/O bandwidth required for transmission, the fine-grained HPC profiles are compressed into much smaller vectors with Compressive Sensing. The experimental results demonstrate an 80% I/O bandwidth reduction after applying Compressive Sensing, without compromising the detection and identification capabilities.

Finding Sensitive Accounts on Twitter: An Automated Approach Based on Follower Anonymity

March 31, 2016

Sai Teja Peddinti, Keith W. Ross, and Justin Cappos

We explore the feasibility of automatically finding accounts that publish sensitive content on Twitter, by examining the percentage of anonymous and identifiable followers the accounts have. We first designed a machine learning classifier to automatically determine if a Twitter account is anonymous or identifiable. We then classified an account as potentially sensitive based on the percentages of anonymous and identifiable followers the account has. We applied our approach to approximately 100,000 accounts with 404 million active followers. The approach uncovered accounts that were sensitive for a diverse number of reasons.

Security verification of 3rd party intellectual property cores for information leakage

March 17, 2016

Jeyavijayan Rajendran, A Dhandayuthapany, Ramesh Karri, V Vedula

Globalization of the system-on-chip (SoC) design flow has created opportunities for rogue intellectual property (IP) vendors to insert malicious circuits (a.k.a. hardware Trojans) into their IPs. We propose to formally verify third party IPs (3PIPs) for unauthorized information leakage. We validate our technique using Trojan benchmarks from the Trust-Hub.

Diplomat: Using Delegations to Protect Community Repositories

March 16, 2016

Trishank Karthik Kuppusamy, Santiago Torres-Arias, Vladimir Diaz, and Justin Cappos

The authors demonstrate that community repositories can offer compromise-resilience and real-time project registration by employing mechanisms that disambiguate trust delegations.

Dynamic Privacy For Distributed Machine Learning Over Network

March 9, 2016

Tao Zhang and Quanyan Zhu

Privacy-preserving distributed machine learning becomes increasingly important due to the recent rapid growth of data. This paper focuses on a class of regularized empirical risk minimization (ERM) machine learning problems, and develops two methods to provide differential privacy to distributed learning algorithms over a network.

Characterizing user behaviors in location-based find-and-flirt services: Anonymity and demographics

February 26, 2016

Minhui Xue, Limin Yang, 
Keith W. Ross, and Haifeng Qian

In this paper, we explore: (i) if one gender tends to use the People Nearby service more than another; (ii) if users of People Nearby are more anonymous than ordinary WeChat users; (iii) if ordinary WeChat users are more anonymous than Twitter users. We also take an in-depth examination of the user anonymity and demographics in a combined fashion and examine: (iv) if ordinary WeChat females are more anonymous than ordinary males; (v) if People Nearby females are more anonymous than People Nearby males.

Interdependent Network Formation Games

February 24, 2016

Juntao Chen and Quanyan Zhu

Designing optimal interdependent networks is important for the robustness and efficiency of national critical infrastructures. Here, we establish a two-person game-theoretic model in which two network designers choose to maximize the global connectivity independently. This framework enables decentralized network design by using iterative algorithms.

Do You See What I See? Differential Treatment of Anonymous Users

February 23, 2016

Sheharbano Khattak, David Fifield, Sadia Afroz, Mobin Javed, Srikanth Sundaresan, Vern Paxson, Steven J. Murdoch, and Damon McCoy

The utility of anonymous communication is undermined by a growing number of websites treating users of such services in a degraded fashion…We conduct the first study to methodically enumerate and characterize the treatment of anonymous users as second-class Web citizens in the context of Tor.

Understanding Craigslist Rental Scams

February 22, 2016

Youngsam Park, Damon McCoy, and Elaine Shi

Fraudulently posted online rental listings, rental scams, have been frequently reported by users. However, our understanding of the structure of rental scams is limited. In this paper, we conduct the first systematic empirical study of online rental scams on Craigslist. This study is enabled by a suite of techniques that allowed us to identify scam campaigns and our automated system that is able to collect additional information by conversing with scammers.

Systems, Processes and Computer-Accessible Medium for Providing Logic Encryption Utilizing Fault Analysis

February 18, 2016

Ozgur Sinanoglu, Youngok Pino, Jeyavijayan Rajendran, and Ramesh Karri

Exemplary systems, methods and computer-accessible mediums can encrypting a circuit by determining at least one location to insert at least one gate in the circuit using a fault analysis, and inserting the at least one gate in at least one section of the at least one location. The determination can include an iterative procedure that can be a greedy iterative procedure. The determination can be based on an effect of the particular location on a maximum number of outputs of the circuit.

Law Enforcement Online: Innovative Doesn’t Mean Illegal

February 16, 2016


Judith H. Germano

Criminal actors have an increasing ability to commit serious crimes remotely via computers, while concealing their identity and location through the use of various means, including Tor hidden service protocols. To effectively identify and apprehend these criminals, law enforcement must be nimble and technologically savvy, and must employ regularly updated investigative tools.