Student research highlight: Secure and resilient distributed machine learning under adversarial environments
June 17, 2016
Rui Zhang and Quanyan Zhu
Machine learning algorithms, such as support vector machines (SVMs), neutral networks, and decision trees (DTs) have been widely used in data processing for estimation and detection. They can be used to classify samples based on a model built from training data. However, under the assumption that training and testing samples come from the same natural distribution, an attacker who can generate or modify training data will lead to misclassification or misestimation.
June 2, 2016
This talk will cover various forms of threats that the electronic chip supply chain is up against, as well as defenses against these threats. The talk will elucidate the development of CAD algorithms/tools for this newly emerging field by mostly leveraging principles from other more mature research domains.
June 1, 2016
Srikanth Sundaresan, Damon McCoy, Sadia Afroz, and Vern Paxson
Online underground forums serve a key role in facilitating information exchange and commerce between gray market or even cybercriminal actors. In order to streamline
bilateral communication to complete sales, merchants often publicly post their IM contact details, such as their Skype handle. Merchants that publicly post their Skype handle
potentially leak information, since Skype has a known protocol flaw that reveals the IP address(es) of a user when they are online.
May 25, 2016
Fei Miao, Quanyan Zhu, Miroslav Pajic, and George J. Pappas
This paper considers a method of coding the sensor outputs in order to detect stealthy false data injection attacks.
May 11, 2016
Steven Eric Zeltmann, Nikhil Gupta, Nektarios Georgios Tsoutsos, Michail Maniatakos, Jeyavijayan Rajendran, and Ramesh Karri
As the manufacturing time, quality, and cost associated with additive manufacturing (AM) continue to improve, more and more businesses and consumers are adopting this technology. Some of the key benefits of AM include customizing products, localizing production and reducing logistics. Due to these and numerous other benefits, AM is enabling a globally distributed manufacturing process and supply chain spanning multiple parties, and hence raises concerns about the reliability of the manufactured product. In this work, we first present a brief overview of the potential risks that exist in the cyber-physical environment of additive manufacturing.
May 3, 2016
Arun Kanuparthi, Jeyavijayan Rajendran, Ramesh Karri
In this paper, the authors propose Dynamic Sequence Checker (DSC), a framework to verify the validity of control flow between basic blocks in the program
May 3, 2016
Muhammad Yasin, Bodhisatwa Mazumdar, Jeyavijayan J V Rajendran, and Ozgur Sinanoglu
Logic locking is an Intellectual Property (IP) protection technique that thwarts IP piracy, hardware Trojans, reverse engineering, and IC overproduction. Researchers have taken multiple attempts in breaking logic locking techniques and recovering its secret key. A Boolean Satisfiability (SAT) based attack has been recently presented that breaks all the existing combinational logic locking techniques.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.