Fifteen Minutes of Unwanted Fame: Detecting and Characterizing Doxing

November 3, 2017

Peter Snyder, Periwinkle Doerfler, Chris Kanich and Damon McCoy.

Doxing is online abuse where a malicious party attempts to harm another by releasing identifying or sensitive information. Motivations for doxing include personal, competitive, and political reasons, and web users of all ages, genders and internet experience have been targeted. Existing research on doxing is primarily qualitative. This work improves our understanding of doxing by being the first to take a quantitative approach. We do so by designing and deploying a tool which can detect dox files and measure the frequency, content, targets, and effects of doxing occurring on popular dox-posting sites.

Provably-Secure Logic Locking: From Theory To Practice

November 1, 2017

Muhammad Yasin, Abhrajit Sengupta, Mohammed Thari Nabeel, Mohammed Ashraf, Jeyavijayan (JV) Rajendran and Ozgur Sinanoglu

Logic locking has been conceived as a promising proactive defense strategy against intellectual property (IP) piracy, counterfeiting, hardware Trojans, reverse engineering, and overbuilding attacks. Yet, various attacks that use a working chip as an oracle have been launched on logic locking to successfully retrieve its secret key, undermining the defense of all existing locking techniques. In this paper, we propose stripped-functionality logic locking (SFLL), which strips some of the functionality of the design and hides it in the form of a secret key(s), thereby rendering on-chip implementation functionally different from the original one.

A Large-Scale Markov Game Approach to Dynamic Protection of Interdependent Infrastructure Networks

October 28, 2017

Linan Huang, Juntao Chen, and Quanyan Zhu

The integration of modern information and communication technologies (ICTs) into critical infrastructures (CIs) improves its connectivity and functionalities yet also brings cyber threats. It is thus essential to understand the risk of ICTs on CIs holistically as a cyberphysical system and design efficient security hardening mechanisms. To this end, we capture the system behaviors of the CIs under malicious attacks and the protection strategies by a zero-sum game. We further propose a computationally tractable approximation for large-scale networks which builds on the factored graph that exploits the dependency structure of the nodes of CIs and the approximate dynamic programming tools for stochastic Markov games.

Reverse Engineering Camouflaged Sequential Circuits Without Scan Access

October 28, 2017

Mohamed El Massad, Siddharth Garg and Mahesh Tripunitara.

Integrated circuit (IC) camouflaging is a promising technique to protect the design of a chip from reverse engineering. However, recent work has shown that even camouflaged ICs can be reverse engineered from the observed input/output behaviour of a chip using SAT solvers. However, these so-called SAT attacks have so far targeted only camouflaged combinational circuits. For camouflaged sequential circuits, the SAT attack requires that the internal state of the circuit is controllable and observable via the scan chain. It has been implicitly assumed that restricting scan chain access increases the security of camouflaged ICs from reverse engineering attacks.

DPFEE: A High Performance Scalable Pre-processor for Network Security Systems

October 23, 2017

Vinayaka Jyothi, Sateesh K. Addepalli and Ramesh Karri

Network Intrusion Detection Systems (NIDS) and Anti-Denial-of-Service (DoS) employ Deep Packet Inspection (DPI) which provides visibility to the content of payload to detect network attacks. All DPI engines assume a pre-processing step that extracts the various protocol-specific fields. However, application layer (L7) field extraction is computationally expensive. We propose a novel Deep Packet Field Extraction Engine (DPFEE) for application layer field extraction to hardware. DPFEE is a content-aware, grammar-based, Layer 7 programmable field extraction engine for text-based protocols.

Game-Theoretic Design of Secure and Resilient Distributed Support Vector Machines with Adversaries

October 12, 2017

Rui Zhang and Quanyan Zhu

With a large number of sensors and control units in networked systems, distributed support vector machines (DSVMs) play a fundamental role in scalable and efficient multisensor classification and prediction tasks. However, DSVMs are vulnerable to adversaries who can modify and generate data to deceive the system to misclassification and misprediction. This work aims to design defense strategies for DSVM learner against a potential adversary. We establish a game-theoretic framework to capture the conflicting interests between the DSVM learne r and the attacker.

An Information Theoretic Framework for Active De-anonymization in Social Networks Based on Group Memberships

October 11, 2017

Farhad Shirani, Siddharth Garg, and Elza Erkip

In this paper, a new mathematical formulation for the problem of de-anonymizing social network users by actively querying their membership in social network groups is introduced. In this formulation, the attacker has access to a noisy observation of the group membership of each user in the social network. When an unidentified victim visits a malicious website, the attacker uses browser history sniffing to make queries regarding the victim’s social media activity. Particularly, it can make polar queries regarding the victim’s group memberships and the victim’s identity. The attacker receives noisy responses to her queries. The goal is to de-anonymize the victim with the minimum number of queries.

Rethinking Split Manufacturing: An Information-Theoretic Approach with Secure Layout Techniques

October 5, 2017

Abhrajit Sengupta, Satwik Patnaik, Johann Knechtel, Mohammed Ashraf, Siddharth Garg and Ozgur Sinanoglu

Split manufacturing is a promising technique to defend against fab-based malicious activities such as IP piracy, overbuilding, and insertion of hardware Trojans. However, a network flow-based proximity attack, proposed by Wang et al. (DAC’16) [1], has demonstrated that most prior art on split manufacturing is highly vulnerable. Here in this work, we present two practical layout techniques towards secure split manufacturing: (i) gate-level graph coloring and (ii) clustering of same-type gates. Our approach shows promising results against the advanced proximity attack, lowering its success rate by 5.27x, 3.19x, and 1.73x on average compared to the unprotected layouts when splitting at metal layers M1, M2, and M3, respectively.

Manipulating Adversary’s Belief: A Dynamic Game Approach to Deception by Design for Proactive Network Security

October 4, 2017

Karel Horák, Quanyan Zhu and Branislav Bošanský.

Due to the sophisticated nature of current computer systems, traditional defense measures, such as firewalls, malware scanners, and intrusion detection/prevention systems, have been found inadequate. These technological systems suffer from the fact that a sophisticated attacker can study them, identify their weaknesses and thus get an advantage over the defender. To prevent this from happening a proactive cyber defense is a new defense mechanism in which we strategically engage the attacker by using cyber deception techniques, and we influence his actions by creating and reinforcing his view of the computer system.

Dynamics of Strategic Protection Against Virus Propagation in Heterogeneous Complex Networks

October 4, 2017

Yezekael Hayel and Quanyan Zhu

With an increasing number of wide-spreading cyber-attacks on networks such as the recent WannaCry and Petya Ransomware, protection against malware and virus spreading in large scale networks is essential to provide security to network systems. In this paper, we consider a network protection game in which heterogeneous agents decide their individual protection levels against virus propagation over complex networks. Each agent has his own private type which characterizes his recovery rate, transmission capabilities, and perceived cost. We propose an evolutionary Poisson game framework to model the heterogeneous interactions of the agents over a complex network and analyze the equilibrium strategies for decentralized protection.

Strategic Defense Against Deceptive Civilian GPS Spoofing of Unmanned Aerial Vehicles

October 4, 2017

Tao Zhang and Quanyan Zhu

The Global Positioning System (GPS) is commonly used in civilian Unmanned Aerial Vehicles (UAVs) to provide geolocation and time information for navigation. However, GPS is vulnerable to many intentional threats such as the GPS signal spoofing, where an attacker can deceive a GPS receiver by broadcasting incorrect GPS signals. Defense against such attacks is critical to ensure the reliability and security of UAVs. In this work, we propose a signaling game framework in which the GPS receiver can strategically infer the true location when the attacker attempts to mislead it with a fraudulent and purposefully crafted signal.

Smoke Screener or Straight Shooter: Detecting Elite Sybil Attacks in User-Review Social Networks

September 20, 2017

Haizhong Zheng, Minhui Xue, Hao Lu, Shuang Hao, Haojin Zhu, Xiaohui Liang and Keith Ross.

Popular User-Review Social Networks (URSNs)-such as Dianping, Yelp, and Amazon-are often the targets of reputation attacks in which fake reviews are posted in order to boost or diminish the ratings of listed products and services. These attacks often emanate from a collection of accounts, called Sybils, which are collectively managed by a group of real users. A new advanced scheme, which we term elite Sybil attacks, recruits organically highly-rated accounts to generate seemingly-trustworthy and realistic-looking reviews. These elite Sybil accounts taken together form a large-scale sparsely-knit Sybil network for which existing Sybil fake-review defense systems are unlikely to succeed.

Linking Amplification DDoS Attacks to Booter Services

September 20, 2017

Johannes Krupp, Mohammad Karami, Christian Rossow, Damon McCoy and Michael Backes

We present techniques for attributing amplification DDoS attacks to the booter services that launched the attack. Our k-Nearest Neighbor (k-NN) classification algorithm is based on features that are characteristic for a DDoS service, such as the set of reflectors used by that service. This allows us to attribute DDoS attacks based on observations from honeypot amplifiers, augmented with training data from ground truth attack-to-services mappings we generated by subscribing to DDoS services and attacking ourselves in a controlled environment.

Enabling Extreme Energy Efficiency Via Timing Speculation for Deep Neural Network Accelerators

September 10, 2017

Jeff (Jun) Zhang, Zahra Ghodsi, Kartheek Rangineni and Siddharth Garg

Due to the success of deep neural networks (DNN) in achieving and surpassing state-of-the-art results for a range of machine learning applications, there is growing interest in the design of high-performance hardware accelerators for DNN execution. Further, as DNN hardware accelerators are increasingly being deployed in datacenters, accelerator power and energy efficiency have become key design metrics. In this paper, we seek to enhance the energy efficiency of high-performance systolic array based DNN accelerators, like the recently released Google TPU, using voltage underscaling based timing speculation, a powerful energy reduction technique that enables digital logic to execute below its nominal supply voltage.

Cognitive Connectivity Resilience in Multi-layer Remotely Deployed Mobile Internet of Things

September 2, 2017

Muhammad Junaid Farooq and Quanyan Zhu

Enabling the Internet of things in remote areas without traditional communication infrastructure requires a multi-layer network architecture. The devices in the overlay network are required to provide coverage to the underlay devices as well as to remain connected to other overlay devices. The coordination, planning, and design of such two-layer heterogeneous networks is an important problem to address. Moreover, the mobility of the nodes and their vulnerability to adversaries pose new challenges to the connectivity. For instance, the connectivity of devices can be affected by changes in the network, e.g., the mobility of the underlay devices or the unavailability of overlay devices due to failure or adversarial attacks.

Secure Randomized Checkpointing for Digital Microfluidic Biochips

August 31, 2017

Jack Tang, Mohamed Ibrahim,Krishnendu Chakrabarty and Ramesh Karri

Digital microfluidic biochips (DMFBs) integrated with processors and arrays of sensors form cyberphysical systems and consequently face a variety of unique, recently described security threats. It has been noted that techniques used for error recovery can provide some assurance of integrity when a cyberphysical DMFB is under attack. This work proposes the use of such hardware for security purposes through the randomization of checkpoints in both space and time, and provides design guidelines for designers of such systems. We define security metrics and present techniques for improving performance through static checkpoint maps, and describe performance trade-offs associated with static and random checkpoints.

BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain

August 22, 2017

Tianyu Gu, Brendan Dolan-Gavitt and Siddharth Garg

Deep learning-based techniques have achieved stateof-the-art performance on a wide variety of recognition and classification tasks. However, these networks are typically computationally expensive to train, requiring weeks of computation on many GPUs; as a result, many users outsource the training procedure to the cloud or rely on pre-trained models that are then fine-tuned for a specific task. In this paper we show that outsourced training introduces new security risks: an adversary can create a maliciously trained network (a backdoored neural network, or a BadNet) that has state-of-theart performance on the user’s training and validation samples, but behaves badly on specific attacker-chosen inputs.

A network framework for dynamic models of urban food, energy and water systems (FEWS)

August 22, 2017

Rae Zimmerman, Quanyan Zhu and Carolyn Dimitri

The urban food system addressed here centers on urban food processing, distribution and consumption (including food packaging and waste disposal) and as such addresses how food moves from processing and distribution centers to points of consumption and ultimately waste disposal within cities. The Food-Energy-Water Systems (FEWS) Nexus extends to and through urban boundaries. Energy and water resource use are vital along these routes and are interdependent with one another and with food processing in ways that differ from those in agricultural production systems outside urban boundaries. This paper addresses how the urban food system affects the intensity of energy and water resource use and how these interdependencies can be altered by abrupt changes or extreme events.