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.
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.
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.
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) , 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
August 21, 2017
Bahareh Khazaei, Javad Salimi Sartakhti, Mohammad Hossein Manshaei, Quanyan Zhu, Mehdi Sadeghi and Seyed Rasoul Mousavi
Understanding the dynamics of human immunodeficiency virus (HIV) is essential for depicting, developing, and investigating effective treatment strategies. HIV infects several types of immune cells, but its main target is to destroy helper T-cells. In the lymph nodes, the infected T-cells interact with each other and their environment to obtain more resources. According to infectivity and replicative capacity of T-cells in the HIV infection process, they can be divided into four phenotypes.
August 17, 2017
Saffet Vatansever, Ahmet Emer Dirik and Nasir Memon
Electrical network frequency (ENF) instantaneously fluctuates around its nominal value (50/60 Hz) due to a continuous disparity between generated power and consumed power. Consequently, luminous intensity of a mains-powered light source varies depending on ENF fluctuations in the grid network. Variations in the luminance over time can be captured from video recordings and ENF can be estimated through content analysis of these recordings. In ENF-based video forensics, it is critical to check whether a given video file is appropriate for this type of analysis. That is, if ENF signal is not present in a given video, it would be useless to apply ENF-based forensic analysis.
August 17, 2017
Rebecca S. Portnoff, Danny Yuxing Huang, Periwinkle Doerfler, Sadia Afroz and Damon McCoy
Sites for online classified ads selling sex are widely used by human traffickers to support their pernicious business. The sheer quantity of ads makes manual exploration and analysis unscalable. In addition, discerning whether an ad is advertising a trafficked victim or a independent sex worker is a very difficult task. Very little concrete ground truth (i.e., ads definitively known to be posted by a trafficker) exists in this space. In this work, we develop tools and techniques that can be used separately and in conjunction to group sex ads by their true owner (and not the claimed author in the ad). Specifically, we develop a machine learning classifier that uses stylometry to distinguish between ads posted by the same vs. different authors with 96% accuracy.