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. To this end, this work proposes a feedback based adaptive, self-configurable, and resilient framework for the overlay network that cognitively adapts to the changes in the network to provide reliable connectivity between spatially dispersed smart devices. Our results show that if sufficient overlay devices are available, the framework leads to a connected configuration that ensures a high coverage of the mobile underlay network. Moreover, the framework can actively reconfigure itself in the event of varying levels of device failure.
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. We also provide detailed classification of attack models and demonstrate the feasibility of our techniques with case studies on assays implemented in typical DMFB hardware.
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. We first explore the properties of BadNets in a toy example, by creating a backdoored handwritten digit classifier. Next, we demonstrate backdoors in a more realistic scenario by creating a U.S. street sign classifier that identifies stop signs as speed limits when a special sticker is added to the stop sign; we then show in addition that the backdoor in our US street sign detector can persist even if the network is later retrained for another task and cause a drop in accuracy of 25% on average when the backdoor trigger is present.
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. Although genetic mutations in the reverse transcription that beget these phenotypes are random, the framework by which a phenotype become favored is affected by the environment and neighboring phenotypes. Moreover, the HIV disease has all components of an evolutionary process, including replication, mutation, and selection
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. In this letter, an ENF signal presence detection method is introduced for videos. The proposed method is based on multiple ENF signal estimations from steady superpixels, i.e., pixels that are most likely uniform in color, brightness, and texture, and intra-class similarity of the estimated signals. Subsequently, consistency among these estimates is then used to determine the presence or absence of an ENF signal in a given video. The proposed technique can operate on video clips as short as 2 min and is independent of the camera sensor type, i.e., CCD or CMOS.
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. We also design a linking technique that takes advantage of leakages from the Bitcoin mempool, blockchain and sex ad site, to link a subset of sex ads to Bitcoin public wallets and transactions. Finally, we demonstrate via a 4-week proof of concept using Backpage as the sex ad site, how an analyst can use these automated approaches to potentially find human traffickers.
August 15, 2017
Fei Chen, Gary Mac, and Nikhil Gupta
The additive manufacturing (AM) process chain relies heavily on cloud based resources and software programs that are connected to the internet. Cybersecurity has become a major concern for cloud based resources. While network security is important and is the responsibility of the information technology departments of corporations, a second line of defense is necessary if the cybersecurity is breached and the computer aided design (CAD) files are stolen. The stolen CAD files can be used to print components in exactly the same quality as the original component. The present work aims at developing design features in CAD models that can be used for the purpose of security against counterfeiting.
August 11, 2017
Yuan Liu, Jie Zhang, Quanyan Zhu and Xingwei Wang
In electronic commerce, buyers and sellers conduct transactions without physical interactions. In reputation systems, the trustworthiness of sellers is achieved by aggregating the ratings shared by other buyers with whom the sellers have ever conducted transactions. However, the ratings provided by buyers for evaluating the same seller could be diverse due to their different judgment criteria, which is referred as the subjectivity problem of reputation systems. It indicates that the ratings shared by some buyers may mislead other buyers with different personalities, making it challenging to aggregate the ratings properly in reputation systems. In this paper, in order to cope with the subjectivity problem, a hybrid architecture of reputation systems is proposed, which is based on coalition formation game theory. In the proposed module, buyers with the same subjectivity will automatically form a club, and share their ratings so as to build seller reputation within their club. The utility of a club is the profit created by the reputation system, which is further divided among the buyers of the club. Two utility allocation algorithms have been investigated, i.e., the proportional and Shapley allocations, respectively. Theoretical analysis and experimental results have shown that buyers with the same personality have the incentive to form a separate pure club if specific conditions are satisfied.
August 9, 2017
Samet Taspinar, Manoranjan Mohanty and Nasir Memon
Photo Response Non-Uniformity (PRNU) noisebased source attribution is a well known technique to verify the camera of an image or video. Researchers have proposed various countermeasures to prevent PRNU-based source camera attribution. Forced seam-carving is one such recently proposed counter forensics technique. This technique can disable PRNUbased source camera attribution by forcefully removing seams such that the size of most uncarved image blocks is less than 50 × 50 pixels. In this paper, we show that given multiple seamcarved images from the same camera, source attribution can still be possible even if the size of uncarved blocks in the image is less than the recommended size of 50 × 50 pixels. Theoretical analysis and experiments with multiple cameras demonstrate that the effectiveness of our scheme depends on the number of seams carved from an image and the randomness of the seam positions.
August 4, 2017
Randal Milch, Distinguished Fellow at the Center on Law and Security at NYU School of Law and the NYU Center for Cybersecurity, comments on the Internet of Things (IoT) Cybersecurity Improvements Act of 2017:
The bill seeks to use the federal government’s purchasing power to drive much-needed cybersecurity improvements in internet-connected devices. In addition, the bill would amend the Computer Fraud and Abuse Act and the Digital Millennium Copyright Act to encourage research on device vulnerabilities. These are important first steps in combating a large and growing menace from billions of poorly secured devices.
Large-Scale 3D Chips: Challenges and Solutions for Design Automation, Testing, and Trustworthy Integration
August 2, 2017
Johann Knechtel, Ozgur Sinanoglu, Ibrahim (Abe) M. Elfadel, Jens Lienig and Cliff C. N. Sze
Adaptive and Resilient Revenue Maximizing Resource Allocation and Pricing in Cloud Computing Environments
July 27, 2017
Muhammad Junaid Farooq, Quanyan Zhu
Cloud computing is becoming an essential component of modern computer and communication systems. The available resources at the cloud such as computing nodes, storage, databases, etc. are often packaged in the form of virtual machines (VMs) to be used by remotely located client applications for computational tasks. However, the cloud has a limited number of VMs available, which have to be efficiently utilized to generate higher productivity and subsequently generate maximum revenue. Client applications generate requests with computational tasks at random times with random complexity to be processed by the cloud. The cloud service provider (CSP) has to decide whether to allocate a VM to a task at hand or to wait for a higher complexity task in the future. We propose a threshold-based mechanism to optimally decide the allocation and pricing of VMs to sequentially arriving requests in order to maximize the revenue of the CSP over a finite time horizon. Moreover, we develop an adaptive and resilient framework based that can counter the effect of realtime changes in the number of available VMs at the cloud server, the frequency and nature of arriving tasks on the revenue of the CSP.
July 25, 2017
Jeffrey Pawlick, Thi Thu Hang Nguyen, Quanyan Zhu
Advanced persistent threats (APTs) are stealthy attacks which make use of social engineering and deception to give adversaries insider access to networked systems. Against APTs, active defense technologies aim to create and exploit information asymmetry for defenders. In this paper, we study a scenario in which a powerful defender uses honeypots for active defense in order to observe an attacker who has penetrated the network. Rather than immediately eject the attacker, the defender may elect to gather information. We introduce a Markov decision process on a continuous state space in order to model the defender’s problem. We find a threshold of information that the defender should gather about the attacker before ejecting him. Then we study the robustness of this policy using a Stackelberg game. Our results provide a quantitative foundation for studying optimal timing for attacker engagement in network defense.
July 25, 2017
Jeffrey Pawlick and Quanyan Zhu
Deception plays a critical role in many interactions in communication and network security. Game-theoretic models called “cheap talk signaling games” capture the dynamic and information asymmetric nature of deceptive interactions. But signaling games inherently model undetectable deception. In this paper, we investigate a model of signaling games in which the receiver can detect deception with some probability. This model nests traditional signaling games and complete information Stack- elberg games as special cases. We present the pure strategy perfect Bayesian Nash equilibria of the game. Then we illustrate these analytical results with an application to active network defense. The presence of evidence forces majority-truthful behavior and eliminates some pure strategy equilibria. It always benefits the deceived player, but surprisingly sometimes also benefits the deceiving player.
July 24, 2017
Hamza Anwar and Quanyan Zhu
Sensing in complex systems requires large-scale information exchange and on-the-go communications over heterogeneous networks and integrated processing platforms. Many networked cyber-physical systems exhibit hierarchical infrastructures of information flows, which naturally leads to a multi-level tree-like information structure in which each level corresponds to a particular scale of representation. This work focuses on the multiscale fusion of data collected at multiple levels of the system. We propose a multiscale state-space model to represent multi-resolution data over the hierarchical information system and formulate a multi-stage dynamic zero-sum game to design a multi-scale H∞ robust filter. We present numerical experiments for one and two-dimensional signals and provide a comparative analysis of the minimax filter with the standard Kalman filter to show the improvement in signal-to-noise ratio (SNR).
July 21, 2017
Juntao Chen, Corinne Touati, Quanyan Zhu
Infrastructure networks are vulnerable to both cyber and physical attacks. Building a secure and resilient networked system is essential for providing reliable and dependable services. To this end, we establish a two-player three-stage game framework to capture the dynamics in the infrastructure protection and recovery phases. Specifically, the goal of the infrastructure network designer is to keep the network connected before and after the attack, while the adversary aims to disconnect the network by compromising a set of links. With costs for creating and removing links, the two players aim to maximize their utilities while minimizing the costs. In this paper, we use the concept of subgame perfect equilibrium (SPE) to characterize the optimal strategies of the network defender and attacker. We derive the SPE explicitly in terms of system parameters. Finally, we use a case study of UAV-enabled communication networks for disaster recovery to corroborate the obtained analytical results.
July 21, 2017