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Yu, Xiaodong, Feng, Wu-chun, Yao, Danfeng(Daphne), Becchi, Michela.  2016.  O3FA: A Scalable Finite Automata-based Pattern-Matching Engine for Out-of-Order Deep Packet Inspection. Proceedings of the 2016 Symposium on Architectures for Networking and Communications Systems. :1–11.

To match the signatures of malicious traffic across packet boundaries, network-intrusion detection (and prevention) systems (NIDS) typically perform pattern matching after flow reassembly or packet reordering. However, this may lead to the need for large packet buffers, making detection vulnerable to denial-of-service (DoS) attacks, whereby attackers exhaust the buffer capacity by sending long sequences of out-of-order packets. While researchers have proposed solutions for exact-match patterns, regular-expression matching on out-of-order packets is still an open problem. Specifically, a key challenge is the matching of complex sub-patterns (such as repetitions of wildcards matched at the boundary between packets). Our proposed approach leverages the insight that various segments matching the same repetitive sub-pattern are logically equivalent to the regular-expression matching engine, and thus, inter-changing them would not affect the final result. In this paper, we present O3FA, a new finite automata-based, deep packet-inspection engine to perform regular-expression matching on out-of-order packets without requiring flow reassembly. O3FA consists of a deterministic finite automaton (FA) coupled with a set of prefix-/suffix-FA, which allows processing out-of-order packets on the fly. We present our design, optimization, and evaluation for the O3FA engine. Our experiments show that our design requires 20x-4000x less buffer space than conventional buffering-and-reassembling schemes on various datasets and that it can process packets in real-time, i.e., without reassembly.

Xu, H., Hu, L., Liu, P., Xiao, Y., Wang, W., Dayal, J., Wang, Q., Tang, Y..  2018.  Oases: An Online Scalable Spam Detection System for Social Networks. 2018 IEEE 11th International Conference on Cloud Computing (CLOUD). :98–105.
Web-based social networks enable new community-based opportunities for participants to engage, share their thoughts, and interact with each other. Theses related activities such as searching and advertising are threatened by spammers, content polluters, and malware disseminators. We propose a scalable spam detection system, termed Oases, for uncovering social spam in social networks using an online and scalable approach. The novelty of our design lies in two key components: (1) a decentralized DHT-based tree overlay deployment for harvesting and uncovering deceptive spam from social communities; and (2) a progressive aggregation tree for aggregating the properties of these spam posts for creating new spam classifiers to actively filter out new spam. We design and implement the prototype of Oases and discuss the design considerations of the proposed approach. Our large-scale experiments using real-world Twitter data demonstrate scalability, attractive load-balancing, and graceful efficiency in online spam detection for social networks.
Brakerski, Zvika, Vaikuntanathan, Vinod, Wee, Hoeteck, Wichs, Daniel.  2016.  Obfuscating Conjunctions Under Entropic Ring LWE. Proceedings of the 2016 ACM Conference on Innovations in Theoretical Computer Science. :147–156.
We show how to securely obfuscate conjunctions, which are functions f(x1,...,xn) = ∧i∈I yi where I ⊆ [n] and each literal yi is either just xi or ¬ xi e.g., f(xi,...,x\_n) = xi ⊆ ¬ x3 ⊆ ¬ x7 ... ⊆ x\\textbackslashvphantom\n-1. Whereas prior work of Brakerski and Rothblum (CRYPTO 2013) showed how to achieve this using a non-standard object called cryptographic multilinear maps, our scheme is based on an "entropic" variant of the Ring Learning with Errors (Ring LWE) assumption. As our core tool, we prove that hardness assumptions on the recent multilinear map construction of Gentry, Gorbunov and Halevi (TCC 2015) can be established based on entropic Ring LWE. We view this as a first step towards proving the security of additional mutlilinear map based constructions, and in particular program obfuscators, under standard assumptions. Our scheme satisfies virtual black box (VBB) security, meaning that the obfuscated program reveals nothing more than black-box access to f as an oracle, at least as long as (essentially) the conjunction is chosen from a distribution having sufficient entropy.
Su, Fang-Hsiang, Bell, Jonathan, Kaiser, Gail, Ray, Baishakhi.  2018.  Obfuscation Resilient Search Through Executable Classification. Proceedings of the 2Nd ACM SIGPLAN International Workshop on Machine Learning and Programming Languages. :20-30.

Android applications are usually obfuscated before release, making it difficult to analyze them for malware presence or intellectual property violations. Obfuscators might hide the true intent of code by renaming variables and/or modifying program structures. It is challenging to search for executables relevant to an obfuscated application for developers to analyze efficiently. Prior approaches toward obfuscation resilient search have relied on certain structural parts of apps remaining as landmarks, un-touched by obfuscation. For instance, some prior approaches have assumed that the structural relationships between identifiers are not broken by obfuscators; others have assumed that control flow graphs maintain their structures. Both approaches can be easily defeated by a motivated obfuscator. We present a new approach, MACNETO, to search for programs relevant to obfuscated executables leveraging deep learning and principal components on instructions. MACNETO makes few assumptions about the kinds of modifications that an obfuscator might perform. We show that it has high search precision for executables obfuscated by a state-of-the-art obfuscator that changes control flow. Further, we also demonstrate the potential of MACNETO to help developers understand executables, where MACNETO infers keywords (which are from relevant un-obfuscated programs) for obfuscated executables.

Yao, X., Zhou, X., Ma, J..  2015.  Object event visibility for anti-counterfeiting in RFID-enabled product supply chains. 2015 Science and Information Conference (SAI). :141–150.

RFID-enabled product supply chain visibility is usually implemented by building up a view of the product history of its activities starting from manufacturing or even earlier with a dynamically updated e-pedigree for track-and-trace, which is examined and authenticated at each node of the supply chain for data consistence with the pre-defined one. However, while effectively reducing the risk of fakes, this visibility can't guarantee that the product is authentic without taking further security measures. To the best of our knowledge, this requires deeper understandings on associations of object events with the counterfeiting activities, which is unfortunately left blank. In this paper, the taxonomy of counterfeiting possibilities is initially developed and analyzed, the structure of EPC-based events is then re-examined, and an object-centric coding mechanism is proposed to construct the object-based event “pedigree” for such event exception detection and inference. On this basis, the system architecture framework to achieve the objectivity of object event visibility for anti-counterfeiting is presented, which is also applicable to other aspects of supply chain management.

Wang, Wenhao, Xu, Xiaoyang, Hamlen, Kevin W..  2017.  Object Flow Integrity. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. :1909–1924.
Object flow integrity (OFI) augments control-flow integrity (CFI) and software fault isolation (SFI) protections with secure, first-class support for binary object exchange across inter-module trust boundaries. This extends both source-aware and source-free CFI and SFI technologies to a large class of previously unsupported software: those containing immutable system modules with large, object-oriented APIs—which are particularly common in component-based, event-driven consumer software. It also helps to protect these inter-module object exchanges against confused deputy-assisted vtable corruption and counterfeit object-oriented programming attacks. A prototype implementation for Microsoft Component Object Model demonstrates that OFI is scalable to large interfaces on the order of tens of thousands of methods, and exhibits low overheads of under 1% for some common-case applications. Significant elements of the implementation are synthesized automatically through a principled design inspired by type-based contracts.
Shahriar, Hossain, Haddad, Hisham.  2016.  Object Injection Vulnerability Discovery Based on Latent Semantic Indexing. Proceedings of the 31st Annual ACM Symposium on Applied Computing. :801–807.

Object Injection Vulnerability (OIV) is an emerging threat for web applications. It involves accepting external inputs during deserialization operation and use the inputs for sensitive operations such as file access, modification, and deletion. The challenge is the automation of the detection process. When the application size is large, it becomes hard to perform traditional approaches such as data flow analysis. Recent approaches fall short of narrowing down the list of source files to aid developers in discovering OIV and the flexibility to check for the presence of OIV through various known APIs. In this work, we address these limitations by exploring a concept borrowed from the information retrieval domain called Latent Semantic Indexing (LSI) to discover OIV. The approach analyzes application source code and builds an initial term document matrix which is then transformed systematically using singular value decomposition to reduce the search space. The approach identifies a small set of documents (source files) that are likely responsible for OIVs. We apply the LSI concept to three open source PHP applications that have been reported to contain OIVs. Our initial evaluation results suggest that the proposed LSI-based approach can identify OIVs and identify new vulnerabilities.

Woon Cho, Abidi, M.A., Kyungwon Jeong, Nahyun Kim, Seungwon Lee, Joonki Paik, Gwang-Gook Lee.  2014.  Object retrieval using scene normalized human model for video surveillance system. Consumer Electronics (ISCE 2014), The 18th IEEE International Symposium on. :1-2.

This paper presents a human model-based feature extraction method for a video surveillance retrieval system. The proposed method extracts, from a normalized scene, object features such as height, speed, and representative color using a simple human model based on multiple-ellipse. Experimental results show that the proposed system can effectively track moving routes of people such as a missing child, an absconder, and a suspect after events.

Nathi, Rohan A., Sutar, Dimpal.  2019.  Object Secured TCP Socket for Remote Monitoring IoT Devices. 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1–5.

With evolution of the communication technology remote monitoring has rooted into many applications. Swift innovation in Internet of Things (IoT) technology led to development of electronics embedded devices capable of sensing into the remote location and transferring the data through internet across the globe. Such devices transfers the sensitive data, which are susceptible to security attacks by the intruder and network hacker. Paper studies the existing security solutions and limitations for IoT environment and provides a pragmatic lightweight security scheme on Transmission Control Protocol (TCP) network for Remote Monitoring System devices over internet. This security scheme will aid Original Equipment Manufacturer (OEM) developing massive IoT products for remote monitoring. Real time evaluation of this scheme has been analyzed.

Alimadadi, Mohammadreza, Stojanovic, Milica, Closas, Pau.  2017.  Object Tracking Using Modified Lossy Extended Kalman Filter. Proceedings of the International Conference on Underwater Networks & Systems. :7:1–7:5.

We address the problem of object tracking in an underwater acoustic sensor network in which distributed nodes measure the strength of field generated by moving objects, encode the measurements into digital data packets, and transmit the packets to a fusion center in a random access manner. We allow for imperfect communication links, where information packets may be lost due to noise and collisions. The packets that are received correctly are used to estimate the objects' trajectories by employing an extended Kalman Filter, where provisions are made to accommodate a randomly changing number of obseravtions in each iteration. An adaptive rate control scheme is additionally applied to instruct the sensor nodes on how to adjust their transmission rate so as to improve the location estimation accuracy and the energy efficiency of the system. By focusing explicitly on the objects' locations, rather than working with a pre-specified grid of potential locations, we resolve the spatial quantization issues associated with sparse identification methods. Finally, we extend the method to address the possibility of objects entering and departing the observation area, thus improving the scalability of the system and relaxing the requirement for accurate knowledge of the objects' initial locations. Performance is analyzed in terms of the mean-squared localization error and the trade-offs imposed by the limited communication bandwidth.

Storteboom, Sarah, Thudt, Alice, Knudsen, Søren, Carpendale, Sheelagh.  2017.  Objective Meaning: Presentation Mediation in an Interactive Installation. Proceedings of the 2017 ACM International Conference on Interactive Surfaces and Spaces. :360–365.

We explore the presentation technique of visual abstraction as a form of mediation to manage content generated by the public in order to maintain a respectful discourse. We identify technological and social mediation as two dimensions within the space of content mediation, and discuss different solutions based on related work in public interactive displays and art installations. We further discuss a novel approach to technological mediation by describing our interactive artwork Objective Meaning - an installation that invites the audience to express themselves through anonymous text messages. The design of this system mediates discourse by visually abstracting the presentation of messages on a display by breaking messages apart into decontextualized words. We briefly discuss the public response during a one-month deployment of the installation in a library setting.

Jang, Uyeong, Wu, Xi, Jha, Somesh.  2017.  Objective Metrics and Gradient Descent Algorithms for Adversarial Examples in Machine Learning. Proceedings of the 33rd Annual Computer Security Applications Conference. :262–277.
Fueled by massive amounts of data, models produced by machine-learning (ML) algorithms are being used in diverse domains where security is a concern, such as, automotive systems, finance, health-care, computer vision, speech recognition, natural-language processing, and malware detection. Of particular concern is use of ML in cyberphysical systems, such as driver-less cars and aviation, where the presence of an adversary can cause serious consequences. In this paper we focus on attacks caused by adversarial samples, which are inputs crafted by adding small, often imperceptible, perturbations to force a ML model to misclassify. We present a simple gradient-descent based algorithm for finding adversarial samples, which performs well in comparison to existing algorithms. The second issue that this paper tackles is that of metrics. We present a novel metric based on few computer-vision algorithms for measuring the quality of adversarial samples.
Xia, Haijun.  2016.  Object-Oriented Interaction: Enabling Direct Physical Manipulation of Abstract Content via Objectification. Proceedings of the 29th Annual Symposium on User Interface Software and Technology. :13–16.

Touch input promises intuitive interactions with digital content as it employs our experience of manipulating physical objects: digital content can be rotated, scaled, and translated using direct manipulation gestures. However, the reliance on analog also confines the scope of direct physical manipulation: the physical world provides no mechanism to interact with digital abstract content. As such, applications on touchscreen devices either only include limited functionalities or fallback on the traditional form-filling paradigm, which is tedious, slow, and error prone for touch input. My research focuses on designing a new UI framework to enable complex functionalities on touch screen devices by expanding direct physical manipulation to abstract content via objectification. I present two research projects, objectification of attributes and selection, which demonstrate considerable promises.

Farulla, G. A., Pane, A. J., Prinetto, P., Varriale, A..  2017.  An object-oriented open software architecture for security applications. 2017 IEEE East-West Design Test Symposium (EWDTS). :1–6.

This paper introduces a newly developed Object-Oriented Open Software Architecture designed for supporting security applications, while leveraging on the capabilities offered by dedicated Open Hardware devices. Specifically, we target the SEcube™ platform, an Open Hardware security platform based on a 3D SiP (System on Package) designed and produced by Blu5 Group. The platform integrates three components employed for security in a single package: a Cortex-M4 CPU, a FPGA and an EAL5+ certified Smart Card. The Open Software Architecture targets both the host machine and the security device, together with the secure communication among them. To maximize its usability, this architecture is organized in several abstraction layers, ranging from hardware interfaces to device drivers, from security APIs to advanced applications, like secure messaging and data protection. We aim at releasing a multi-platform Open Source security framework, where software and hardware cooperate to hide to both the developer and the final users classical security concepts like cryptographic algorithms and keys, focusing, instead, on common operational security concepts like groups and policies.

Shinya, A., Tung, N. D., Harada, T., Thawonmas, R..  2017.  Object-Specific Style Transfer Based on Feature Map Selection Using CNNs. 2017 Nicograph International (NicoInt). :88–88.
We propose a method for transferring an arbitrary style to only a specific object in an image. Style transfer is the process of combining the content of an image and the style of another image into a new image. Our results show that the proposed method can realize style transfer to specific object.
Okada, Kazuya, Hazeyama, Hiroaki, Kadobayashi, Youki.  2014.  Oblivious DDoS Mitigation with Locator/ID Separation Protocol. Proceedings of The Ninth International Conference on Future Internet Technologies. :8:1–8:6.

The need to keep an attacker oblivious of an attack mitigation effort is a very important component of a defense against denial of services (DoS) and distributed denial of services (DDoS) attacks because it helps to dissuade attackers from changing their attack patterns. Conceptually, DDoS mitigation can be achieved by two components. The first is a decoy server that provides a service function or receives attack traffic as a substitute for a legitimate server. The second is a decoy network that restricts attack traffic to the peripheries of a network, or which reroutes attack traffic to decoy servers. In this paper, we propose the use of a two-stage map table extension Locator/ID Separation Protocol (LISP) to realize a decoy network. We also describe and demonstrate how LISP can be used to implement an oblivious DDoS mitigation mechanism by adding a simple extension on the LISP MapServer. Together with decoy servers, this method can terminate DDoS traffic on the ingress end of an LISP-enabled network. We verified the effectiveness of our proposed mechanism through simulated DDoS attacks on a simple network topology. Our evaluation results indicate that the mechanism could be activated within a few seconds, and that the attack traffic can be terminated without incurring overhead on the MapServer.

Tran, Muoi, Luu, Loi, Kang, Min Suk, Bentov, Iddo, Saxena, Prateek.  2018.  Obscuro: A Bitcoin Mixer Using Trusted Execution Environments. Proceedings of the 34th Annual Computer Security Applications Conference. :692–701.
Bitcoin provides only pseudo-anonymous transactions, which can be exploited to link payers and payees – defeating the goal of anonymous payments. To thwart such attacks, several Bitcoin mixers have been proposed, with the objective of providing unlinkability between payers and payees. However, existing Bitcoin mixers can be regarded as either insecure or inefficient. We present Obscuro, a highly efficient and secure Bitcoin mixer that utilizes trusted execution environments (TEEs). With the TEE's confidentiality and integrity guarantees for code and data, our mixer design ensures the correct mixing operations and the protection of sensitive data (i.e., private keys and mixing logs), ruling out coin theft and address linking attacks by a malicious service provider. Yet, the TEE-based implementation does not prevent the manipulation of inputs (e.g., deposit submissions, blockchain feeds) to the mixer, hence Obscuro is designed to overcome such limitations: it (1) offers an indirect deposit mechanism to prevent a malicious service provider from rejecting benign user deposits; and (2) scrutinizes blockchain feeds to prevent deposits from being mixed more than once (thus degrading anonymity) while being eclipsed from the main blockchain branch. In addition, Obscuro provides several unique anonymity features (e.g., minimum mixing set size guarantee, resistant to dropping user deposits) that are not available in existing centralized and decentralized mixers. Our prototype of Obscuro is built using Intel SGX and we demonstrate its effectiveness in Bitcoin Testnet. Our implementation mixes 1000 inputs in just 6.49 seconds, which vastly outperforms all of the existing decentralized mixers.
Dey, L., Mahajan, D., Gupta, H..  2014.  Obtaining Technology Insights from Large and Heterogeneous Document Collections. Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2014 IEEE/WIC/ACM International Joint Conferences on. 1:102-109.

Keeping up with rapid advances in research in various fields of Engineering and Technology is a challenging task. Decision makers including academics, program managers, venture capital investors, industry leaders and funding agencies not only need to be abreast of latest developments but also be able to assess the effect of growth in certain areas on their core business. Though analyst agencies like Gartner, McKinsey etc. Provide such reports for some areas, thought leaders of all organisations still need to amass data from heterogeneous collections like research publications, analyst reports, patent applications, competitor information etc. To help them finalize their own strategies. Text mining and data analytics researchers have been looking at integrating statistics, text analytics and information visualization to aid the process of retrieval and analytics. In this paper, we present our work on automated topical analysis and insight generation from large heterogeneous text collections of publications and patents. While most of the earlier work in this area provides search-based platforms, ours is an integrated platform for search and analysis. We have presented several methods and techniques that help in analysis and better comprehension of search results. We have also presented methods for generating insights about emerging and popular trends in research along with contextual differences between academic research and patenting profiles. We also present novel techniques to present topic evolution that helps users understand how a particular area has evolved over time.

Lin Chen, Lu Zhou, Chunxue Liu, Quan Sun, Xiaobo Lu.  2014.  Occlusive vehicle tracking via processing blocks in Markov random field. Progress in Informatics and Computing (PIC), 2014 International Conference on. :294-298.

The technology of vehicle video detecting and tracking has been playing an important role in the ITS (Intelligent Transportation Systems) field during recent years. The occlusion phenomenon among vehicles is one of the most difficult problems related to vehicle tracking. In order to handle occlusion, this paper proposes an effective solution that applied Markov Random Field (MRF) to the traffic images. The contour of the vehicle is firstly detected by using background subtraction, then numbers of blocks with vehicle's texture and motion information are filled inside each vehicle. We extract several kinds of information of each block to process the following tracking. As for each occlusive block two groups of clique functions in MRF model are defined, which represents spatial correlation and motion coherence respectively. By calculating each occlusive block's total energy function, we finally solve the attribution problem of occlusive blocks. The experimental results show that our method can handle occlusion problems effectively and track each vehicle continuously.

Isaacson, D. M..  2018.  The ODNI-OUSD(I) Xpress Challenge: An Experimental Application of Artificial Intelligence Techniques to National Security Decision Support. 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC). :104-109.
Current methods for producing and disseminating analytic products contribute to the latency of relaying actionable information and analysis to the U.S. Intelligence Community's (IC's) principal customers, U.S. policymakers and warfighters. To circumvent these methods, which can often serve as a bottleneck, we report on the results of a public prize challenge that explored the potential for artificial intelligence techniques to generate useful analytic products. The challenge tasked solvers to develop algorithms capable of searching and processing nearly 15,000 unstructured text files into a 1-2 page analytic product without human intervention; these analytic products were subsequently evaluated and scored using established IC methodologies and criteria. Experimental results from this challenge demonstrate the promise for the ma-chine generation of analytic products to ensure that the IC warns and informs in a more timely fashion.
Chakraborty, K., Saha, G..  2016.  Off-line voltage security assessment of power transmission systems using UVSI through artificial neural network. 2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI). :158–162.

Coming days are becoming a much challenging task for the power system researchers due to the anomalous increase in the load demand with the existing system. As a result there exists a discordant between the transmission and generation framework which is severely pressurizing the power utilities. In this paper a quick and efficient methodology has been proposed to identify the most sensitive or susceptible regions in any power system network. The technique used in this paper comprises of correlation of a multi-bus power system network to an equivalent two-bus network along with the application of Artificial neural network(ANN) Architecture with training algorithm for online monitoring of voltage security of the system under all multiple exigencies which makes it more flexible. A fast voltage stability indicator has been proposed known as Unified Voltage Stability Indicator (UVSI) which is used as a substratal apparatus for the assessment of the voltage collapse point in a IEEE 30-bus power system in combination with the Feed Forward Neural Network (FFNN) to establish the accuracy of the status of the system for different contingency configurations.

Hossain, M., Xie, J..  2018.  Off-sensing and Route Manipulation Attack: A Cross-Layer Attack in Cognitive Radio based Wireless Mesh Networks. IEEE INFOCOM 2018 - IEEE Conference on Computer Communications. :1376–1384.
Cognitive Radio (CR) has garnered much attention in the last decade, while the security issues are not fully studied yet. Existing research on attacks and defenses in CR - based networks focuses mostly on individual network layers, whereas cross-layer attacks remain fortified against single-layer defenses. In this paper, we shed light on a new vulnerability in cross-layer routing protocols and demonstrate how a perpetrator can exploit this vulnerability to manipulate traffic flow around it. We propose this cross-layer attack in CR-based wireless mesh networks (CR-WMNs), which we call off-sensing and route manipulation (OS-RM) attack. In this cross-layer assault, off-sensing attack is launched at the lower layers as the point of attack but the final intention is to manipulate traffic flow around the perpetrator. We also introduce a learning strategy for a perpetrator, so that it can gather information from the collaboration with other network entities and capitalize this information into knowledge to accelerate its malice intentions. Simulation results show that this attack is far more detrimental than what we have experienced in the past and need to be addressed before commercialization of CR-based networks.
Islam, Mohammad A., Ren, Shaolei.  2018.  Ohm's Law in Data Centers: A Voltage Side Channel for Timing Power Attacks. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. :146-162.

Maliciously-injected power load, a.k.a. power attack, has recently surfaced as a new egregious attack vector for dangerously compromising the data center availability. This paper focuses on the emerging threat of power attacks in a multi-tenant colocation data center, an important type of data center where multiple tenants house their own servers and share the power distribution system. Concretely, we discover a novel physical side channel –- a voltage side channel –- which leaks the benign tenants' power usage information at runtime and helps an attacker precisely time its power attacks. The key idea we exploit is that, due to the Ohm's Law, the high-frequency switching operation (40\textasciitilde100kHz) of the power factor correction circuit universally built in today's server power supply units creates voltage ripples in the data center power lines. Importantly, without overlapping the grid voltage in the frequency domain, the voltage ripple signals can be easily sensed by the attacker to track the benign tenants' runtime power usage and precisely time its power attacks. We evaluate the timing accuracy of the voltage side channel in a real data center prototype, demonstrating that the attacker can extract benign tenants' power pattern with a great accuracy (correlation coefficient = 0.90+) and utilize 64% of all the attack opportunities without launching attacks randomly or consecutively. Finally, we highlight a few possible defense strategies and extend our study to more complex three-phase power distribution systems used in large multi-tenant data centers.

Compagno, Alberto, Conti, Mauro, Droms, Ralph.  2016.  OnboardICNg: A Secure Protocol for On-boarding IoT Devices in ICN. Proceedings of the 3rd ACM Conference on Information-Centric Networking. :166–175.

Information-Centric Networking (ICN) is an emerging networking paradigm that focuses on content distribution and aims at replacing the current IP stack. Implementations of ICN have demonstrated its advantages over IP, in terms of network performance and resource requirements. Because of these advantages, ICN is also considered to be a good network paradigm candidate for the Internet-of-Things (IoT), especially in scenarios involving resource constrained devices. In this paper we propose OnboardICNg, the first secure protocol for on-boarding (authenticating and authorizing) IoT devices in ICN mesh networks. OnboardICNg can securely onboard resource constrained devices into an existing IoT network, outperforming the authentication protocol selected for the ZigBee-IP specification: EAP-PANA, i.e., the Protocol for carrying Authentication for Network Access (PANA) combined with the Extensible Authentication Protocol (EAP). In particular we show that, compared with EAP-PANA, OnboardICNg reduces the communication and energy consumption, by 87% and 66%, respectively.

Gundabolu, S., Wang, X..  2018.  On-chip Data Security Against Untrustworthy Software and Hardware IPs in Embedded Systems. 2018 IEEE Computer Society Annual Symposium on VLSI (ISVLSI). :644–649.

State-of-the-art system-on-chip (SoC) field programmable gate arrays (FPGAs) integrate hard powerful ARM processor cores and the reconfigurable logic fabric on a single chip in addition to many commonly needed high performance and high-bandwidth peripherals. The increasing reliance on untrustworthy third-party IP (3PIP) cores, including both hardware and software in FPGA-based embedded systems has made the latter increasingly vulnerable to security attacks. Detection of trojans in 3PIPs is extremely difficult to current static detection methods since there is no golden reference model for 3PIPs. Moreover, many FPGA-based embedded systems do not have the support of security services typically found in operating systems. In this paper, we present our run-time, low-cost, and low-latency hardware and software based solution for protecting data stored in on-chip memory blocks, which has attracted little research attention. The implemented memory protection design consists of a hierarchical top-down structure and controls memory access from software IPs running on the processor and hardware IPs running in the FPGA, based on a set of rules or access rights configurable at run time. Additionally, virtual addressing and encryption of data for each memory help protect confidentiality of data in case of a failure of the memory protection unit, making it hard for the attacker to gain access to the data stored in the memory. The design is implemented and tested on the Intel (Altera) DE1-SoC board featuring a SoC FPGA that integrates a dual-core ARM processor with reconfigurable logic and hundreds of memory blocks. The experimental results and case studies show that the protection model is successful in eliminating malicious IPs from the system without need for reconfiguration of the FPGA. It prevents unauthorized accesses from untrusted IPs, while arbitrating access from trusted IPs generating legal memory requests, without incurring a serious area or latency penalty.