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Paul, S., Padhy, N. P., Mishra, S. K., Srivastava, A. K..  2019.  UUCA: Utility-User Cooperative Algorithm for Flexible Load Scheduling in Distribution System. 2019 8th International Conference on Power Systems (ICPS). :1—6.
Demand response analysis in smart grid deployment substantiated itself as an important research area in recent few years. Two-way communication between utility and users makes peak load reduction feasible by delaying the operation of deferrable appliances. Flexible appliance rescheduling is preferred to the users compared to traditional load curtailment. Again, if users' preferences are accounted into appliance transferring process, then customers concede a little discomfort to help the utility in peak reduction. This paper presents a novel Utility-User Cooperative Algorithm (UUCA) to lower total electricity cost and gross peak demand while preserving users' privacy and preferences. Main driving force in UUCA to motivate the consumers is a new cost function for their flexible appliances. As a result, utility will experience low peak and due to electricity cost decrement, users will get reduced bill. However, to maintain privacy, the behaviors of one customer have not be revealed either to other customers or to the central utility. To justify the effectiveness, UUCA is executed separately on residential, commercial and industrial customers of a distribution grid. Harmony search optimization technique has proved itself superior compared to other heuristic search techniques to prove efficacy of UUCA.
Aydeger, Abdullah, Saputro, Nico, Akkaya, Kemal.  2018.  Utilizing NFV for Effective Moving Target Defense Against Link Flooding Reconnaissance Attacks. MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM). :946—951.

Moving target defense (MTD) is becoming popular with the advancements in Software Defined Networking (SDN) technologies. With centralized management through SDN, changing the network attributes such as routes to escape from attacks is simple and fast. Yet, the available alternate routes are bounded by the network topology, and a persistent attacker that continuously perform the reconnaissance can extract the whole link-map of the network. To address this issue, we propose to use virtual shadow networks (VSNs) by applying Network Function Virtualization (NFV) abilities to the network in order to deceive attacker with the fake topology information and not reveal the actual network topology and characteristics. We design this approach under a formal framework for Internet Service Provider (ISP) networks and apply it to the recently emerged indirect DDoS attacks, namely Crossfire, for evaluation. The results show that attacker spends more time to figure out the network behavior while the costs on the defender and network operations are negligible until reaching a certain network size.

Kemp, C., Calvert, C., Khoshgoftaar, T..  2018.  Utilizing Netflow Data to Detect Slow Read Attacks. 2018 IEEE International Conference on Information Reuse and Integration (IRI). :108–116.
Attackers can leverage several techniques to compromise computer networks, ranging from sophisticated malware to DDoS (Distributed Denial of Service) attacks that target the application layer. Application layer DDoS attacks, such as Slow Read, are implemented with just enough traffic to tie up CPU or memory resources causing web and application servers to go offline. Such attacks can mimic legitimate network requests making them difficult to detect. They also utilize less volume than traditional DDoS attacks. These low volume attack methods can often go undetected by network security solutions until it is too late. In this paper, we explore the use of machine learners for detecting Slow Read DDoS attacks on web servers at the application layer. Our approach uses a generated dataset based upon Netflow data collected at the application layer on a live network environment. Our Netflow data uses the IP Flow Information Export (IPFIX) standard providing significant flexibility and features. These Netflow features can process and handle a growing amount of traffic and have worked well in our previous DDoS work detecting evasion techniques. Our generated dataset consists of real-world network data collected from a production network. We use eight different classifiers to build Slow Read attack detection models. Our wide selection of learners provides us with a more comprehensive analysis of Slow Read detection models. Experimental results show that the machine learners were quite successful in identifying the Slow Read attacks with a high detection and low false alarm rate. The experiment demonstrates that our chosen Netflow features are discriminative enough to detect such attacks accurately.
Manning, F.J., Mitropoulos, F.J..  2014.  Utilizing Attack Graphs to Measure the Efficacy of Security Frameworks across Multiple Applications. System Sciences (HICSS), 2014 47th Hawaii International Conference on. :4915-4920.

One of the primary challenges when developing or implementing a security framework for any particular environment is determining the efficacy of the implementation. Does the implementation address all of the potential vulnerabilities in the environment, or are there still unaddressed issues? Further, if there is a choice between two frameworks, what objective measure can be used to compare the frameworks? To address these questions, we propose utilizing a technique of attack graph analysis to map the attack surface of the environment and identify the most likely avenues of attack. We show that with this technique we can quantify the baseline state of an application and compare that to the attack surface after implementation of a security framework, while simultaneously allowing for comparison between frameworks in the same environment or a single framework across multiple applications.

Gursoy, Mehmet Emre, Liu, Ling, Truex, Stacey, Yu, Lei, Wei, Wenqi.  2018.  Utility-Aware Synthesis of Differentially Private and Attack-Resilient Location Traces. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. :196-211.
As mobile devices and location-based services become increasingly ubiquitous, the privacy of mobile users' location traces continues to be a major concern. Traditional privacy solutions rely on perturbing each position in a user's trace and replacing it with a fake location. However, recent studies have shown that such point-based perturbation of locations is susceptible to inference attacks and suffers from serious utility losses, because it disregards the moving trajectory and continuity in full location traces. In this paper, we argue that privacy-preserving synthesis of complete location traces can be an effective solution to this problem. We present AdaTrace, a scalable location trace synthesizer with three novel features: provable statistical privacy, deterministic attack resilience, and strong utility preservation. AdaTrace builds a generative model from a given set of real traces through a four-phase synthesis process consisting of feature extraction, synopsis learning, privacy and utility preserving noise injection, and generation of differentially private synthetic location traces. The output traces crafted by AdaTrace preserve utility-critical information existing in real traces, and are robust against known location trace attacks. We validate the effectiveness of AdaTrace by comparing it with three state of the art approaches (ngram, DPT, and SGLT) using real location trace datasets (Geolife and Taxi) as well as a simulated dataset of 50,000 vehicles in Oldenburg, Germany. AdaTrace offers up to 3-fold improvement in trajectory utility, and is orders of magnitude faster than previous work, while preserving differential privacy and attack resilience.
Fang, Yi, Godavarthy, Archana, Lu, Haibing.  2016.  A Utility Maximization Framework for Privacy Preservation of User Generated Content. Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval. :281–290.

The prodigious amount of user-generated content continues to grow at an enormous rate. While it greatly facilitates the flow of information and ideas among people and communities, it may pose great threat to our individual privacy. In this paper, we demonstrate that the private traits of individuals can be inferred from user-generated content by using text classification techniques. Specifically, we study three private attributes on Twitter users: religion, political leaning, and marital status. The ground truth labels of the private traits can be readily collected from the Twitter bio field. Based on the tweets posted by the users and their corresponding bios, we show that text classification yields a high accuracy of identification of these personal attributes, which poses a great privacy risk on user-generated content. We further propose a constrained utility maximization framework for preserving user privacy. The goal is to maximize the utility of data when modifying the user-generated content, while degrading the prediction performance of the adversary. The KL divergence is minimized between the prior knowledge about the private attribute and the posterior probability after seeing the user-generated data. Based on this proposed framework, we investigate several specific data sanitization operations for privacy preservation: add, delete, or replace words in the tweets. We derive the exact transformation of the data under each operation. The experiments demonstrate the effectiveness of the proposed framework.

Mercadier, Darius, Dagand, Pierre-Évariste.  2019.  Usuba: High-Throughput and Constant-Time Ciphers, by Construction. Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation. :157–173.
Cryptographic primitives are subject to diverging imperatives. Functional correctness and auditability pushes for the use of a high-level programming language. Performance and the threat of timing attacks push for using no more abstract than an assembler to exploit (or avoid!) the micro-architectural features of a given machine. We believe that a suitable programming language can reconcile both views and actually improve on the state of the art of both. Usuba is an opinionated dataflow programming language in which block ciphers become so simple as to be ``obviously correct'' and whose types document and enforce valid parallelization strategies at the granularity of individual bits. Its optimizing compiler, Usubac, produces high-throughput, constant-time implementations performing on par with hand-tuned reference implementations. The cornerstone of our approach is a systematization and generalization of bitslicing, an implementation trick frequently used by cryptographers.
Shockley, Matt, Maixner, Chris, Johnson, Ryan, DeRidder, Mitch, Petullo, W. Michael.  2017.  Using VisorFlow to Control Information Flow Without Modifying the Operating System Kernel or Its Userspace. Proceedings of the 2017 International Workshop on Managing Insider Security Threats. :13–24.

VisorFlow aims to monitor the flow of information between processes without requiring modifications to the operating system kernel or its userspace. VisorFlow runs in a privileged Xen domain and monitors the system calls executing in other domains running either Linux or Windows. VisorFlow uses its observations to prevent confidential information from leaving a local network. We describe the design and implementation of VisorFlow, describe how we used VisorFlow to confine na\"ıve users and malicious insiders during the 2017 Cyber-Defense Exercise, and provide performance measurements. We have released VisorFlow and its companion library, libguestrace, as open-source software.

Resch, S., Paulitsch, M..  2017.  Using TLA+ in the Development of a Safety-Critical Fault-Tolerant Middleware. 2017 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW). :146–152.

Creating and implementing fault-tolerant distributed algorithms is a challenging task in highly safety-critical industries. Using formal methods supports design and development of complex algorithms. However, formal methods are often perceived as an unjustifiable overhead. This paper presents the experience and insights when using TLA+ and PlusCal to model and develop fault-tolerant and safety-critical modules for TAS Control Platform, a platform for railway control applications up to safety integrity level (SIL) 4. We show how formal methods helped us improve the correctness of the algorithms, improved development efficiency and how part of the gap between model and implementation has been closed by translation to C code. Additionally, we describe how we gained trust in the formal model and tools by following a specific design process called property-driven design, which also implicitly addresses software quality metrics such as code coverage metrics.

Mhamdi, L., Njima, C. B., Dhouibi, H., Hassani, M..  2017.  Using timed automata and fuzzy logic for diagnosis of multiple faults in DES. 2017 International Conference on Control, Automation and Diagnosis (ICCAD). :457–463.

This paper proposes a design method of a support tool for detection and diagnosis of failures in discrete event systems (DES). The design of this diagnoser goes through three phases: an identification phase and finding paths and temporal parameters of the model describing the two modes of normal and faulty operation, a detection phase provided by the comparison and monitoring time operation and a location phase based on the combination of the temporal evolution of the parameters and thresholds exceeded technique. Our contribution lays in the application of this technique in the presence of faults arising simultaneously, sensors and actuators. The validation of the proposed approach is illustrated in a filling system through a simulation.

Djemaiel, Yacine, Fessi, Boutheina A., Boudriga, Noureddine.  2019.  Using Temporal Conceptual Graphs and Neural Networks for Big Data-Based Attack Scenarios Reconstruction. 2019 IEEE Intl Conf on Parallel Distributed Processing with Applications, Big Data Cloud Computing, Sustainable Computing Communications, Social Computing Networking (ISPA/BDCloud/SocialCom/SustainCom). :991–998.
The emergence of novel technologies and high speed networks has enabled a continually generation of huge volumes of data that should be stored and processed. These big data have allowed the emergence of new forms of complex attacks whose resolution represents a big challenge. Different methods and tools are developed to deal with this issue but definite detection is still needed since various features are not considered and tracing back an attack remains a timely activity. In this context, we propose an investigation framework that allows the reconstruction of complex attack scenarios based on huge volume of data. This framework used a temporal conceptual graph to represent the big data and the dependency between them in addition to the tracing back of the whole attack scenario. The selection of the most probable attack scenario is assisted by a developed decision model based on hybrid neural network that enables the real time classification of the possible attack scenarios using RBF networks and the convergence to the most potential attack scenario within the support of an Elman network. The efficiency of the proposed framework has been illustrated for the global attack reconstruction process targeting a smart city where a set of available services are involved.
Le, T. A., Baydin, A. G., Zinkov, R., Wood, F..  2017.  Using synthetic data to train neural networks is model-based reasoning. 2017 International Joint Conference on Neural Networks (IJCNN). :3514–3521.
We draw a formal connection between using synthetic training data to optimize neural network parameters and approximate, Bayesian, model-based reasoning. In particular, training a neural network using synthetic data can be viewed as learning a proposal distribution generator for approximate inference in the synthetic-data generative model. We demonstrate this connection in a recognition task where we develop a novel Captcha-breaking architecture and train it using synthetic data, demonstrating both state-of-the-art performance and a way of computing task-specific posterior uncertainty. Using a neural network trained this way, we also demonstrate successful breaking of real-world Captchas currently used by Facebook and Wikipedia. Reasoning from these empirical results and drawing connections with Bayesian modeling, we discuss the robustness of synthetic data results and suggest important considerations for ensuring good neural network generalization when training with synthetic data.
Al-Janabi, Mohammed, Quincey, Ed de, Andras, Peter.  2017.  Using Supervised Machine Learning Algorithms to Detect Suspicious URLs in Online Social Networks. Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. :1104–1111.

The increasing volume of malicious content in social networks requires automated methods to detect and eliminate such content. This paper describes a supervised machine learning classification model that has been built to detect the distribution of malicious content in online social networks (ONSs). Multisource features have been used to detect social network posts that contain malicious Uniform Resource Locators (URLs). These URLs could direct users to websites that contain malicious content, drive-by download attacks, phishing, spam, and scams. For the data collection stage, the Twitter streaming application programming interface (API) was used and VirusTotal was used for labelling the dataset. A random forest classification model was used with a combination of features derived from a range of sources. The random forest model without any tuning and feature selection produced a recall value of 0.89. After further investigation and applying parameter tuning and feature selection methods, however, we were able to improve the classifier performance to 0.92 in recall.

Greenstadt, Rachel.  2017.  Using Stylometry to Attribute Programmers and Writers. Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security. :91–91.

In this talk, I will discuss my lab's work in the emerging field of adversarial stylometry and machine learning. Machine learning algorithms are increasingly being used in security and privacy domains, in areas that go beyond intrusion or spam detection. For example, in digital forensics, questions often arise about the authors of documents: their identity, demographic background, and whether they can be linked to other documents. The field of stylometry uses linguistic features and machine learning techniques to answer these questions. We have applied stylometry to difficult domains such as underground hacker forums, open source projects (code), and tweets. I will discuss our Doppelgnger Finder algorithm, which enables us to group Sybil accounts on underground forums and detect blogs from Twitter feeds and reddit comments. In addition, I will discuss our work attributing unknown source code and binaries.

Halba, Khalid, Griffor, Edward, Kamongi, Patrick, Roth, Thomas.  2019.  Using Statistical Methods and Co-Simulation to Evaluate ADS-Equipped Vehicle Trustworthiness. 2019 Electric Vehicles International Conference (EV). :1–5.

With the increasing interest in studying Automated Driving System (ADS)-equipped vehicles through simulation, there is a growing need for comprehensive and agile middleware to provide novel Virtual Analysis (VA) functions of ADS-equipped vehicles towards enabling a reliable representation for pre-deployment test. The National Institute of Standards and Technology (NIST) Universal Cyber-physical systems Environment for Federation (UCEF) is such a VA environment. It provides Application Programming Interfaces (APIs) capable of ensuring synchronized interactions across multiple simulation platforms such as LabVIEW, OMNeT++, Ricardo IGNITE, and Internet of Things (IoT) platforms. UCEF can aid engineers and researchers in understanding the impact of different constraints associated with complex cyber-physical systems (CPS). In this work UCEF is used to produce a simulated Operational Domain Design (ODD) for ADS-equipped vehicles where control (drive cycle/speed pattern), sensing (obstacle detection, traffic signs and lights), and threats (unusual signals, hacked sources) are represented as UCEF federates to simulate a drive cycle and to feed it to vehicle dynamics simulators (e.g. OpenModelica or Ricardo IGNITE) through the Functional Mock-up Interface (FMI). In this way we can subject the vehicle to a wide range of scenarios, collect data on the resulting interactions, and analyze those interactions using metrics to understand trustworthiness impact. Trustworthiness is defined here as in the NIST Framework for Cyber-Physical Systems, and is comprised of system reliability, resiliency, safety, security, and privacy. The goal of this work is to provide an example of an experimental design strategy using Fractional Factorial Design for statistically assessing the most important safety metrics in ADS-equipped vehicles.

Cabaj, K., Mazurczyk, W..  2016.  Using Software-Defined Networking for Ransomware Mitigation: The Case of CryptoWall. IEEE Network. 30:14–20.

Currently, different forms of ransomware are increasingly threatening Internet users. Modern ransomware encrypts important user data, and it is only possible to recover it once a ransom has been paid. In this article we show how software-defined networking can be utilized to improve ransomware mitigation. In more detail, we analyze the behavior of popular ransomware - CryptoWall - and, based on this knowledge, propose two real-time mitigation methods. Then we describe the design of an SDN-based system, implemented using OpenFlow, that facilitates a timely reaction to this threat, and is a crucial factor in the case of crypto ransomware. What is important is that such a design does not significantly affect overall network performance. Experimental results confirm that the proposed approach is feasible and efficient.

Chong, T., Anu, V., Sultana, K. Z..  2019.  Using Software Metrics for Predicting Vulnerable Code-Components: A Study on Java and Python Open Source Projects. 2019 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). :98–103.

Software vulnerabilities often remain hidden until an attacker exploits the weak/insecure code. Therefore, testing the software from a vulnerability discovery perspective becomes challenging for developers if they do not inspect their code thoroughly (which is time-consuming). We propose that vulnerability prediction using certain software metrics can support the testing process by identifying vulnerable code-components (e.g., functions, classes, etc.). Once a code-component is predicted as vulnerable, the developers can focus their testing efforts on it, thereby avoiding the time/effort required for testing the entire application. The current paper presents a study that compares how software metrics perform as vulnerability predictors for software projects developed in two different languages (Java vs Python). The goal of this research is to analyze the vulnerability prediction performance of software metrics for different programming languages. We designed and conducted experiments on security vulnerabilities reported for three Java projects (Apache Tomcat 6, Tomcat 7, Apache CXF) and two Python projects (Django and Keystone). In this paper, we focus on a specific type of code component: Functions. We apply Machine Learning models for predicting vulnerable functions. Overall results show that software metrics-based vulnerability prediction is more useful for Java projects than Python projects (i.e., software metrics when used as features were able to predict Java vulnerable functions with a higher recall and precision compared to Python vulnerable functions prediction).

Schmid, Stefan, Arquint, Linard, Gross, Thomas R..  2016.  Using Smartphones As Continuous Receivers in a Visible Light Communication System. Proceedings of the 3rd Workshop on Visible Light Communication Systems. :61–66.
Visible Light Communication (VLC) allows to reuse a lighting infrastructure for communication while its main purpose of illumination can be carried out at the same time. Light sources based on Light Emitting Diodes (LEDs) are attractive as they are inexpensive, ubiquitous, and allow rapid modulation. This paper describes how to integrate smartphones into such a communication system that supports networking for a wide range of devices, such as toys with single LEDs as transmitter and receivers as well as interconnected LED light bulbs. The main challenge is how to employ the smartphone without any (hardware) modification as a receiver, using the integrated camera as a (slow) light sampling device. This paper presents a simple software-based solution, exploiting the rolling shutter effect and slow motion video capturing capabilities of latest smartphones to enable continuous reception and real-time integration into an existing VLC system. Evaluation results demonstrate a working prototype and report communication distances up to 3m and a maximum data throughput of more than 1200b/s, improving upon previous work.
Uzhga-Rebrov, O., Kuleshova, G..  2020.  Using Singular Value Decomposition to Reduce Dimensionality of Initial Data Set. 2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS). :1–4.
The purpose of any data analysis is to extract essential information implicitly present in the data. To do this, it often seems necessary to transform the initial data into a form that allows one to identify and interpret the essential features of their structure. One of the most important tasks of data analysis is to reduce the dimension of the original data. The paper considers an approach to solving this problem based on singular value decomposition (SVD).
Prosser, B., Dawes, N., Fulp, E.W., McKinnon, A.D., Fink, G.A..  2014.  Using Set-Based Heading to Improve Mobile Agent Movement. Self-Adaptive and Self-Organizing Systems (SASO), 2014 IEEE Eighth International Conference on. :120-128.

Cover time measures the time (or number of steps) required for a mobile agent to visit each node in a network (graph) at least once. A short cover time is important for search or foraging applications that require mobile agents to quickly inspect or monitor nodes in a network, such as providing situational awareness or security. Speed can be achieved if details about the graph are known or if the agent maintains a history of visited nodes, however, these requirements may not be feasible for agents with limited resources, they are difficult in dynamic graph topologies, and they do not easily scale to large networks. This paper introduces a set-based form of heading (directional bias) that allows an agent to more efficiently explore any connected graph, static or dynamic. When deciding the next node to visit, agents are discouraged from visiting nodes that neighbor both their previous and current locations. Modifying a traditional movement method, e.g., random walk, with this concept encourages an agent to move toward nodes that are less likely to have been previously visited, reducing cover time. Simulation results with grid, scale-free, and minimum distance graphs demonstrate heading can consistently reduce cover time as compared to non-heading movement techniques.
 

Dickerson, J.P., Kagan, V., Subrahmanian, V.S..  2014.  Using sentiment to detect bots on Twitter: Are humans more opinionated than bots? Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on. :620-627.

In many Twitter applications, developers collect only a limited sample of tweets and a local portion of the Twitter network. Given such Twitter applications with limited data, how can we classify Twitter users as either bots or humans? We develop a collection of network-, linguistic-, and application-oriented variables that could be used as possible features, and identify specific features that distinguish well between humans and bots. In particular, by analyzing a large dataset relating to the 2014 Indian election, we show that a number of sentimentrelated factors are key to the identification of bots, significantly increasing the Area under the ROC Curve (AUROC). The same method may be used for other applications as well.

Gardner, M. T., Beard, C., Medhi, D..  2017.  Using SEIRS Epidemic Models for IoT Botnets Attacks. DRCN 2017 - Design of Reliable Communication Networks; 13th International Conference. :1–8.

The spread of Internet of Things (IoT) botnets like those utilizing the Mirai malware were successful enough to power some of the most powerful DDoS attacks that have been seen thus far on the Internet. Two such attacks occurred on October 21, 2016 and September 20, 2016. Since there are an estimated three billion IoT devices currently connected to the Internet, these attacks highlight the need to understand the spread of IoT worms like Mirai and the vulnerability that they create for the Internet. In this work, we describe the spread of IoT worms using a proposed model known as the IoT Botnet with Attack Information (IoT-BAI), which utilizes a variation of the Susceptible-Exposed-Infected-Recovered-Susceptible (SEIRS) epidemic model [14]. The IoT-BAI model has shown that it may be possible to mitigate the frequency of IoT botnet attacks with improved user information which may positively affect user behavior. Additionally, the IoT-BAI model has shown that increased vulnerability to attack can be caused by new hosts entering the IoT population on a daily basis. Models like IoT-BAI could be used to predict user behavior after significant events in the network like a significant botnet attack.

Vaarandi, R., Pihelgas, M..  2014.  Using Security Logs for Collecting and Reporting Technical Security Metrics. Military Communications Conference (MILCOM), 2014 IEEE. :294-299.

During recent years, establishing proper metrics for measuring system security has received increasing attention. Security logs contain vast amounts of information which are essential for creating many security metrics. Unfortunately, security logs are known to be very large, making their analysis a difficult task. Furthermore, recent security metrics research has focused on generic concepts, and the issue of collecting security metrics with log analysis methods has not been well studied. In this paper, we will first focus on using log analysis techniques for collecting technical security metrics from security logs of common types (e.g., Network IDS alarm logs, workstation logs, and Net flow data sets). We will also describe a production framework for collecting and reporting technical security metrics which is based on novel open-source technologies for big data.
 

Kong, Shuyu, Shen, Yuanqi, Zhou, Hai.  2017.  Using Security Invariant To Verify Confidentiality in Hardware Design. Proceedings of the on Great Lakes Symposium on VLSI 2017. :487–490.

Due to the increasing complexity of design process, outsourcing, and use of third-party blocks, it becomes harder and harder to prevent Trojan insertion and other malicious design modifications. In this paper, we propose to deploy security invariant as carried proof to prevent and detect Trojans and malicious attacks and to ensure the security of hardware design. Non-interference with down-grading policy is checked for confidentiality. Contrary to existing approaches by type checking, we develop a method to model-check a simple safety property on a composed machine. Down-grading is handled in a better way in model-checking and the effectiveness of our approach is demonstrated on various Verilog benchmarks.

Azriel, Leonid, Ginosar, Ran, Gueron, Shay, Mendelson, Avi.  2016.  Using Scan Side Channel for Detecting IP Theft. Proceedings of the Hardware and Architectural Support for Security and Privacy 2016. :1:1–1:8.

We present a process for detection of IP theft in VLSI devices that exploits the internal test scan chains. The IP owner learns implementation details in the suspect device to find evidence of the theft, while the top level function is public. The scan chains supply direct access to the internal registers in the device, thus making it possible to learn the logic functions of the internal combinational logic chunks. Our work introduces an innovative way of applying Boolean function analysis techniques for learning digital circuits with the goal of IP theft detection. By using Boolean function learning methods, the learner creates a partial dependency graph of the internal flip-flops. The graph is further partitioned using the SNN graph clustering method, and individual blocks of combinational logic are isolated. These blocks can be matched with known building blocks that compose the original function. This enables reconstruction of the function implementation to the level of pipeline structure. The IP owner can compare the resulting structure with his own implementation to confirm or refute that an IP violation has occurred. We demonstrate the power of the presented approach with a test case of an open source Bitcoin SHA-256 accelerator, containing more than 80,000 registers. With the presented method we discover the microarchitecture of the module, locate all the main components of the SHA-256 algorithm, and learn the module's flow control.