Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

International Technology Roadmap for Semiconductors 2003の要求清浄度について － シリコンウエハ表面と雰囲気環境に要求される清浄度, 分析方法の現状について －

Editor's note:Today's integrated circuits are vulnerable to hardware Trojans, which are malicious alterations to the circuit, either during design or fabrication. This article presents a classification of hardware Trojans and a survey of published techniques for Trojan detection.

/pdf/a-survey-of-hardware-trojan-taxonomy-and-detection-4fn90ckd0z.pdf

A Survey of Hardware Trojan Taxonomy and Detection

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Design Of Analog Cmos Integrated Circuits

Hardware intellectual-property (IP) cores have emerged as an integral part of modern system-on-chip (SoC) designs. However, IP vendors are facing major challenges to protect hardware IPs from IP piracy. This paper proposes a novel design methodology for hardware IP protection using netlist-level obfuscation. The proposed methodology can be integrated in the SoC design and manufacturing flow to simultaneously obfuscate and authenticate the design. Simulation results for a set of ISCAS-89 benchmark circuits and the advanced-encryption-standard IP core show that high levels of security can be achieved at less than 5% area and power overhead under delay constraint.

https://www.eecs.ucf.edu/~jinyier/courses/EEE4932/Papers_Final/HARPOON%20-%20An%20Obfuscation-Based%20SoC%20Design%20Methodology%20for%20Hardware%20Protection.pdf

HARPOON: An Obfuscation-Based SoC Design Methodology for Hardware Protection

In order to ensure trusted in---field operation of integrated circuits, it is important to develop efficient low---cost techniques to detect malicious tampering (also referred to as Hardware Trojan ) that causes undesired change in functional behavior Conventional post--- manufacturing testing, test generation algorithms and test coverage metrics cannot be readily extended to hardware Trojan detection In this paper, we propose a test pattern generation technique based on multiple excitation of rare logic conditions at internal nodes Such a statistical approach maximizes the probability of inserted Trojans getting triggered and detected by logic testing, while drastically reducing the number of vectors compared to a weighted random pattern based test generation Moreover, the proposed test generation approach can be effective towards increasing the sensitivity of Trojan detection in existing side---channel approaches that monitor the impact of a Trojan circuit on power or current signature Simulation results for a set of ISCAS benchmarks show that the proposed test generation approach can achieve comparable or better Trojan detection coverage with about 85% reduction in test length on average over random patterns

/pdf/mero-a-statistical-approach-for-hardware-trojan-detection-3gp7gkfg0w.pdf

MERO: A Statistical Approach for Hardware Trojan Detection

Malicious modification of hardware during design or fabrication has emerged as a major security concern. Such tampering (also referred to as Hardware Trojan) causes an integrated circuit (IC) to have altered functional behavior, potentially with disastrous consequences in safety-critical applications. Conventional design-time verification and post-manufacturing testing cannot be readily extended to detect hardware Trojans due to their stealthy nature, inordinately large number of possible instances and large variety in structure and operating mode. In this paper, we analyze the threat posed by hardware Trojans and the methods of deterring them. We present a Trojan taxonomy, models of Trojan operations and a review of the state-of-the-art Trojan prevention and detection techniques. Next, we discuss the major challenges associated with this security concern and future research needs to address them.

Hardware Trojan: Threats and emerging solutions

Hardware Trojan attack in the form of malicious modification of a design has emerged as a major security threat. Sidechannel analysis has been investigated as an alternative to conventional logic testing to detect the presence of hardware Trojans. However, these techniques suffer from decreased sensitivity toward small Trojans, especially because of the large process variations present in modern nanometer technologies. In this paper, we propose a novel noninvasive, multiple-parameter side-channel analysisbased Trojan detection approach. We use the intrinsic relationship between dynamic current and maximum operating frequency of a circuit to isolate the effect of a Trojan circuit from process noise. We propose a vector generation approach and several design/test techniques to improve the detection sensitivity. Simulation results with two large circuits, a 32-bit integer execution unit (IEU) and a 128-bit advanced encryption standard (AES) cipher, show a detection resolution of 1.12 percent amidst ±20 percent parameter variations. The approach is also validated with experimental results. Finally, the use of a combined side-channel analysis and logic testing approach is shown to provide high overall detection coverage for hardware Trojan circuits of varying types and sizes.

Hardware Trojan Detection by Multiple-Parameter Side-Channel Analysis

Malicious hardware Trojan circuitry inserted in safety-critical applications is a major threat to national security. In this work, we propose a novel application of a key-based obfus-cation technique to achieve security against hardware Trojans. The obfuscation scheme is based on modifying the state transition function of a given circuit by expanding its reachable state space and enabling it to operate in two distinct modes — the normal mode and the obfuscated mode. Such a modification obfuscates the rareness of the internal circuit nodes, thus making it difficult for an adversary to insert hard-to-detect Trojans. It also makes some inserted Trojans benign by making them activate only in the obfuscated mode. The combined effect leads to higher Trojan detectability and higher level of protection against such attack. Simulation results for a set of benchmark circuits show that the scheme is capable of achieving high levels of security at modest design overhead. Categories and Subject Descriptors B.6.1 [Logic Design]: Design Styles-sequential circuits; K.6.5 [Management of Computing and Information Systems]: Security and Protection-physical security General Terms Design, Security

Rajat Subhra Chakraborty

Papers

HARPOON: An Obfuscation-Based SoC Design Methodology for Hardware Protection

MERO: A Statistical Approach for Hardware Trojan Detection

Hardware Trojan: Threats and emerging solutions

Hardware Trojan Detection by Multiple-Parameter Side-Channel Analysis

Security against hardware Trojan through a novel application of design obfuscation