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

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.

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A Survey of Hardware Trojan Taxonomy and Detection

State-of-the-art exact and heuristic solution procedures for simple assembly line balancing

A classification of assembly line balancing problems

Assembly lines are special flow-line production systems which are of great importance in the indus-trial production of high quantity standardized commodities. Recently, assembly lines even gained importance in low volume production of customized products (mass-customization). Due to high capital requirements when installing or redesigning a line, its configuration planning is of great rele-vance for practitioners. Accordingly, this attracted attention of plenty researchers, who tried to sup-port real-world configuration planning by suited optimization models (assembly line balancing prob-lems). In spite of the enormous academic effort in assembly line balancing, there remains a consider-able gap between requirements of real configuration problems and the status of research. To ease communication between researchers and practitioners, we provide a classification scheme of assem-bly line balancing. This is a valuable step in identifying remaining research challenges which might contribute to closing the gap.

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

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 alterations of integrated circuits during fabrication in untrusted foundries pose major concern in terms of their reliable and trusted field operation. It is extremely difficult to discover such alterations, also referred to as “hardware Trojans” using conventional structural or functional testing strategies. In this paper, we propose a novel non-invasive, multiple-parameter side-channel analysis based Trojan detection approach that is capable of detecting malicious hardware modifications in the presence of large process variation induced noise. We exploit the intrinsic relationship between dynamic current (I DDT ) and maximum operating frequency (F max ) of a circuit to distinguish the effect of a Trojan from process induced fluctuations in I DDT . We propose a vector generation approach for I DDT measurement that can improve the Trojan detection sensitivity for arbitrary Trojan instances. Simulation results with two large circuits, a 32-bit integer execution unit (IEU) and a 128-bit Advanced Encryption System (AES) cipher, show a detection resolution of 0.04% can be achieved in presence of ±20% parameter (V th ) variations. The approach is also validated with experimental results using 120nm FPGA (Xilinx Virtex-II) chips.

Multiple-parameter side-channel analysis: A non-invasive hardware Trojan detection approach

Access to embedded processor cores for application of test has greatly complicated the testability of large systems on silicon. Scan based testing methods cannot be applied to processor cores which cannot be modified to meet the design requirements for scan insertion. Instruction Randomization Self Test (IRST) achieves stuck-at-fault coverage for an embedded processor core without the need for scan insertion or mux isolation for application of test patterns. This is a new built-in self test method which combines the execution of microprocessor instructions with a small amount of on-chip test hardware which is used to randomize those instructions. IRST is well suited for meeting the challenges of testing ASIC systems which contain embedded processor cores.

Instruction randomization self test for processor cores

A high level synthesis for testability method is presented whose objective is to generate self-testable RTL designs from data flow behavioral descriptions. The approach is formulated as an allocation problem based on an underlying structural testability model and its connection rules. Two allocation techniques have been developed to solve this problem: one based on an efficient heuristic algorithm that generates cost-effective designs, the other based on an integer linear program formulation that generates optimal designs. The allocation algorithms have been implemented and several benchmark examples are presented.

Christos A. Papachristou

Papers

MERO: A Statistical Approach for Hardware Trojan Detection

Hardware Trojan Detection by Multiple-Parameter Side-Channel Analysis

Multiple-parameter side-channel analysis: A non-invasive hardware Trojan detection approach

Instruction randomization self test for processor cores

A data path synthesis method for self-testable designs