A Hardware Immune System for MC8051 IP Core

A Hardware Immune System for MC8051 IP Core

Xin Wang (University of Science and Technology of China, China), Wenjian Luo (University of Science and Technology of China, China), Zhifang Li (University of Science and Technology of China, China) and Xufa Wang (University of Science and Technology of China, China)
DOI: 10.4018/978-1-60566-310-4.ch015
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Abstract

A hardware immune system for the error detection of MC8051 IP core is designed in this chapter. The binary string to be detected by the hardware immune system is made from the concatenation of the PC values in two sequential machine cycles of the MC8051. When invalid PC transitions occurred in the MC8051, the alarm signal of the hardware immune system can be activated by the detector set. The hardware immune system designed in this chapter is implemented and tested on an FPGA development board, and the result is given in waveforms of the implemented circuits. The disadvantages and future works about the system are also discussed.
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Backgrounds

This section is organized as follows. Firstly, the Negative Selection Algorithm (NSA) by Forrest and her colleagues (Forrest et al., 1994) is briefly introduced, which is the fundamental algorithm applied in the hardware immune system. Secondly, a brief introduction to the architecture of the hardware immune system proposed by Bradley and Tyrrell (Bradley & Tyrrell, 2002b) is given. Related works about NSA and hardware immune systems are also given.

Key Terms in this Chapter

Hardware Immune System: Taking inspiration from the biological immune systems, a method of error detection for the sequential digital systems represented as finite state machines (FSM) is proposed by Bradley and Tyrrell as a novel approach to hardware fault tolerance. Negative Selection Algorithm (NSA) proposed by Forrest et al is applied to identify faults within a FSM by distinguishing invalid state transitions from valid state transitions. The state transitions of the system under monitoring are represented by binary strings in the form of “previous state / current state / current input”. In particular, it is shown that by use of partial matching in NSA, high fault coverage can be achieved with limited memory requirements. Bradley and Tyrrell also introduced a generic FSM immunization cycle that allows any system that can be represented as an FSM to be “immunized” against the occurrence of faulty operations.

Negative Selection Algorithm: In biological immune system, all new birth immature T-cells must undergo a process of negative selection in the thymus, where the self-reactive T-cells binding with self proteins are destroyed. When the mature T-cells are released to the blood circle, they can only bind with non-self antigens. Inspired by the self-nonself discrimination mechanism of the biological immune system, the Negative Selection Algorithm (NSA) is proposed by Forrest et al in 1994 as a change detection algorithm. The first step of the NSA is to collect a set of self strings that defines the normal state of the monitored system. Then the second step is to generate a set of detectors that only recognize non-self strings. Finally, the detector set is used to monitor the anomaly changes of the data to be protected.

MC8051 IP Core: MC8051 IP Core is a synthesizable VHDL microcontroller IP core provided by Oregano Systems – Design & Consulting GesmbH. It is binary compatible to the well known 8051 processor of Intel, and offers faster program execution compared to the original Intel 8051 devices since the processor’s architecture has been optimized. The VHDL source codes of the MC8051 IP Core are available for free under the GNU LGPL (Lesser General Public License). The source codes used in this chapter are downloaded from web page http://oregano.at/eng/8051.html.

Artificial Immune System: Inspired by several immunological principles, Artificial Immune Systems (AIS) emerged in the 1990s as a new branch of Computational Intelligence. Like artificial neural networks (ANNs), evolutionary algorithms (EAs), and cellular automata, AISs also try to extract ideas from the biological mechanisms in order to develop novel computational techniques for solving science and engineering problems. A number of AIS models are applied in areas like pattern recognition, fault detection, computer security, etc. Among various AIS models, negative selection, immune network and clonal selection are the most discussed models.

Finite State Machine: A model of computation consisting of a set of states, a start state, an input alphabet, and a transition function that maps input symbols and current states to a next state. Computation begins in the start state with an input string. It changes to new states depending on the transition function. There are many variants, for instance, machines having actions (outputs) associated with transitions (Mealy machine) or states (Moore machine), etc. (http://www.nist.gov/dads/HTML/finiteStateMachine.html). A finite state machine can be used both as a development tool for solving problems and as a formal way of describing specific device or program interactions.

Immunotronics: Immunotronics is a term combined from “immunological electronics”. It represents the immune-inspired hardware fault-tolerance technique proposed by Bradley and Tyrrell, i.e., the hardware immune system.

Error Detection: Discovering an error operation in hardware or software. In this chapter, the error detection indicates the detection of invalid state transitions occurred in the monitored system (the MC8051 IP Core) which can be represented as an FSM.

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Table of Contents
Foreword
Lipo Wang
Preface
Hongwei Mo
Chapter 1
Fabio Freschi, Carlos A. Coello Coello, Maurizio Repetto
This chapter aims to review the state of the art in algorithms of multiobjective optimization with artificial immune systems (MOAIS). As it will be... Sample PDF
Multiobjective Optimization and Artificial Immune Systems: A Review
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Chapter 2
Jun Chen, Mahdi Mahfouf
The primary objective of this chapter is to introduce Artificial Immune Systems (AIS) as a relatively new bio-inspired optimization technique and to... Sample PDF
Artificial Immune Systems as a Bio-Inspired Optimization Technique and Its Engineering Applications
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Chapter 3
Licheng Jiao, Maoguo Gong, Wenping Ma
Many immue-inspired algorithms are based on the abstractions of one or several immunology theories, such as clonal selection, negative selection... Sample PDF
An Artificial Immune Dynamical System for Optimization
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Chapter 4
Malgorzata Lucinska, Slawomir T. Wierzchon
Multi-agent systems (MAS), consist of a number of autonomous agents, which interact with one-another. To make such interactions successful, they... Sample PDF
An Immune Inspired Algorithm for Learning Strategies in a Pursuit-Evasion Game
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Chapter 5
Luis Fernando Niño Vasquez, Fredy Fernando Muñoz Mopan, Camilo Eduardo Prieto Salazar, José Guillermo Guarnizo Marín
Artificial Immune Systems (AIS) have been widely used in different fields such as robotics, computer science, and multi-agent systems with high... Sample PDF
Applications of Artificial Immune Systems in Agents
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Chapter 6
Xingquan Zuo
Inspired from the robust control principle, a robust scheduling method is proposed to solve uncertain scheduling problems. The uncertain scheduling... Sample PDF
An Immune Algorithm Based Robust Scheduling Methods
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Chapter 7
Fabio Freschi, Maurizio Repetto
The increasing cost of energy and the introduction of micro-generation facilities and the changes in energy production systems require new... Sample PDF
Artificial Immune System in the Management of Complex Small Scale Cogeneration Systems
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Chapter 8
Krzysztof Ciesielski, Mieczyslaw A. Klopotek, Slawomir T. Wierzchon
In this chapter the authors discuss an application of an immune-based algorithm for extraction and visualization of clusters structure in large... Sample PDF
Applying the Immunological Network Concept to Clustering Document Collections
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Chapter 9
Xiangrong Zhang, Fang Liu
The problem of feature selection is fundamental in various tasks like classification, data mining, image processing, conceptual learning, and so on.... Sample PDF
Feature Selection Based on Clonal Selection Algorithm: Evaluation and Application
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Chapter 10
Yong-Sheng Ding, Xiang-Feng Zhang, Li-Hong Ren
Future Internet should be capable of extensibility, survivability, mobility, and adaptability to the changes of different users and network... Sample PDF
Immune Based Bio-Network Architecture and its Simulation Platform for Future Internet
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Chapter 11
Tao Gong
Static Web immune system is an important applicatiion of artificial immune system, and it is also a good platform to develop new immune computing... Sample PDF
A Static Web Immune System and Its Robustness Analysis
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Chapter 12
Alexander O. Tarakanov
Based on mathematical models of immunocomputing, this chapter describes an approach to spatio-temporal forecast (STF) by intelligent signal... Sample PDF
Immunocomputing for Spatio-Temporal Forecast
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Chapter 13
Fu Dongmei
In engineering application, the characteristics of the control system are entirely determined by the system controller once the controlled object... Sample PDF
Research of Immune Controllers
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Chapter 14
Xiaojun Bi
In fact, image segmentation can be regarded as a constrained optimization problem, and a series of optimization strategies can be used to complete... Sample PDF
Immune Programming Applications in Image Segmentation
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Chapter 15
Xin Wang, Wenjian Luo, Zhifang Li, Xufa Wang
A hardware immune system for the error detection of MC8051 IP core is designed in this chapter. The binary string to be detected by the hardware... Sample PDF
A Hardware Immune System for MC8051 IP Core
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Chapter 16
Mark Burgin, Eugene Eberbach
There are different models of evolutionary computations: genetic algorithms, genetic programming, etc. This chapter presents mathematical... Sample PDF
On Foundations of Evolutionary Computation: An Evolutionary Automata Approach
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Chapter 17
Terrence P. Fries
Path planning is an essential component in the control software for an autonomous mobile robot. Evolutionary strategies are employed to determine... Sample PDF
Evolutionary Path Planning for Robot Navigation Under Varying Terrain Conditions
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Chapter 18
Konstantinos Konstantinidis, Georgios Ch. Sirakoulis, Ioannis Andreadis
The aim of this chapter is to provide the reader with a Content Based Image Retrieval (CBIR) system which incorporates AI through ant colony... Sample PDF
Ant Colony Optimization for Use in Content Based Image Retrieval
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Chapter 19
Miroslav Bursa, Lenka Lhotska
The chapter concentrates on the use of swarm intelligence in data mining. It focuses on the problem of medical data clustering. Clustering is a... Sample PDF
Ant Colonies and Data Mining
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Chapter 20
Bo-Suk Yang
This chapter describes a hybrid artificial life optimization algorithm (ALRT) based on emergent colonization to compute the solutions of global... Sample PDF
Artificial Life Optimization Algorithm and Applications
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Chapter 21
Martin Macaš, Lenka Lhotská
A novel binary optimization technique is introduced called Social Impact Theory based Optimizer (SITO), which is based on social psychology model of... Sample PDF
Optimizing Society: The Social Impact Theory Based Optimizer
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Chapter 22
James F. Peters, Shabnam Shahfar
The problem considered in this chapter is how to use the observed behavior of organisms as a basis for machine learning. The proposed approach for... Sample PDF
Ethology-Based Approximate Adaptive Learning: A Near Set Approach
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Chapter 23
Dingju Zhu
Parallel computing is more and more important for science and engineering, but it is not used so widely as serial computing. People are used to... Sample PDF
Nature Inspired Parallel Computing
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Chapter 24
Tang Mo, Wang Kejun, Zhang Jianmin, Zheng Liying
An understanding of the human brain’s local function has improved in recent years. But the cognition of human brain’s working process as a whole is... Sample PDF
Fuzzy Chaotic Neural Networks
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About the Contributors