Results 1 - 10
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59
The Use of Computational Intelligence in Intrusion Detection Systems: A Review
, 2008
"... Intrusion detection based upon computational intelligence is currently attracting considerable interest from the research community. Characteristics of computational intelligence (CI) systems, such as adaptation, fault tolerance, high computational speed and error resilience in the face of noisy inf ..."
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Cited by 46 (0 self)
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Intrusion detection based upon computational intelligence is currently attracting considerable interest from the research community. Characteristics of computational intelligence (CI) systems, such as adaptation, fault tolerance, high computational speed and error resilience in the face of noisy information fit the requirements of building a good intrusion detection model. Here we want to provide an overview of the research progress in applying CI methods to the problem of intrusion detection. The scope of this review will be on core methods of CI, including artificial neural networks, fuzzy systems, evolutionary computation, artificial immune systems, swarm intelligence, and soft computing. The research contributions in each field are systematically summarized and compared, allowing us to clearly define existing research challenges, and to highlight promising new research directions. The findings of this review should provide useful insights into the current IDS literature and be a good source for anyone who is interested in the application of CI approaches to IDSs or related fields.
Computational Intelligence in Wireless Sensor Networks: A Survey
- IEEE COMMUNICATIONS SURVEYS & TUTORIALS
, 2011
"... Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense or monitor physical or environmental conditions cooperatively. WSNs face many challenges, mainly caused by communication failures, storage and computational constraints and limited power supply. Paradigms o ..."
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Cited by 41 (0 self)
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Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense or monitor physical or environmental conditions cooperatively. WSNs face many challenges, mainly caused by communication failures, storage and computational constraints and limited power supply. Paradigms of computational intelligence (CI) have been successfully used in recent years to address various challenges such as data aggregation and fusion, energy aware routing, task scheduling, security, optimal deployment and localization. CI provides adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures and scenario changes. However, WSN developers are usually not or not completely aware of the potential CI algorithms offer. On the other side, CI researchers are not familiar with all real problems and subtle requirements of WSNs. This mismatch makes collaboration and development difficult. This paper intends to close this gap and foster collaboration by offering a detailed introduction to WSNs and their properties. An extensive survey of CI applications to various problems in WSNs from various research areas and publication venues is presented in the paper. Besides, a discussion on advantages and disadvantages of CI algorithms over traditional WSN solutions is offered. In addition, a general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for WSNs.
Integrated Innate and Adaptive Artificial Immune Systems Applied to Process Anomaly Detection
, 2007
"... This thesis explores the design and application of artificial immune systems (AISs), problem-solving systems inspired by the human and other immune systems. AISs to date have largely been modelled on the biological adaptive immune system and have taken little inspiration from the innate immune syste ..."
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Cited by 24 (5 self)
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This thesis explores the design and application of artificial immune systems (AISs), problem-solving systems inspired by the human and other immune systems. AISs to date have largely been modelled on the biological adaptive immune system and have taken little inspiration from the innate immune system. The first part of this thesis examines the biological innate immune system, which controls the adaptive immune system. The importance of the innate immune system suggests that AISs should also incorporate models of the innate immune system as well as the adaptive immune system. This thesis presents and discusses a number of design principles for AISs which are modelled on both innate and adaptive immunity. These novel design principles provided a structured framework for developing AISs which incorporate innate and adaptive immune systems in general. These design principles are used to build a software system which allows such AISs to be implemented and explored.
Recent Advances in Artificial Immune Systems: Models and Applications
- APPLIED SOFT COMPUTING
, 2011
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A danger theory inspired approach to web mining
- Artificial Immune Systems. Second International Conference, ICARIS 2003 Proceedings, number 2787 in Lecture Notes In Computer Science
, 2003
"... Abstract. Within immunology, new theories are constantly being proposed that challenge current ways of thinking. These include new theories regarding how the immune system responds to pathogenic material. This conceptual paper takes one relatively new such theory: the Danger theory, and explores the ..."
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Cited by 9 (2 self)
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Abstract. Within immunology, new theories are constantly being proposed that challenge current ways of thinking. These include new theories regarding how the immune system responds to pathogenic material. This conceptual paper takes one relatively new such theory: the Danger theory, and explores the relevance of this theory to the application domain of web mining. Central to the idea of Danger theory is that of a context dependant response to invading pathogens. This paper argues that this context dependency could be utilised as powerful metaphor for applications in web mining. An illustrative example adaptive mailbox filter is presented that exploits properties of the immune system, including the Danger theory. This is essentially a dynamical classification task: a task that this paper argues is well suited to the field of artificial immune systems, particularly when drawing inspiration from the Danger theory. 1
An immune-inspired approach to anomaly detection
- Handbook of Research on Information Assurance and Security. Idea Publishing Group, accepted for publication May 2007. 230
, 2007
"... The immune system provides a rich metaphor for computer security: anomaly detection that works in nature should work for machines. However, early artificial immune system approaches for computer security had only limited success. Arguably, this was due to these artificial systems being based on too ..."
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Cited by 6 (4 self)
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The immune system provides a rich metaphor for computer security: anomaly detection that works in nature should work for machines. However, early artificial immune system approaches for computer security had only limited success. Arguably, this was due to these artificial systems being based on too simplistic a view of the immune system. We present here a second generation artificial immune system for process anomaly detection. It improves on earlier systems by having different artificial cell types that process information. Following detailed information about how to build such second generation systems, we find that communication between cells types is key to performance. Through realistic testing and validation we show that second generation artificial immune systems are capable of anomaly detection beyond generic system policies. The paper concludes with a discussion and outline of the next steps in this exciting area of computer security.
The Lemmings Puzzle: Computational Complexity of an Approach and Identification of Difficult Instances
"... Artificial Intelligence can be thought of as the study of machines that are capable of solving problems that require human level intelligence. It has frequently been concerned with game playing. In this thesis we shall focus on the areas of search and complexity, with respect to single-player games. ..."
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Cited by 3 (1 self)
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Artificial Intelligence can be thought of as the study of machines that are capable of solving problems that require human level intelligence. It has frequently been concerned with game playing. In this thesis we shall focus on the areas of search and complexity, with respect to single-player games. These types of games are called puzzles. Puzzles have received research attention for some decades. Consequently, inter-esting insights into some of these puzzles, and into the approaches for solving them, have emerged. However, many of these puzzles have been neglected by the artificial intelligence research community. Therefore, we survey these puzzles in the hope that we can motivate research towards them so that further interesting insights might emerge in the future. We describe research on a puzzle called LEMMINGS that is derived from a game called Lemmings, which itself is in NP-Complete. We attempt to find the first success-ful approach for LEMMINGS. We report on a successful approach to a sub-problem
Comments on Real-Valued Negative Selection vs. Real-Valued Positive Selection and One-Class SVM
"... Abstract — Real-valued negative selection (RVNS) is an immune-inspired technique for anomaly detection problems. It has been claimed that this technique is a competitive approach, comparable to statistical anomaly detection approaches such as one-class Support Vector Machine. Moreover, it has been c ..."
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Cited by 3 (0 self)
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Abstract — Real-valued negative selection (RVNS) is an immune-inspired technique for anomaly detection problems. It has been claimed that this technique is a competitive approach, comparable to statistical anomaly detection approaches such as one-class Support Vector Machine. Moreover, it has been claimed that the complementary approach to RVNS, termed real-valued positive selection, is not a realistic solution. We investigate these claims and show that these claims can not be sufficiently supported. I.
Negative selection with antigen feedback in intrusion detection
- in: 7th international conference on Artificial Immune Systems
, 2008
"... Abstract. One of the major challenges for negative selection is to efficiently generate effective detectors. The experiment in the past shows that random generation fails to generate useful detectors within acceptable time duration. In this paper, we propose an antigen feedback mechanism for genera ..."
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Abstract. One of the major challenges for negative selection is to efficiently generate effective detectors. The experiment in the past shows that random generation fails to generate useful detectors within acceptable time duration. In this paper, we propose an antigen feedback mechanism for generating the detectors. For an unmatched antigen, we make a copy of the antigen and treat it the same as a newly randomly generated antibody: it goes through the same maturing process and is subject to elimination due to self matching. If it survives and is then activated by more antigens, it becomes a legitimate detector. Our experiment demonstrates that the antigen feedback mechanism provides an efficient way to generate enough effective detectors within a very short period of time. With the antigen feedback mechanism, we achieved 95.21% detection rate on attack strings, with 4.79% false negative rate, and 99.21% detection rate on normal strings, 0.79% false positive. In this paper, we also introduce Arisytis -Artificial Immune System Tool Kits -a project we are undertaking for not only our own experiment, but also the research communities in the same area to avoid the waste on repeatedly developing similar software. Arisytis is available on the public domain. Finally, we also discuss the effectiveness of the r-continuous bits match and its impact on data presentation.
