The Experts below are selected from a list of 8133 Experts worldwide ranked by ideXlab platform
Shahaboddin Shamshirband - One of the best experts on this subject based on the ideXlab platform.
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co fais cooperative fuzzy artificial immune system for Detecting Intrusion in wireless sensor networks
2014Co-Authors: Shahaboddin Shamshirband, Nor Badrul Anuar, Laiha Mat Kiah, Vala Ali Rohani, Dalibor Petkovic, Sanjay Misra, Abdul Nasir KhanAbstract:Abstract Due to the distributed nature of Denial-of-Service attacks, it is tremendously challenging to identify such malicious behavior using traditional Intrusion detection systems in Wireless Sensor Networks (WSNs). In the current paper, a bio-inspired method is introduced, namely the cooperative-based fuzzy artificial immune system (Co-FAIS). It is a modular-based defense strategy derived from the danger theory of the human immune system. The agents synchronize and work with one another to calculate the abnormality of sensor behavior in terms of context antigen value (CAV) or attackers and update the fuzzy activation threshold for security response. In such a multi-node circumstance, the sniffer module adapts to the sink node to audit data by analyzing the packet components and sending the log file to the next layer. The fuzzy misuse detector module (FMDM) integrates with a danger detector module to identify the sources of danger signals. The infected sources are transmitted to the fuzzy Q-learning vaccination modules (FQVM) in order for particular, required action to enhance system abilities. The Cooperative Decision Making Modules (Co-DMM) incorporates danger detector module with the fuzzy Q-learning vaccination module to produce optimum defense strategies. To evaluate the performance of the proposed model, the Low Energy Adaptive Clustering Hierarchy (LEACH) was simulated using a network simulator. The model was subsequently compared against other existing soft computing methods, such as fuzzy logic controller (FLC), artificial immune system (AIS), and fuzzy Q-learning (FQL), in terms of detection accuracy, counter-defense, network lifetime and energy consumption, to demonstrate its efficiency and viability. The proposed method improves detection accuracy and successful defense rate performance against attacks compared to conventional empirical methods.
Abdul Nasir Khan - One of the best experts on this subject based on the ideXlab platform.
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co fais cooperative fuzzy artificial immune system for Detecting Intrusion in wireless sensor networks
2014Co-Authors: Shahaboddin Shamshirband, Nor Badrul Anuar, Laiha Mat Kiah, Vala Ali Rohani, Dalibor Petkovic, Sanjay Misra, Abdul Nasir KhanAbstract:Abstract Due to the distributed nature of Denial-of-Service attacks, it is tremendously challenging to identify such malicious behavior using traditional Intrusion detection systems in Wireless Sensor Networks (WSNs). In the current paper, a bio-inspired method is introduced, namely the cooperative-based fuzzy artificial immune system (Co-FAIS). It is a modular-based defense strategy derived from the danger theory of the human immune system. The agents synchronize and work with one another to calculate the abnormality of sensor behavior in terms of context antigen value (CAV) or attackers and update the fuzzy activation threshold for security response. In such a multi-node circumstance, the sniffer module adapts to the sink node to audit data by analyzing the packet components and sending the log file to the next layer. The fuzzy misuse detector module (FMDM) integrates with a danger detector module to identify the sources of danger signals. The infected sources are transmitted to the fuzzy Q-learning vaccination modules (FQVM) in order for particular, required action to enhance system abilities. The Cooperative Decision Making Modules (Co-DMM) incorporates danger detector module with the fuzzy Q-learning vaccination module to produce optimum defense strategies. To evaluate the performance of the proposed model, the Low Energy Adaptive Clustering Hierarchy (LEACH) was simulated using a network simulator. The model was subsequently compared against other existing soft computing methods, such as fuzzy logic controller (FLC), artificial immune system (AIS), and fuzzy Q-learning (FQL), in terms of detection accuracy, counter-defense, network lifetime and energy consumption, to demonstrate its efficiency and viability. The proposed method improves detection accuracy and successful defense rate performance against attacks compared to conventional empirical methods.
Jafar Habibi - One of the best experts on this subject based on the ideXlab platform.
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design and analysis of genetic fuzzy systems for Intrusion detection in computer networks
2011Co-Authors: Mohammad Saniee Abadeh, Hamid Mohamadi, Jafar HabibiAbstract:Research highlights? We present three kinds of genetic fuzzy systems for Intrusion detection problem. ? These IDSs can detect normal and abnormal behaviors in computer networks efficiently. ? Computer simulations demonstrate high performance of the proposed IDSs.? GFSs are able to develop accurate and also interpretable Intrusion detection systems. The capability of fuzzy systems to solve different kinds of problems has been demonstrated in several previous investigations. Genetic fuzzy systems (GFSs) hybridize the approximate reasoning method of fuzzy systems with the learning capability of evolutionary algorithms. The objective of this paper is to design and analysis of various kinds of genetic fuzzy systems to deal with Intrusion detection problem as a new real-world application area which is not previously tackled with GFSs. The resulted Intrusion detection system would be capable of Detecting normal and abnormal behaviors in computer networks. We have presented three kinds of genetic fuzzy systems based on Michigan, Pittsburgh and iterative rule learning (IRL) approaches to deal with Intrusion detection as a high-dimensional classification problem. Experiments were performed with DARPA data sets which have information on computer networks, during normal and intrusive behaviors. The paper presents some results and compares the performance of different generated fuzzy rule sets in Detecting Intrusion in a computer network according to three different types of genetic fuzzy systems.
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Intrusion detection using a fuzzy genetics based learning algorithm
2007Co-Authors: Saniee M Abadeh, Jafar Habibi, C LucasAbstract:Fuzzy systems have demonstrated their ability to solve different kinds of problems in various applications domains. Currently, there is an increasing interest to augment fuzzy systems with learning and adaptation capabilities. Two of the most successful approaches to hybridize fuzzy systems with learning and adaptation methods have been made in the realm of soft computing. Neural fuzzy systems and genetic fuzzy systems hybridize the approximate reasoning method of fuzzy systems with the learning capabilities of neural networks and evolutionary algorithms. The objective of this paper is to describe a fuzzy genetics-based learning algorithm and discuss its usage to detect Intrusion in a computer network. Experiments were performed with DARPA data sets [KDD-cup data set. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html], which have information on computer networks, during normal behaviour and intrusive behaviour. This paper presents some results and reports the performance of generated fuzzy rules in Detecting Intrusion in a computer network.
C Lucas - One of the best experts on this subject based on the ideXlab platform.
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Intrusion detection using a fuzzy genetics based learning algorithm
2007Co-Authors: Saniee M Abadeh, Jafar Habibi, C LucasAbstract:Fuzzy systems have demonstrated their ability to solve different kinds of problems in various applications domains. Currently, there is an increasing interest to augment fuzzy systems with learning and adaptation capabilities. Two of the most successful approaches to hybridize fuzzy systems with learning and adaptation methods have been made in the realm of soft computing. Neural fuzzy systems and genetic fuzzy systems hybridize the approximate reasoning method of fuzzy systems with the learning capabilities of neural networks and evolutionary algorithms. The objective of this paper is to describe a fuzzy genetics-based learning algorithm and discuss its usage to detect Intrusion in a computer network. Experiments were performed with DARPA data sets [KDD-cup data set. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html], which have information on computer networks, during normal behaviour and intrusive behaviour. This paper presents some results and reports the performance of generated fuzzy rules in Detecting Intrusion in a computer network.
Sanjay Misra - One of the best experts on this subject based on the ideXlab platform.
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co fais cooperative fuzzy artificial immune system for Detecting Intrusion in wireless sensor networks
2014Co-Authors: Shahaboddin Shamshirband, Nor Badrul Anuar, Laiha Mat Kiah, Vala Ali Rohani, Dalibor Petkovic, Sanjay Misra, Abdul Nasir KhanAbstract:Abstract Due to the distributed nature of Denial-of-Service attacks, it is tremendously challenging to identify such malicious behavior using traditional Intrusion detection systems in Wireless Sensor Networks (WSNs). In the current paper, a bio-inspired method is introduced, namely the cooperative-based fuzzy artificial immune system (Co-FAIS). It is a modular-based defense strategy derived from the danger theory of the human immune system. The agents synchronize and work with one another to calculate the abnormality of sensor behavior in terms of context antigen value (CAV) or attackers and update the fuzzy activation threshold for security response. In such a multi-node circumstance, the sniffer module adapts to the sink node to audit data by analyzing the packet components and sending the log file to the next layer. The fuzzy misuse detector module (FMDM) integrates with a danger detector module to identify the sources of danger signals. The infected sources are transmitted to the fuzzy Q-learning vaccination modules (FQVM) in order for particular, required action to enhance system abilities. The Cooperative Decision Making Modules (Co-DMM) incorporates danger detector module with the fuzzy Q-learning vaccination module to produce optimum defense strategies. To evaluate the performance of the proposed model, the Low Energy Adaptive Clustering Hierarchy (LEACH) was simulated using a network simulator. The model was subsequently compared against other existing soft computing methods, such as fuzzy logic controller (FLC), artificial immune system (AIS), and fuzzy Q-learning (FQL), in terms of detection accuracy, counter-defense, network lifetime and energy consumption, to demonstrate its efficiency and viability. The proposed method improves detection accuracy and successful defense rate performance against attacks compared to conventional empirical methods.