Intrusion Detection System for Wireless Sensor Networks Using Danger Theory Immune-Inspired Techniques

Salmon, Helio Mendes; Farias, Claudio M. de; Loureiro, Paula; Pìrmez, Luci; Rossetto, Silvana; Rodrigues, Paulo H. de A.; Pirmez, Rodrigo; Delicato, Flávia C.; Carmo, Luiz Fernando Rust da Costa

doi:10.1007/s10776-012-0179-z

Cited by 58 publications

(48 citation statements)

References 14 publications

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“…The possibility to apply Dendritic Cell Algorithm (DCA) in the attack detection in sensor network is investigated in [12]. An Intrusion Detection System (IDS) framework to be applied in the wireless sensor network context is proposed in [13]. However, regardless of model differences, in the case of large scale networks, sufficient sensors are required, which has a negative impact on system scalability.…”

Section: B Sensor Network Based Aismentioning

confidence: 99%

“…The attempts mainly focus on two parts: the algorithm itself and alternative hardware implementations [1]. In the aspect of hardware implementation, due to the mixed nature of AIS-based computing, traditional and unconventional computing paradigms have been put forward as AIS-based computing substrates [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Although increasing research has been conducted to investigate better versions of AIS and more suitable computing architectures to accommodate them [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20], few have compared the suitability of different computing approaches for the unique features of AIS when trying to introduce novel computing architectures, and few consider the practicality of their solutions for solving real-world machine learning problems.…”

Section: Introductionmentioning

confidence: 99%

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Improving Artificial-Immune-System-based computing by exploiting intrinsic features of computer architectures

Deng

Bentley

Momshad

2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

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Abstract-Biological systems have become highly significant for traditional computer architectures as examples of highly complex self-organizing systems that perform tasks in parallel with no centralized control. However, few researchers have compared the suitability of different computing approaches for the unique features of Artificial Immune Systems (AIS) when trying to introduce novel computing architectures, and few consider the practicality of their solutions for real world machine learning problems. We propose that the efficacy of AIS-based computing for tackling real world datasets can be improved by the exploitation of intrinsic features of computer architectures. This paper reviews and evaluates current existing implementation solutions for AIS on different computing paradigms and introduces the idea of "C Principles" and "A Principles". Three Artificial Immune Systems implemented on different architectures are compared using these principles to examine the possibility of improving AIS through taking advantage of intrinsic hardware features.

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Section: B Sensor Network Based Aismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving Artificial-Immune-System-based computing by exploiting intrinsic features of computer architectures

Deng

Bentley

Momshad

2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

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“…Sinkhole attacks (black hole and selective forwarding) can be characterized by degrading the RSSI and increasing packets loss according to [11].…”

Section: Case Studymentioning

confidence: 99%

DYSSS: A Dynamic and Context-Aware Security System for Shared Sensor Networks

Farias

Pìrmez

Carmo

et al. 2015

International Journal of Distributed Sensor Networks

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In recent years, we have witnessed the emergence of Shared Sensor Networks (SSNs) as a core component of cyber-physical systems for diverse applications. As Wireless Sensor and Actuator Networks (WSANs) design starts shifting from application-specific platforms to shared system infrastructures, a pressing research challenge is security. In scenarios involving unprotected hostile outdoor areas, SSNs are prone to different types of attacks that can compromise reliability, integrity, and availability of the sensor data traffic and sensor lifetime as well. In this work we propose a Dynamic Security System to be applied in the shared sensor network context. Its basic feature is the nodes neighborhood monitoring and collaboration (through the use of the Byzantine algorithm) to identify an attack and enhance security. The proposed security system is dynamic since it is able to manage the availability, integrity, and confidentiality of multiple applications according to the current execution context. It is also resilient, since it is able to support the continuous network operation even in the presence of malicious or faulty nodes. Its resilience is achieved for the capacity of gathering information from several nodes, thus inferring the countermeasures using context information.

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“…As an effective danger theory algorithm, dendritic cell algorithm [9] that based on the features of dendritic cell has already been used in the intrusion detection in WSN and achieved a higher detection rate [4][5]10]. As stated by Kim et al [4], the properties of danger theory based AIS intrusion detection algorithms, especially DCA can meet the security requirements of sensor networks environment as a distributed, limited power and limited capacity wireless sensor networks.…”

Section: Introductionmentioning

confidence: 99%

An Improved Dendritic Cell Algorithm Based Intrusion Detection System for Wireless Sensor Networks

Guo¹,

Chen²

2017

IJSIA

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Intrusion Detection System for Wireless Sensor Networks Using Danger Theory Immune-Inspired Techniques

Cited by 58 publications

References 14 publications

Improving Artificial-Immune-System-based computing by exploiting intrinsic features of computer architectures

Improving Artificial-Immune-System-based computing by exploiting intrinsic features of computer architectures

DYSSS: A Dynamic and Context-Aware Security System for Shared Sensor Networks

An Improved Dendritic Cell Algorithm Based Intrusion Detection System for Wireless Sensor Networks

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