Anomaly detection and foresight response strategy for wireless sensor networks

GhasemiGol, Mohammad; Ghaemi-Bafghi, Abbas; Moghaddam, Mohammad Hossein Yaghmaee; Sadoghi-Yazdi, Hadi

doi:10.1007/s11276-014-0858-z

Cited by 8 publications

(4 citation statements)

References 38 publications

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“…In this section, the performance of our proposed method is evaluated by applying it to synthetic and real data sets, and compared with the method of FCSVDD and LP-FCSVDD in the literature [26]. All experimental evaluations are performed on MATLAB with the data sets and are run on Intel Core i5 CPU, 3.30 GHZ, and the OS is windows 7.…”

Section: Simulation Scenariomentioning

confidence: 99%

“…Rajasegarar et al [24,25] proposed a distributed approach of one-class quarter-sphere support vector machine (QSSVM) and a centered approach of hyper-ellipsoidal support vector machine (CESVM) for anomaly detection in WSNs; they compared and analyzed the detection accuracy and sensitivity to parameter settings of CESVM and QSSVM. Gol et al [26] proposed a linearprogramming-based fuzzy-constraint SVDD method for anomaly detection in WSNs. In general, data mining and machine learning methods can achieve the desired effect of anomaly detection in WSNs.…”

Section: Introductionmentioning

confidence: 99%

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A Lightweight Anomaly Detection Method Based on SVDD for Wireless Sensor Networks

Chen

2019

Wireless Pers Commun

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Limited resources and harsh deployment environments may cause raw observations collected by sensor nodes to have poor data quality and reliability, which will influence the accuracy of the analysis and decision making in wireless sensor networks (WSNs). Therefore, anomaly detection must be implemented on the data collected by nodes. Support vector data description based on spatiotemporal and attribute correlations (STASVDD) can efficiently detect outliers. A novel optimization method based on STASVDD (N-STAS-VDD) is put forward in this paper. The proposed method considers that outliers can independently occur in each attribute when the collected data vectors are independent and identically distributed in WSNs. The proposed method applies the concept of core-sets to reduce the computational complexity of the quadratic programming problem in STAS-VDD, consequently reducing the energy consumption of resources-constrained WSNs. In addition, comparing the distributed and centralized detection approach of this method, the results show that the distributed approach has better performance because it relieves the communication burden. Extensive experiments were performed on both synthetic and real WSNs datasets. Results revealed that N-STASVDD achieves low time complexity and high detection accuracy.

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Section: Simulation Scenariomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Lightweight Anomaly Detection Method Based on SVDD for Wireless Sensor Networks

Chen

2019

Wireless Pers Commun

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“…The flow of data is disrupted by modifying data packets, creating unwanted routing activities, selectively dropping and turning off the wormhole link periodically, when the attackers utilize a high volume of network traffic during the wormhole. [14,17,20]. In this paper an attempt has been made to propose a fully distributed approach for early warning, when pre-attack activities are detected, using trust mechanisms.…”

Section: Threats To Wireless Lanmentioning

confidence: 99%

DISTRIBUTED AGENT BASED TECHNIQUE FOR DETECTING DISTRIBUTED DENIAL-OF-SERVICE (DDoS) ATTACKS IN WLAN

Singh¹,

Dhir²

2018

ijarcs

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Abstract:By sending large amount of data flows from multiple sites, Distributed Denial-of-Service (DDoS) attacks target the victims. Thus, there is a demand to implement number of DDoS defense techniques all together and collaboratively on many nodes, especially on where there is a need to maintain round-the-clock Internet connectivity. The security mechanism works on a probabilistic basis that is based on the detection of illegitimate traffic and then to discard it, that forced a specific number of legitimate packets to be fallout in the process and reducing the overall quality of service. In this paper a Distributed Agent Based technique for detecting DDoS Attacks in wireless LAN has been proposed. It is fully distributed and provides an early warning when pre-attack activities are detected, using trust mechanisms. From the simulation results it has been found that the proposed distributed agent based architecture achieves high throughput with low packet drop, by detecting and isolating the attack traffic flows.

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“…Comprehensive analysis shows that the main hidden dangers of current network security include the design defects of the physical level of network structure, the contradictions existing in the openness characteristics of the network, and the decreasing difficulty of traditional network password decoding. At present, the main intrusion methods of network wireless sensor networks mainly include distributed network intrusion means, target machine attack means, denial of service attack means, and corresponding system attack means [ 6 , 7 ]. In addition to the problems of intrusion detection technology, the current privacy protection technology of wireless sensor networks is also threatened by the energy, calculation, and storage of wireless sensor network nodes.…”

Section: Introductionmentioning

confidence: 99%

Privacy Protection and Intrusion Detection System of Wireless Sensor Network Based on Artificial Neural Network

Shi¹,

Li²

2022

Computational Intelligence and Neuroscience

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With the increasing openness and development of network technology, the network based on the wireless sensor network system has increasingly become an important tool for human social life and production, but it also brings some network security problems. Among them, focus is on network privacy disclosure and foreign intrusion and the research of intrusion detection and privacy protection has increasingly become an important topic of network security. This paper deeply studies the wireless sensor network system based on neural network on the basis of traditional privacy protection and intrusion detection system. Firstly, it applies particle swarm optimization algorithm and constructs a wireless sensor network intrusion detection system based on particle swarm optimization algorithm. The system includes important modules such as data extraction, data analysis, data feedback, and auxiliary decision-making. Compared with other algorithms, particle swarm optimization algorithm does not rely on problem information. It mainly uses real numbers to solve, so the algorithm has strong universality. At the same time, its corresponding principle is simple and easy to implement and less parameters need to be adjusted. Compared with other algorithms, particle swarm optimization algorithm has fast convergence speed and little memory requirement for the computer. At the same time, this paper uses the leap of particle swarm optimization algorithm to make it easier to find the global optimal solution. At the corresponding level of wireless sensor network privacy protection, based on the original data aggregation privacy protection scheme, this paper proposes a privacy protection scheme based on polynomial regression and a user privacy protection scheme based on the same state encryption, which further improves the security of privacy protection and facilitates the management of information. To realize the integrity of user privacy information protection, this paper realizes the decryption of data based on the correlation between binary metadata and compares the corresponding decrypted data with aggregated data, so as to complete the integrity of privacy data protection. The experimental results show that the binary metadata correlation decryption method proposed in this paper and the introduction of the corresponding particle swarm optimization algorithm improve the stability of the system by about 10%, the corresponding system security by a positive proportion, and the integrity of private data by about 10%; therefore, the algorithm proposed in this paper has obvious advantages.

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Anomaly detection and foresight response strategy for wireless sensor networks

Cited by 8 publications

References 38 publications

A Lightweight Anomaly Detection Method Based on SVDD for Wireless Sensor Networks

A Lightweight Anomaly Detection Method Based on SVDD for Wireless Sensor Networks

DISTRIBUTED AGENT BASED TECHNIQUE FOR DETECTING DISTRIBUTED DENIAL-OF-SERVICE (DDoS) ATTACKS IN WLAN

Privacy Protection and Intrusion Detection System of Wireless Sensor Network Based on Artificial Neural Network

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