Open Access Open Access  Restricted Access Subscription Access

Minimalistic Error via Clibat Algorithm for Attack-Defence Model on Wireless Sensor Networks (WSN)


Affiliations
1 Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh,, India
 

Wireless Sensor Networks have become the recent trend to effectively solve the problem in medical fields, primarily in agriculture and others in IoT monitoring. The expenses for ease of use in current domains are particular for cost and energy-effective solutions estimating different for different attack types on Wireless Sensor Network designs. Even though the energy, Routing problems are effectively solved, due to its wireless operative effects, the performance, such as speed and attackers, are reduced due to unevaded attacks. Hence, reducing this problem with energy-featured node optimization, network rate, and unevaded or random attack types like wormholes and black holes would implicate a significant problem in real-time modelling. On this basis, we postulate a solution analysis with the CLIBAT algorithm that implicates different possibilities and its probabilistic approaches, considering a proposed hybrid routing protocol on novel attack and defence algorithms to reduce the attack pattern with Wormhole and black hole attacks. In this perspective, an attack and defence pattern with an intuitive approach is implemented via the Improved Conditionally Expected Criteria feature to emphasize the type of attack (Wormhole or black hole attacks). Also, the Defense algorithm on improved sigmoid function on node characteristics is utilized to implicate with minimum distance formulations on the defense model effectively, MATLAB simulations with the solutions on WSN with CLIBAT algorithm inclusive of attack and defences are effectively removed.

Keywords

Attacks, DDoS, Firefly, Leach, Conditional Logistic Intuitive BAT Algorithm (CLIBAT), Wireless Sensor Networks (WSN), Distributed Energy-Efficient Clustering (DEEC), Least Probability Gradient algorithm (LPA), Intuitive Cumulative Expected Conditionality (ICEC), Multi-Point Route (MPR).
User
Notifications
Font Size

  • RaniyahWazirali, Rami Ahmad, “Machine learning approaches to detect DoS and their effect on WSNs lifetime” Computers, Materials and Continua (2022).
  • Karen Ávila Paul Sanmartin Daladier Jabba, Javier Gómez, “An analytical Survey of Attack Scenario Parameters on the Techniques of Attack Mitigation in WSN” Wireless Personal Communications (2022).
  • Kale NavnathDattatrayaK. Raghava Rao, "Hybrid-based cluster head selection for maximizing network lifetime and energy efficiency in WSN", Journal of King Saud University - Computer and Information Sciences (2022).
  • P. Gite, K. Chouhan, K. Murali Krishna, C. Kumar Nayak, M. Soni, and A. Shrivastava, “ML Based Intrusion Detection Scheme for various types of attacks in a WSN using C4.5 and CART classifiers,” Materials Today: Proceedings, 2021, DOI: 10.1016/j.matpr.2021.07.378.
  • A. Arshad, Z. M. Hanapi, S. Subramaniam, and R. Latip, “A survey of Sybil attack countermeasures in IoT-based wireless sensor networks,” PeerJ Computer Science, vol. 7, 2021, DOI: 10.7717/peerj-cs.673.
  • B. A. Ashwini and S. S. Manivannan, “Supervised Machine Learning Classification Algorithmic Approach for Finding Anomaly Type of Intrusion Detection in Wireless Sensor Network,” Optical Memory and Neural Networks (Information Optics), vol. 29, no. 3, 2020, DOI: 10.3103/S1060992X20030029.
  • R. Fotohi and S. Firoozi Bari, “A novel countermeasure technique to protect WSN against denial-of-sleep attacks using firefly and Hopfield neural network (HNN) algorithms,” Journal of Supercomputing, vol. 76, no. 9, 2020, DOI: 10.1007/s11227-019-03131-x.
  • N. Nithiyanandam and P. Latha, “Artificial bee colony-based sinkhole detection in wireless sensor networks,” Journal of Ambient Intelligence and Humanized Computing, 2019, DOI: 10.1007/s12652-019-01404-0.
  • S. V, “Detection of Localization Error in a WSN under Sybil Attack using Advanced DV-Hop Methodology,” IRO Journal on Sustainable Wireless Systems, vol. 3, no. 2, 2021, DOI: 10.36548/jsws.2021.2.003.
  • S. Ilavarasan and P. Latha, “Detection and elimination of black hole attack in WSN,” International Journal of Innovative Technology and Exploring Engineering, vol. 9, no. 1, 2019, DOI: 10.35940/ijitee.L3908.119119.
  • S. Singh and H. S. Saini, “Learning-Based Security Technique for Selective Forwarding Attack in Clustered WSN,” Wireless Personal Communications, vol. 118, no. 1, 2021, DOI: 10.1007/s11277-020- 08044-0.
  • R. Bhatt, P. Maheshwary, P. Shukla, P. Shukla, M. Shrivastava, and S. Changlani, “Implementation of Fruit Fly Optimization Algorithm (FFOA) to escalate the attacking efficiency of node capture attack in Wireless Sensor Networks (WSN),” Computer Communications, vol. 149, 2020, DOI: 10.1016/j.comcom.2019.09.007.
  • A. Dogra and T. Kaur, “DDOS attack detection and handling mechanism in WSN,” International Journal of Recent Technology and Engineering, vol. 8, no. 3, 2019, DOI: 10.35940/ijrte.C5644.098319.
  • M. v. Pawar and A. Jagadeesan, “Detection of blackhole and wormhole attacks in WSN enabled by optimal feature selection using selfAdaptive multi-verse optimizer with deep learning," International Journal of Communication Networks and Distributed Systems, vol. 26, no. 4, 2021, DOI: 10.1504/ijcnds.2021.115573.
  • V. Saini, J. Gupta, and K. D. Garg, “WSN protocols, research challenges in WSN, integrated areas of sensor networks, security attacks in WSN,” European Journal of Molecular and Clinical Medicine, vol. 7, no. 3, 2020.
  • E. Karakoç and C. Çeken, “Black hole attack prevention scheme using a blockchain-block approach in SDN-enabled WSN,” International Journal of Ad Hoc and Ubiquitous Computing, vol. 37, no. 1, 2021, DOI: 10.1504/IJAHUC.2021.115125.
  • M. Jeyaselvi, S. Suchitra, M. Sathya, and R. Mekala, “Energy efficient witness based clone and jamming attack detection in wsn,” Journal of Green Engineering, vol. 11, no. 2, 2021.
  • D. Dhadwal, V. Bhatia, and P. N. Hrisheekesha, “Method & implementation of fault detection & prevention attack in WSN,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 9 Special Issue, 2019, DOI: 10.35940/ijitee.I1126.0789S19.
  • Y. Wu, B. Kang, and H. Wu, "Strategies of attack–defence game for wireless sensor networks considering the effect of confidence level in fuzzy environment," Engineering Applications of Artificial Intelligence, vol. 102, 2021, DOI: 10.1016/j.engappai.2021.104238.
  • C. Hongsong, M. Caixia, F. Zhongchuan, and C. H. Lee, "Novel DDoS attack detection by Spark-assisted correlation analysis approach in wireless sensor network," IET Information Security, vol. 14, no. 4, 2020, DOI: 10.1049/iet-ifs.2018.5512.
  • P. D. Halle and S. Shiyamala, “Secure routing through refining reliability for WSN against DOS attacks using AODSD2V2 algorithm for AMI,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 10, 2019, doi: 10.35940/ijitee.I8178.0881019.
  • S. Godala and R. P. v. Vandella, "A study on intrusion detection system in wireless sensor networks," International Journal of Communication Networks and Information Security, vol. 12, no. 1, 2020.
  • Y. Prathyusha Reddy, B. Manasa, V. Jyothi, and V. Srikanth, "Detection and defence of DDOS attack for WSN," International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 7, 2019.
  • M. Alotaibi, “Security to wireless sensor networks against malicious attacks using Hamming residue method,” Eurasip Journal on Wireless Communications and Networking, vol. 2019, no. 1, 2019, DOI: 10.1186/s13638-018-1337-5.
  • S. Dong, X. gang Zhang, and W. gang Zhou, “A Security Localization Algorithm Based on DV-Hop Against Sybil Attack in Wireless Sensor Networks,” Journal of Electrical Engineering and Technology, vol. 15, no. 2, 2020, DOI: 10.1007/s42835-020-00361-5.
  • H. Xie, Z. Yan, Z. Yao, and M. Atiquzzaman, “Data collection for security measurement in wireless sensor networks: A survey,” IEEE Internet of Things Journal, vol. 6, no. 2, 2019, DOI: 10.1109/JIOT.2018.2883403.
  • M. A. Elsadig, A. Altigani, and M. A. A. Baraka, “Security issues and challenges on wireless sensor networks,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 8, no. 4, 2019, DOI: 10.30534/ijatcse/2019/78842019.
  • Basith, K. Abdul, and T. N. Shankar. "Hybrid state analysis with improved firefly optimized linear congestion models of WSNs for DDOS & CRA attacks." PeerJ Computer Science 8 (2022): e845.
  • S. Nosratian, M. Moradkhani, and M. B. Tavakoli, “Fuzzy-Based Reliability Prediction Model for Secure Routing Protocol Using GA and TLBO for Implementation of Black Hole Attacks in WSN,” Journal of Circuits, Systems and Computers, vol. 30, no. 6, 2021, DOI: 10.1142/S0218126621500985.
  • M. A. Rizvi, S. Moontaha, K. A. Trisha, S. T. Cynthia, and S. Ripon, "Data mining approach to analyzing intrusion detection of a wireless sensor network," Indonesian Journal of Electrical Engineering and Computer Science, vol. 21, no. 1, 2021, DOI: 10.11591/ijeecs.v21.i1.pp516-523.

Abstract Views: 149

PDF Views: 1




  • Minimalistic Error via Clibat Algorithm for Attack-Defence Model on Wireless Sensor Networks (WSN)

Abstract Views: 149  |  PDF Views: 1

Authors

K Abdul Basith
Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh,, India
T.N. Shankar
Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh,, India

Abstract


Wireless Sensor Networks have become the recent trend to effectively solve the problem in medical fields, primarily in agriculture and others in IoT monitoring. The expenses for ease of use in current domains are particular for cost and energy-effective solutions estimating different for different attack types on Wireless Sensor Network designs. Even though the energy, Routing problems are effectively solved, due to its wireless operative effects, the performance, such as speed and attackers, are reduced due to unevaded attacks. Hence, reducing this problem with energy-featured node optimization, network rate, and unevaded or random attack types like wormholes and black holes would implicate a significant problem in real-time modelling. On this basis, we postulate a solution analysis with the CLIBAT algorithm that implicates different possibilities and its probabilistic approaches, considering a proposed hybrid routing protocol on novel attack and defence algorithms to reduce the attack pattern with Wormhole and black hole attacks. In this perspective, an attack and defence pattern with an intuitive approach is implemented via the Improved Conditionally Expected Criteria feature to emphasize the type of attack (Wormhole or black hole attacks). Also, the Defense algorithm on improved sigmoid function on node characteristics is utilized to implicate with minimum distance formulations on the defense model effectively, MATLAB simulations with the solutions on WSN with CLIBAT algorithm inclusive of attack and defences are effectively removed.

Keywords


Attacks, DDoS, Firefly, Leach, Conditional Logistic Intuitive BAT Algorithm (CLIBAT), Wireless Sensor Networks (WSN), Distributed Energy-Efficient Clustering (DEEC), Least Probability Gradient algorithm (LPA), Intuitive Cumulative Expected Conditionality (ICEC), Multi-Point Route (MPR).

References





DOI: https://doi.org/10.22247/ijcna%2F2022%2F217700