Anomaly-based Detection of Blackhole Attacks in WSN and MANET Utilizing Quantum-metaheuristic algorithms | ||
| Journal of Communication Engineering | ||
| مقاله 7، دوره 9، شماره 1 - شماره پیاپی 18، فروردین 2020، صفحه 77-92 اصل مقاله (1.54 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22070/jce.2020.5466.1160 | ||
| نویسندگان | ||
| Mirsaeid Hosseini Shirvani* 1؛ Amir Akbarifar2 | ||
| 1Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran | ||
| 2Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran. | ||
| چکیده | ||
| Wireless sensor network (WSN) comprises various distributed nodes that are physically separated. Nodes are constantly applying for sensing their environment. If the information sensitivity coefficient is very high, data should be conveyed continually and also with confidentially. WSNs have many vulnerability features because of data transferring on the open air, self-organization without reformed structure, bounded range of sources and memory, and limited computing capabilities. Therefore, the implementation of security protocols in WSN is inescapable. According to the resemblance between WSN and biotic reaction to the real menace in nature, bio-inspired approaches have variant rules in computer network investigations. In this paper, we exploited an ant colony optimization (ACO) algorithm based on Ad-hoc On-Demand Distance Vector (AODV) protocol for detection of black hole attacks. Finally, the Grover quantum metaheuristic algorithm is applied to optimize attack paths detection. The results gained from extensive simulations in WSN proved that the proposed approach is capable of improving some fundamental network parameters such as throughput, end-to-end delay, and packet delivery ratio in comparison with other approaches. | ||
| کلیدواژهها | ||
| Security؛ Meta-heuristic Algorithm؛ Blackhole Attack؛ Ant Colony Optimization؛ Quantum Algorithm | ||
| مراجع | ||
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1- Azimi, C., Pahl, and M. Hosseini Shirvani, “Particle Swarm Optimization for Performance Management in Multi-Cluster IoT Edge Architecture,” 10th international Conference on Cloud Computing and Service Sciences (CLOSER), pp. 1-10, May 2020.
2- Hosseini Shirvani and S. Ehsani, “A novel sleep/wakeup power management in wireless sensor network: A Fuzzy TOPSIS approach,” Journal of Advances in Computer Research, vol. 8, no. 4, pp. 95-105, Autumn 2017.
3- Rad, M. Reshadi, and A. Khademzadeh, “Design of a Low-Latency Router Based on Virtual Output Queuing and Bypass Channels for Wireless Network-on-Chip,” Journal of Communication Engineering, vol. 8, no. 2, pp. 179-196, Summer & Autumn 2019. doi: 10.22070/jce.2019.3882.1117.
4- Yarinezhad and A. Sarabi, “MLCA: A Multi-Level Clustering Algorithm for Routing in Wireless Sensor Networks,” Journal of Communication Engineering, vol. 8, no. 2, pp. 249-265, Summer & Autumn 2019. doi: 10.22070/jce.2020.4009.1124.
5- Sadeghizadeh, and O. Marouzi, “Securing Cluster-heads in Wireless Sensor Networks by a Hybrid Intrusion Detection System Based on Data Mining,” Journal of Communication Engineering, vol. 8, no. 1, pp. 1-19, Winter & Autumn 2019. doi: 10.22070/jce.2019.3687.1105.
6-Hosseini Shirvani, N. Amirsoleimani, S. Salimpour and A. Azab, “Multi-criteria task scheduling in distributed systems based on fuzzy TOPSIS,” IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1-4, April 2017.
7- S. Tanenbaum, Distributed operating systems, 3rd Edition, Pearson Education India, 1995
8- S. Iyengar and R. R. Brooks, Distributed sensor networks: sensor networking and applications, CRC press, 2016
9- S. Mohanty, S. Panigrahi, N. Sarma and S. S. Satapathy, “Security issues in wireless sensor network data gathering protocols: a survey,” Journal of Theoretical & Applied Information Technology, vol. 13, no. 1, pp. 14-27, March 2010.
10- Kazemitabar Amirkolaei, Enhancing Bio-inspired Intrusion Response in Ad-hoc Networks, Edinburgh Napier University, 2013.
11- Bahrami and M.S. Hosseini Shirvani, “Prediction and Diagnosis of Heart Disease by Datamining Techniques,” Journal of Multidisciplinary Engineering Science and Technology”, vol. 2, no. 2, pp. 164-168, Feb. 2015.
12- Maleh, and A. Ezzati, “A review of security attacks and Intrusion Detection Schemes in Wireless Sensor Networks,” International Journal of Wireless and Mobile Networks, vol. 5, no. 6, pp.1-12, Dec. 2013.
13- Mokaripoor, M. Hosseini Shirvani, “A state of the art survey on DVFS techniques in Cloud Computing Environment,” J. Multidiscip. Eng. Sci. Technol, vol. 3, no. 5, pp. 4740-4743, May 2016.
14- Chaitanya and A. Ghosh, “Analysis of Denial-of-Service attacks on Wireless Sensor networks using simulation,” Middlesex University, 1-13, 2010.
15- Kaur and J. Malhotra, “Review on Security Issues and Attacks in Wireless Sensor Networks,” International Journal of Future Generation Communication and Networking, vol. 8, no. 4, pp. 81-88, Sep. 2015.
16- Akbarifar and H. Abbasi Neyshabouri, "Analysis of Security in Wireless Sensor Networks," Paper presented at the 11 Symposium on Advances in Science & Technology, Mashhad, IRAN (in persian), 2017.
17- Akbarifar and H. Abbasi Neyshabouri, "A survey on Intrusion Detection System in Wireless Sensor Networks," Paper presented at the 11 Symposium on Advances in Science & Technology, Mashhad, IRAN, 2017.
18- Haghighi, K. Mizanian and G. Mirjalily, ” Improved MCBDS for defending against gray hole and black hole attacks in MANETs,, Advances in Science and Technology Research Journal, vol. 10, no. 30, June 2016.
19- Somasundaram, ”An Effective CBHDAP Protocol for Black Hole Attack Detection in MANET,” Indian Journal of Science and Technology, vol. 9, no. 36, Sept. 2016.
20- Nitnaware and A. Thakur, “Black hole attack detection and prevention strategy in DYMO for MANET,” Paper presented at the Signal Processing and Integrated Networks (SPIN), 3rd International Conference on, 2016.
21- A. Khan, M. Imran, H. Abbas, and M.H. Durad, “A detection and prevention system against collaborative attacks in Mobile Ad hoc Networks,” Future Generation Computer Systems, vol. 68, pp. 416-427, March 2017.
22- Hosseini Shirvani, "A new Shuffled Genetic-based Task Scheduling Algorithm in Heterogeneous Distributed Systems," Journal of Advances in Computer Research, vol. 9, no. 4, pp. 19-36, 2018. http://jacr.iausari.ac.ir/article_660143.html.
23-Hosseini Shirvani and A. Babazadeh Gorji, "Optimisation of automatic web services composition using genetic algorithm," Int. J. Cloud Computing, vol. 9, no. 2, pp. 1-15, 2020.
24-Hosseini Shirvani, "A hybrid meta-heuristic algorithm for scientific workflow scheduling in heterogeneous distributed computing systems," Engineering Application of Artificial Intelligence, vol. 90, pp. 1-20, April 2020.
25- Javadian Kootanaee, A. Poor Aghajan and M. Hosseini Shirvani, "A hybrid model based on machine learning and genetic algorithm for detecting fraud in financial statements," Journal of Optimization in Industrial Engineering, vol. 14, no. 1, pp:1-10, 2020. doi: 10.22094/joie.2020.1877455.1685.
26- Razavi, F. Zabihi and M. Hosseini Shirvani, "Multi-layer Perceptron Neural Network Training Based on Improved of Stud GA," Journal of Advances in Computer Research, vol. 7, no. 3, pp. 1-14, 2016.
27- Hosseini Shirvani, A.M. Rahmani and A. Sahafi, "An iterative mathematical decision model for cloud migration: A cost and security risk approach," Software: Practice and Experience, vol. 48, no. 3, pp. 449-485, 2018. https://doi.org/10.1002/spe.2528.
28-Hosseini Shirvani, "Web Service Composition in multi-cloud environment: A bi-objective genetic optimization algorithm," In 2018 Innovations in Intelligent Systems and Applications (INISTA), pp. 1-6, 2018. IEEE. https://doi.org/10.1109/INISTA.2018.8466267.
29-Hosseini Shirvani, "Bi-objective web service composition problem in multi-cloud environment: a bi-objective time-varying particle swarm optimisation algorithm," Journal of Experimental & Theoretical Artificial Intelligence, Feb. 2020. DOI:10.1080/0952813X.2020.1725652.
30- Farzai , M. Hosseini Shirvani and M. Rabbani , "Multi-Objective Communication-Aware Optimization for Virtual Machine Placement in Cloud Datacenters," Sustainable Computing: Informatics and Systems, no. 28, Dec. 2020, doi: https://doi.org/10.1016/j.suscom.2020.100374.
31- Binitha and S. S. Sathya, "A survey of bio inspired optimization algorithms," International Journal of Soft Computing and Engineering, vol. 2, no. 2, pp. 137-151, 2012.
32- Fu, K. Zheng, "Biologically inspired anomaly detection for hierarchical wireless sensor networks," Journal of Networks, vol. 7, no. 8, pp. 1214-1219, Aug. 2012.
33- Kolias, G. Kambourakis and K. Maragoudakis, "Swarm intelligence in intrusion detection: A survey," Computers & Security, vol. 30, no. 8, pp. 625-642, Nov. 2011.
34- Meisel, V. Pappas and L. Zhang, "A taxonomy of biologically inspired research in computer networking," Computer Networks, vol. 54, no.6, pp: 901-916, April 2010.
35- M. Sekhar, S. Abhijth and C. Janiwarad, "A survey on bio-inspired security in wireless sensor networks," International Journal of Research in Engineering and Technology, 2015.
36- S. Soliman, R. Bahgat, A. Adly, "Associative classification using a bio-inspired algorithm," Proceedings of the Tenth Australasian Data Mining Conference-Volume 134, Australian Computer Society, Inc., December 2012.
37-https://www.unb.ca/cic/datasets/nsl.html, [Accessed 15 Sep. 2020].
38-Dorigo, "Optimization, Learning and Natural Algorithms," PhD dissertation, Dep. of Comp. Politecnico di Milano, Italy, 1992.
39-Bhimla, "Ant Based Black Hole Detection and Protection in MANET," North South University; Mr. Sunil Gupta, North South University.
40-Khemariya, "An Efficient Algorithm for Detection of Black Hole Attack in AODV based MANET", Int. journal of com. applications, vol. 66, no. 18, pp. 18-24, March 2013.
41-Ullah, "Analysis of Black Hole Attack on MANETs Using Different MANET Routing Protocol", Master Thesis, 2010.
42-https://en.wikipedia.org/wiki/Grover%27s_algorithm, [Accessed 15 Sep. 2020].
43-Butun, S. D. Morgera and R. Sankar, "A survey of intrusion detection systems in wireless sensor networks", Communications Surveys & Tutorials, IEEE, vol. 16, no. 1, pp. 266-282, May 2014.
44-IBM 5 qubits qunatum computer. "www.research.ibm.com/quantum", [Accessed 27 February 2017].
45-Libquantum,” http://www.libquantum.de/api/1.1/index.html", [Accessed 27 February 2017].
46-Farzai, M. Hosseini Shirvani, M. Rabbani, "Communication-Aware Traffic Stream Optimization for Virtual Machine Placement in Cloud Datacenters with VL2 topology", Journal of Advances in Computer Research, vol. 11, no. 3, pp. 1-21, Sep. 2020.
47-R. Amin and M. Hosseini Shirvani, “Evaluation of scheduling solutions in parallel processing using DEA FDH model,” J. Ind. Eng. Int, vol. 5, no. 9, pp. 58–62, June 2009.
48-Hosseini Shirvani, “Evaluating of Feasible Solutions on Parallel Scheduling Tasks with DEA Decision Maker,” J. Adv. Comput. Res., vol. 6, pp. 109–115, 2015.
49-Hosseinzadeh, M. Hosseini Shirvani, "Optimizing energy consumption in clouds by using genetic algorithm", Journal of multidisciplinary engineering science and technology, vol. 2, no. 6, pp. 1431-1434, June 2015.
50-stackoverflow.com/questions [Accessed 15 Sep. 2020].
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