Abstract
This paper suggests how fuzzy logic can be applied to route traffic between two nodes in a wireless network. The main focus of this paper is also to use fuzzy logic, implemented with genetic algorithm, for intrusion detection in the network. As wireless network has unparalleled mobility, it is important for the nodes to re-arrange themselves frequently. Not only this, there are multiple nodes between source and destination that helps to route packets to the destination. So every node acts as a router. There may be delay and packet loss during the transit. Fuzzy logic helps to reduce these drawbacks by clearly configuring the route. Apart from this, fuzzy logic can be implemented using GA for intrusion detection in the network. Intrusion detection broadly deals with how to be aware of network-based and Internet attacks. Firewalls could not properly handle nefarious attacks. This has proved to be very robust and effective with 80% success rate. This paper also suggests how fuzzy logic can be used to address some of the cybersecurity challenges.
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References
Gomez J, Dasgupta D (2005) Evolving fuzzy classifiers for intrusion detection. In: Proceedings of the IEEE
Shabadkar R, Reddy SSS, Vijay Bhaskar Reddy V (2017) Sensor based vehicle motion recognition in portable computing machines for wireless networks. Int J Mech Eng Technol (IJMET) 8(12):465–468
Xia T, Qu G, Hariri S, Yousif M (2005) An efficient network intrusion detection method based on information theory and genetic algorithm. In: Proceedings of the 24th IEEE international performance computing and communications conference (IPCCC’05), Phoenix, AZ, USA
Reddy SSS, Mamatha C, Chatterjee P, Nagarjuana Reddy S (2017) Contemporary supply chain and inventory data management using data analytics. Int J Mech Eng Technol (IJMET) 8(12): 290–294
Yao, JT, Zhao SL, Saxton LV (2005) A study on fuzzy intrusion detection. In: Proceedings of the data mining, intrusion detection, information assurance, and data networks security, SPIE, vol. 5812. Orlando, Florida, USA, pp. 23–30
Reddy SS, Shabadkar R, Vijaya Bhaskar Reddy Y, Ranjith Kumar A (2017) Sensor based spontaneous water level recognition in smart cities environment. Int J Civ Eng Technol (IJCIET) 8(8)
Chatterjee P, Reddy SSS, Prasada Reddy GS, Hemanth PV (2012) Train time scheduling using genetic algorithm. Int J Civ Eng Technol (IJCIET) 8(12): 410–413
Reddy SSS, Nagarjuna Reddy S, Subba Reddy N, Srinivasa Rao M (2017) Framework for efficient authentication mechanism for genuine user’s payload transmission in cybernetic locations. Int J Civ Eng Technol (IJCIET) 8(8): 898–904
Neog TJ, Sut DK (2011) Complement of an extended fuzzy set. Int J Comput Appl 29(3): 39–45
Shahabadkar R, Reddy SSS, Manjunath C, Channabasava U, Shahabadkar KR (2017) Secure framework of authentication mechanism over cloud environment, Springer International Publishing AG 2017. In: Silhavy R et al. (ed), Software engineering trends and techniques in intelligent systems, advances in intelligent systems and computing, vol 575. pp 285–291
Reddy SSS, Prasada Reddy GS, Balakrishna B, Sirisha N (2017) Drone control vehicle for prospective protected shipping. Int J Civ Eng Technol (IJCIET) 8(8): 905–909
Lu W, Traore I (2004) Detecting new forms of network intrusion using genetic programming. Comput Intel 20(3), 475–494 (BlackIll Publishing, Malden)
Reddy SSS, Jabbar MA, Shahabadkar R (2017) Framework for safe direction-finding for protected multimedia contents in cloud computing environment. International Conference on Cognitive Informatics & Soft Computing (CISC-2017)
Pushpalatha N, Reddy SSS (2017) Towards an extensible web usage mining framework for actionable knowledge. In: Proceedings in international conference on inventive communication and computational technologies (ICICCT), pp 39–40
Sung A, Mukkamala S (2003) Identifying important features for intrusion detection using support vector machines and neural networks. In: Symposium on applications and the internet, pp 209–216
Reddy SSS, Shabadkar R, Mamatha C, Chatterjee P (2017) Protection Policy in networked locations using machine learning and data science approach, ICATCCT-2017, pp 237–240
Reddy SSS, Reddy LSS, Khanna V, Lavanya A (2008) Advanced techniques for scientific datawarehouses. In: Proceedings of IEEE international conference on advanced computer control, pp 576–580
Kumar S, Spafford E (1995) A software architecture to support misuse intrusion detection. In: The 18th national information security conference, pp 194–204
Ilgun K, Kemmerer R, Porras PA (1995) State transition analysis: a rule-based intrusion detection approach. IEEE Trans Softw Eng 181–199
Bobor V (2006) Efficient intrusion detection system architecture based on neural networks and genetic algorithms. Department of Computer and Systems Sciences, Stockholm University/Royal Institute of Technology, KTH/DSV
KDD-CUP (1999) Task description http://kdd.ics.uci.edu/databases/kddcup99/task.html
KDD Cup (1999) Tasks http://www.kdd.org/kddcup/index.php?section=1999&method=task
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Sai Satyanarayana Reddy, S., Chatterjee, P., Mamatha, C. (2019). Intrusion Detection in Wireless Network Using Fuzzy Logic Implemented with Genetic Algorithm. In: Peng, SL., Dey, N., Bundele, M. (eds) Computing and Network Sustainability. Lecture Notes in Networks and Systems, vol 75. Springer, Singapore. https://doi.org/10.1007/978-981-13-7150-9_45
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DOI: https://doi.org/10.1007/978-981-13-7150-9_45
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