Abstract
There are various attack which is possible in the network, it may be from externally or internally. But internal attacks are more dangerous than external. So, my mainly concern upon Wireless LAN and Wired LAN attacks which occurs internally. There are various Signature based tools, IDS/IPS (Intrusion detection or prevention system) available now-a-days for detecting these types of attacks but these are not sufficient due to high false alarm rate. So, I detect these types of attacks with three ways: through Wireshark, with signature based tools (Snort and Kismet) and with machine learning tools (WEKA). In wired LAN attack, my mainly concern on PING scan or PING flood, NMAP scan (portsweep) and ARP spoofing attacks. In wireless LAN attacks, I take care of Deauthentication attack, Disassociation attack and Access point (AP) spoofing attack. Signature based tools detect these types of the attacks based on the stored signature and timing threshold. But machine learning tools take several different feature to detect these types of attacks with more accuracy and low false positive rate.
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References
Mitchell, Changhua He John C.: Security Analysis and Improvements for IEEE 802.11 i, In: 12th annual network and distributed system security symposium, NDSS05 (2005).
Farooq, Taimur, David Llewellyn-Jones, and Madjid M.: MAC Layer DoS Attacks in IEEE 802.11 Networks, In: The 11th Annual Conference on the Convergence of Telecommunications, Networking and Broadcasting, PGNet, Liverpool, UK, (2010).
Ratnayake, Deepthi N., et al.: An intelligent approach to detect probe request attacks in IEEE 802.11 networks, In: Engineering Applications of Neural Networks, Springer Berlin Heidelberg, pp. 372–381, (2011).
Bellardo, John, and Stefan S.: 802.11 Denial-of-Service Attacks: Real Vulnerabilities and Practical Solutions, USENIX security, (2003).
Bernaschi, Massimo, Francesco Ferreri, and Leonardo V.: Access points vulnerabilities to DoS attacks in 802.11 networks, Wireless Networks14.2, pp. 159–169, (2008).
B. Vani, L.: Framework to Detect and Prevent Medium Access Control Layer Denial of Service Attacks in WLAN, International Journal of Computer Networks and Wireless Communications, ISSN: 2250-3501 Vol .3, No 2, April (2013).
Agarwal, Mohini, Santosh Biswas, and Sukumar N.: Detection of Deauthentication Denial of Service attack in 802.11 networks, India Conference, INDICON, IEEE, (2013).
Noman, Haitham Ameen, Shahidan M. Abdullah, and Haydar Imad M.: An Automated Approach to Detect Deauthentication and Disassociation Dos Attacks on Wireless 802.11 Networks, In: International Journal of Computer Science Issues, IJCSI 12.4 pp. 107 (2015).
Arockiam, L., and B. Vani: A Survey of Denial of Service Attacks and its Counter measures on Wireless Network, International Journal on Computer Science and Engineering Vol. 02, No. 05, pp. 1563–1571 (2011).
Yusuf B.: LAYER 2 ATTACKS & MITIGATION TECHNIQUES. http://www.sanog.org/resources/sanog7/yusuf-L2-attack-mitigation.pdf (2005).
OConnor, T. J.: Detecting and responding to data link layer attacks, SANS Institute InfoSec Reading Room, Oct 13 (2010).
Tao, Kai, Jing Li, and Srinivas S.: Wise guard-MAC address spoofing detection system for wireless LANs, Second International Conference on Security and Cryptography, Barcelona, Spain, pp. 140–147 (2007).
Korck, Michal, Jaroslav Lmer, and Frantisek J.: Intrusion Prevention/Intrusion Detection System (IPS/IDS) For Wifi Networks, International Journal of Computer Networks and Communications 6.4, pp. 77, (2014).
Nevlud, Pavel, et al.: Anomaly-based Network Intrusion Detection Methods, Advances in Electrical and Electronic Engineering 11.6, pp. 468, (2013).
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Kaur, J. (2018). Wired LAN and Wireless LAN Attack Detection Using Signature Based and Machine Learning Tools. In: Perez, G., Mishra, K., Tiwari, S., Trivedi, M. (eds) Networking Communication and Data Knowledge Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 3. Springer, Singapore. https://doi.org/10.1007/978-981-10-4585-1_2
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