Fuzzy based inference system with ensemble classification based intrusion detection system in MANET

Abstract

Mobile ad hoc networks (MANET) have become one of the hottest research areas in computer science, including in military and civilian applications. Such applications have formed a variety of security threats, particularly in unattended environments. An Intrusion detection system (IDS) must be in place to ensure the security and reliability of MANET services. These IDS must be compatible with the characteristics of MANETs and competent in discovering the biggest number of potential security threats. In this work, a specialized dataset for MANET is implemented to identify and classify three types of Denial of Service (DoS) attacks: Blackhole, Grayhole and Flooding Attack. This work utilized a cluster-based routing algorithm (CBRA) in MANET.A simulation to gather data, then processed to create eight attributes for creating a specialized dataset using Java. Mamdani fuzzy-based inference system (MFIS) is used to create dataset labelling. Furthermore, an ensemble classification technique is trained on the dataset to discover and classify three types of attacks. The proposed ensemble classification has six base classifiers, namely, C4.5, Fuzzy Unordered Rule Induction Algorithm (FURIA), Multilayer Perceptron (MLP), Multinomial Logistic Regression (MLR), Naive Bayes (NB) and Support Vector Machine (SVM). The experimental results demonstrate that MFIS with the Ensemble classification technique enables an enhancing security in MANET’s by modeling the interactions among a malicious node with number of legitimate nodes. This is suitable for future works on multilayer security problem in MANET.

Keywords

Mobile ad hoc networks (MANET)intrusion detection system (IDS)cluster-based routing algorithm (CBRA)mamdani fuzzy-based inference system (MFIS)

Get full access to this article

View all access options for this article.

References

Liu

, Yan

and Pedrycz

, Data collection for attack detection and security measurement in mobile ad hoc networks: A survey, Journal of Network and Computer Applications 105 (2018), 105–122.

Dhief

Al-F.T.

, Sabri

, Fouad

, Latiff

N.A.

and Albader

M.A.A.

, A review of forest fire surveillance technologies: Mobile ad-hoc network routing protocols perspective, Journal of King Saud University-Computer and Information Sciences 31(2) (2019), 135–146.

Ichaba

, Security threats and solutions in mobile ad hoc networks; a review, Universal J Commun Netw 6(2) (2018), 7–17.

Meitei

M.G.

and Sen

, A study on a few approaches to counter security breaches in MANETs, In Advances in Communication, Cloud, and Big Data, (pp. 105–116), Springer, Singapore (2019).

Mihret

E.T.

, Intrusion Detection System-IDS, Am J Compt Sci Inform Technol 9(8) (2021), 108.

Panwar

, Rao

D.S.

and Sriram

, Combined Approach for Detection and Prevention of Flooding and Blackhole Attack in MANET, International Journal of Engineering and Applied Sciences 4(4), 257483.

Bhuvaneswari

and Ramachandran

, Denial of service attack solution in OLSR based manet by various fictitious nodes, Cluster Computing 22(5) (2019), 12689–12699.

Ahmad

, Alleviating Malicious Insider Attacks in MANET using a Multipath On-demand Security Mechanism, International Journal of Computer Network & Information Security 10(6) (2018).

Krishnan

R.S.

, Julie

E.G.

, Robinson

Y.H.

, Kumar

, Son

L.H.

, Tuan

T.A.

and Long

H.V.

, Modified zone-based intrusion detection system for security enhancement in mobile ad hoc networks, Wireless Networks 26(2) (2020), 1275–1289.

10.

Suganya

, Kumar

S.M.

and Vigneshwari

A.G.

, A Study on Discovering Malicious Nodes on MANET through Secure Intrusion Detection,, Annals of the Romanian Society for Cell Biology (2021), 2444–2452.

11.

Nagalakshmi

T.J.

and Raja

P.C.

, Detecting Packet Dropping Malicious Nodes in Manet Using SVM, Indian Journal of Public Health Research & Development 9(8) (2018).

12.

Panda

and Pattanayak

B.K.

, Energy-aware detection and prevention of black hole attack in MANET, International Journal of Engineering and Technology (UAE) 7(2.6) (2018), 135–140.

13.

Abdel-Azim

, Salah

H.E.D.

and Eissa

M.E.

, IDS Against Blackhole Attack for MANET, Int J Netw Secur 20(3) (2018), 585–592.

14.

Naveena

, Senthilkumar

and Manikandan

, Analysis and countermeasures of blackhole attack in manet by employing trust-based routing, In 2020 6th international conference on advanced computing and communication systems (ICACCS), (pp. 1222–1227), IEEE (2020, March).

15.

Gurung

and Chauhan

, Performance analysis of blackhole attack mitigation protocols under grayhole attacks in MANET, Wireless Networks 25(3) (2019), 975–988.

16.

Zardari

Z. Ali

, He

, Zhu

, Mohammadani

K.H.

, Pathan

M.S.

, Hussain

M.I.

and Memon

M.Q.

, A dual attack detection technique to identify black and grayhole attacks using an intrusion detection system and a connected dominating set in MANETs: future Internet, 11(3) (2019), 61.

17.

Khan

A.U.

, Chawhan

M.D.

, Mushrif

M.M.

and Neole

, Performance Analysis of Adhoc On-demand Distance Vector Protocol under the influence of Black-Hole, Gray-Hole and Wormhole Attacks in Mobile Adhoc Network, In 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), (pp. 238–243), IEEE, (2021, May).

18.

Narayanan

S. Sankara

and Murugaboopathi

, Modified secure AODV protocol to prevent wormhole attacks in MANET, Concurrency and Computation: Practice and Experience 32(4) (2020), e5017.

19.

Luong

N.T.

, Vo

T.T.

and Hoang

, FAPRP: A machine learning approach to flooding attacks prevention routing protocol in mobile ad hoc networks, Wireless Communications and Mobile Computing 2019 (2019).

20.

Mohammadi

and Ghaffari

, Defending Against Flooding Attacks in Mobile Ad-Hoc Networks Based on Statistical Analysis, Wireless Personal Communications 106(2) (2019).

21.

Gurung

and Chauhan

, A novel approach for mitigating route request flooding attacks in MANET, Wireless Networks 24(8) (2018), 2899–2914.

22.

Thanuja

and Umamakeswari

, Unethical network attack detection and prevention using fuzzy based decision system in mobile Ad-hoc networks, Journal of Electrical Engineering and Technology 13(5) (2018), 2086–2098.

23.

Chaudhary

, Mamdani and sugeno fuzzy inference systems’ comparison for detection of packet dropping attack in mobile ad hoc networks, In Emerging Technologies in Data Mining and Information Security: Proceedings of IEMIS 2018, Volume 3 (pp. 805–811), Springer Singapore (2019).