(Publisher of Peer Reviewed Open Access Journals)

International Journal of Advanced Technology and Engineering Exploration (IJATEE)

ISSN (Print):2394-5443    ISSN (Online):2394-7454
Volume-10 Issue-104 July-2023
Full-Text PDF
Paper Title : Investigating the performance vulnerability of AODV protocol of IoT network under SYN-flood attack
Author Name : Abhijit Biswas, Rabinder Kumar Prasad, Abhijit Boruah and Sudipta Majumder
Abstract :

Wireless technology is utilized by various Internet of Things (IoT) devices, such as smartphones, drones, and cameras, to establish multiple inter-device connections simultaneously. The flexibility of wireless networks allows users to create IoT-based ad-hoc networks on demand. This connectivity enables linking hundreds to thousands of people, leading to significant enhancements in productivity and profitability. However, the flexibility of the IoT network also introduces a range of threats that require attention. The primary research methodology employed in this study was simulation to investigate and identify existing vulnerabilities or loopholes in the transport layer of IoT networks that use the ad-hoc on-demand distance vector (AODV) routing protocol. The simulation software utilized was NetSim 12.02, which is based on the Institute of Electrical and Electronics Engineers (IEEE) 802.15.4 standard. We conducted simulations of synchronization (SYN) flood attacks on IoT networks by exploiting vulnerabilities in the underlying protocol. The tests and data collection for this study were conducted using the simulation software. The research demonstrates that the transport layer of the IoT network, when utilizing the AODV protocol, can be compromised to launch a SYN-flood attack, resulting in severe performance degradation of the system. The results demonstrate that SYN-flood attack nodes result in a decrease in throughput ranging from 9.1% to 17.79%, an increase in delay ranging from 1.08% to 3.26%, and an increase in network jitter ranging from 15.4% to 22.88%. These findings can assist IoT network administrators in better planning and implementing preventive measures against SYN flood attacks on IoT networks that utilize the AODV protocol.

Keywords : IoT network, AODV, SYN-flood, Intrusion, Half-open connections, Throughput, Delay, Jitter, Transmission overhead, Idle state battery life.
Cite this article : Biswas A, Prasad RK, Boruah A, Majumder S. Investigating the performance vulnerability of AODV protocol of IoT network under SYN-flood attack. International Journal of Advanced Technology and Engineering Exploration. 2023; 10(104):906-929. DOI:10.19101/IJATEE.2022.10100538.
References :
[1]Jha AV, Appasani B, Ghazali AN. Performance evaluation of network layer routing protocols on wireless sensor networks. In international conference on communication and electronics systems 2019 (pp. 1862-5). IEEE.
[Crossref] [Google Scholar]
[2]Tiwary A, Mahato M, Chidar A, Chandrol MK, Shrivastava M, Tripathi M. Internet of things (IoT): research, architectures and applications. International Journal on Future Revolution in Computer Science & Communication Engineering. 2018; 4(3):23-7.
[Google Scholar]
[3]González-zamar MD, Abad-segura E, Vázquez-cano E, López-meneses E. IoT technology applications-based smart cities: research analysis. Electronics. 2020; 9(8):1-36.
[Google Scholar]
[4]Raykarmakar K, Harrison S, Ghata S, Das A. Medical internet of things: techniques, practices and applications. Medical Internet of Things: Techniques, Practices and Applications. 2021.
[Google Scholar]
[5]Pipkin DL. Halting the hacker: a practical guide to computer security. Prentice Hall Professional; 2003.
[Google Scholar]
[6]Mavaluru D, Enduri MK, Thiyagarajan A, Anamalamudi S, Srinivasan K, Carie CA, et al. An AI fuzzy clustering-based routing protocol for vehicular image recognition in vehicular ad hoc IoT networks. Soft Computing. 2023:1-12.
[Crossref] [Google Scholar]
[7]Nourildean SW, Hassib MD, Abd MY. AD-Hoc routing protocols in WSN-WiFi based IoT in smart home. In 15th international conference on developments in esystems engineering 2023 (pp. 82-7). IEEE.
[Crossref] [Google Scholar]
[8]Adarbah HY, Moghadam MF, Maata RL, Mohajerzadeh A, Al-badi AH. Security challenges of selective forwarding attack and design a secure ECDH-based authentication protocol to improve RPL security. IEEE Access. 2022; 11:11268-80.
[Crossref] [Google Scholar]
[9]Prasath N, Sreemathy J. Optimized dynamic source routing protocol for MANETs. Cluster Computing. 2019; 22(Suppl 5):12397-409.
[Crossref] [Google Scholar]
[10]Darville C, Höfner P, Ivankovic F, Pam A. Advanced models for the OSPF routing protocol. Electronic Proceedings in Theoretical Computer Science, EPTCS. 2022; 355:3-26.
[Crossref] [Google Scholar]
[11]Kim D, Andalibi V, Camp J. Protecting IoT devices through localized detection of BGP hijacks for individual things. In security and privacy workshops 2021(pp. 260-7). IEEE.
[Crossref] [Google Scholar]
[12]Mistareehi H, Salameh HB, Manivannan D. An on-board hardware implementation of AODV routing protocol in VANET: design and experimental evaluation. In international conference on internet of things: systems, management and security 2022 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[13]Bertino E, Martino L, Paci F, Squicciarini A, Bertino E, Martino LD, et al. Web services threats, vulnerabilities, and countermeasures. Security for Web Services and Service-Oriented Architectures. 2010:25-44.
[Crossref] [Google Scholar]
[14]Bashir AK, Rawat DB, Wu J, Imran MA. Guest editorial security, reliability, and safety in IoT-enabled maritime transportation systems. IEEE Transactions on Intelligent Transportation Systems. 2023; 24(2):2275-81.
[Crossref] [Google Scholar]
[15]Swamy SN, Kota SR. An empirical study on system level aspects of internet of things (IoT). IEEE Access. 2020; 8:188082-134.
[Crossref] [Google Scholar]
[16]Ghanti S, Naik GM. Defense techniques of SYN flood attack characterization and comparisons. International Journal of Network Security. 2018; 20(4):721-9.
[Google Scholar]
[17]Fan CI, Wang JH, Shie CH, Tsai YL. Software-defined networking integrated with cloud native and proxy mechanism: detection and mitigation system for TCP SYN flooding attack. In 17th international conference on ubiquitous information management and communication 2023 (pp. 1-8). IEEE.
[Crossref] [Google Scholar]
[18]Sheibani M, Konur S, Awan I. DDoS attack detection and mitigation in software-defined networking-based 5G mobile networks with multiple controllers. In 9th international conference on future internet of things and cloud 2022 (pp. 32-9). IEEE.
[Google Scholar]
[19]Kumar BS, Gowda KK. Detection and prevention of TCP SYN flooding attack in WSN using protocol dependent detection and classification system. In international conference on data science and information system 2022 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[20]Kumar R, Lal SP, Sharma A. Detecting TCP SYN flood attack in the cloud. Journal of Software. 2017; 12(7):493-506.
[Google Scholar]
[21]Zrelli A, Khlaifi H, Ezzedine T. Performance evaluation of AODV and OAODV for several WSN/IoT applications. In international conference on software, telecommunications and computer networks 2019 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[22]Silva BN, Khan M, Han K. Internet of things: a comprehensive review of enabling technologies, architecture, and challenges. IETE Technical Review. 2018; 35(2):205-20.
[Crossref] [Google Scholar]
[23]Atzori L, Iera A, Morabito G. The internet of things: a survey. Computer Networks. 2010; 54(15):2787-805.
[Crossref] [Google Scholar]
[24]Li S, Xu LD, Zhao S. The internet of things: a survey. Information Systems Frontiers. 2015; 17:243-59.
[Crossref] [Google Scholar]
[25]Gubbi J, Buyya R, Marusic S, Palaniswami M. Internet of things (IoT): a vision, architectural elements, and future directions. Future Generation Computer Systems. 2013; 29(7):1645-60.
[Crossref] [Google Scholar]
[26]Butun I, Österberg P, Song H. Security of the internet of things: vulnerabilities, attacks, and countermeasures. IEEE Communications Surveys & Tutorials. 2019; 22(1):616-44.
[Crossref] [Google Scholar]
[27]Hassija V, Chamola V, Saxena V, Jain D, Goyal P, Sikdar B. A survey on IoT security: application areas, security threats, and solution architectures. IEEE Access. 2019; 7:82721-43.
[Crossref] [Google Scholar]
[28]Makhdoom I, Abolhasan M, Lipman J, Liu RP, Ni W. Anatomy of threats to the internet of things. IEEE Communications Surveys & Tutorials. 2018; 21(2):1636-75.
[Crossref] [Google Scholar]
[29]Khan F, Al-atawi AA, Alomari A, Alsirhani A, Alshahrani MM, Khan J, et al. Development of a model for spoofing attacks in internet of things. Mathematics. 2022; 10(19):1-16.
[Crossref] [Google Scholar]
[30]Fadele AA, Othman M, Hashem IA, Yaqoob I, Imran M, Shoaib M. A novel countermeasure technique for reactive jamming attack in internet of things. Multimedia Tools and Applications. 2019; 78:29899-920.
[Crossref] [Google Scholar]
[31]Ambika N. Tackling jamming attacks in IoT. Internet of Things (IoT) Concepts and Applications. 2020:153-65.
[Google Scholar]
[32]Sharma B, Sharma L, Lal C, Roy S. Anomaly based network intrusion detection for IoT attacks using deep learning technique. Computers and Electrical Engineering. 2023; 107:108626.
[Crossref] [Google Scholar]
[33]Namvar N, Saad W, Bahadori N, Kelley B. Jamming in the internet of things: a game-theoretic perspective. In global communications conference 2016 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[34]Lin J, Yu W, Zhang N, Yang X, Zhang H, Zhao W. A survey on internet of things: architecture, enabling technologies, security and privacy, and applications. IEEE Internet of Things Journal. 2017; 4(5):1125-42.
[Crossref] [Google Scholar]
[35]Inayat U, Zia MF, Mahmood S, Khalid HM, Benbouzid M. Learning-based methods for cyber attacks detection in IoT systems: a survey on methods, analysis, and future prospects. Electronics. 2022; 11(9):1-20.
[Crossref] [Google Scholar]
[36]Kumar A, Varadarajan V, Kumar A, Dadheech P, Choudhary SS, Kumar VA, et al. Black hole attack detection in vehicular ad-hoc network using secure AODV routing algorithm. Microprocessors and Microsystems. 2021; 80:103352.
[Crossref] [Google Scholar]
[37]Pelechrinis K, Iliofotou M, Krishnamurthy SV. Denial of service attacks in wireless networks: the case of jammers. IEEE Communications Surveys & Tutorials. 2010; 13(2):245-57.
[Crossref] [Google Scholar]
[38]Baig ZA, Sanguanpong S, Firdous SN, Nguyen TG, So-in C. Averaged dependence estimators for DoS attack detection in IoT networks. Future Generation Computer Systems. 2020; 102:198-209.
[Crossref] [Google Scholar]
[39]Gamundani AM. An impact review on internet of things attacks. In international conference on emerging trends in networks and computer communications 2015 (pp. 114-8). IEEE.
[Crossref] [Google Scholar]
[40]Balamurugan B, Biswas D. Security in network layer of IoT: possible measures to preclude. In security breaches and threat prevention in the internet of things 2017 (pp. 46-75). IGI Global.
[Crossref] [Google Scholar]
[41]Pundir S, Wazid M, Singh DP, Das AK, Rodrigues JJ, Park Y. Designing efficient sinkhole attack detection mechanism in edge-based IoT deployment. Sensors. 2020; 20(5):1-27.
[Crossref] [Google Scholar]
[42]Chugh K, Aboubaker L, Loo J. Case study of a black hole attack on LoWPAN-RPL. In the sixth international conference on emerging security information, systems and technologies 2012 (pp. 157-62).
[Google Scholar]
[43]Pongle P, Chavan G. Real time intrusion and wormhole attack detection in internet of things. International Journal of Computer Applications. 2015; 121(9):1-9.
[Google Scholar]
[44]Kamaleshwar T, Lakshminarayanan R, Teekaraman Y, Kuppusamy R, Radhakrishnan A. Self-adaptive framework for rectification and detection of black hole and wormhole attacks in 6lowpan. Wireless Communications and Mobile Computing. 2021; 2021:1-8.
[Crossref] [Google Scholar]
[45]Deogirikar J, Vidhate A. Security attacks in IoT: a survey. In international conference on IoT in social, mobile, analytics and cloud 2017 (pp. 32-7). IEEE.
[Crossref] [Google Scholar]
[46]Kaur B, Dadkhah S, Shoeleh F, Neto EC, Xiong P, Iqbal S, et al. Internet of things (IoT) security dataset evolution: challenges and future directions. Internet of Things. 2023:100780.
[Crossref] [Google Scholar]
[47]Moudoud H, Mlika Z, Khoukhi L, Cherkaoui S. Detection and prediction of FDI attacks in IoT systems via hidden Markov model. IEEE Transactions on Network Science and Engineering. 2022; 9(5):2978-90.
[Crossref] [Google Scholar]
[48]Aboelwafa MM, Seddik KG, Eldefrawy MH, Gadallah Y, Gidlund M. A machine-learning-based technique for false data injection attacks detection in industrial IoT. IEEE Internet of Things Journal. 2020; 7(9):8462-71.
[Crossref] [Google Scholar]
[49]Habib AA, Hasan MK, Alkhayyat A, Islam S, Sharma R, Alkwai LM. False data injection attack in smart grid cyber physical system: issues, challenges, and future direction. Computers and Electrical Engineering. 2023; 107:108638.
[Crossref] [Google Scholar]
[50]Tankard C. Advanced persistent threats and how to monitor and deter them. Network Security. 2011; 2011(8):16-9.
[Crossref] [Google Scholar]
[51]Mercy PP, Basil XS, Jose A, Kathrine GJ, Andrew J. Variants of crypto-jacking attacks and their detection techniques. In international conference on applications and techniques in information security 2022 (pp. 71-87). Singapore: Springer Nature Singapore.
[Crossref] [Google Scholar]
[52]Singh SK, Kumar S. Blockchain technology: introduction, integration, and security issues with IoT. Applications of Blockchain and Big IoT Systems: Digital Solutions for Diverse Industries. 2022.
[Google Scholar]
[53]Aziz Al Kabir M, Elmedany W, Sharif MS. Securing IoT devices against emerging security threats: challenges and mitigation techniques. Journal of Cyber Security Technology. 2023:1-25.
[Crossref] [Google Scholar]
[54]Pathak AK, Saguna S, Mitra K, Åhlund C. Anomaly detection using machine learning to discover sensor tampering in IoT systems. In international conference on communications 2021 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[55]Kaushik K, Singh V, Manikandan VP. A novel approach for an automated advanced MITM attack on IoT networks. In international conference on advancements in interdisciplinary research 2022 (pp. 60-71). Cham: Springer Nature Switzerland.
[Crossref] [Google Scholar]
[56]Dogan-tusha S, Althunibat S, Qaraqe M. A novel sybil attack detection mechanism for mobile IoT networks. In global communications conference 2022 (pp. 1838-43). IEEE.
[Crossref] [Google Scholar]
[57]Anjum A, Olufowobi H. Towards mitigating blackhole attack in NDN-enabled IoT. In international conference on consumer electronics 2023 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[58]Wang C, Chen J, Yang Y, Ma X, Liu J. Poisoning attacks and countermeasures in intelligent networks: status quo and prospects. Digital Communications and Networks. 2022; 8(2):225-34.
[Crossref] [Google Scholar]
[59]Sanders K, Yau SS. An effective approach to protecting low-power and lossy IoT networks against blackhole attacks. In international conferences on internet of things (ithings) and green computing & communications (GreenCom) and Cyber, Physical & Social Computing (CPSCom) and Smart Data (SmartData) and Congress on Cybermatics (Cybermatics) 2021 (pp. 65-72). IEEE.
[Crossref] [Google Scholar]
[60]Sahay R, Geethakumari G, Mitra B, Thejas V. Exponential smoothing based approach for detection of blackhole attacks in IoT. In international conference on advanced networks and telecommunications systems 2018 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[61]Alghamdi R, Bellaiche M. A cascaded federated deep learning based framework for detecting wormhole attacks in IoT networks. Computers & Security. 2023; 125:103014.
[Crossref] [Google Scholar]
[62]Pu C, Choo KK. Lightweight Sybil attack detection in IoT based on bloom filter and physical unclonable function. Computers & Security. 2022; 113:102541.
[Crossref] [Google Scholar]
[63]Srinivas T, Manivannan SS. Black hole and selective forwarding attack detection and prevention in IoT in health care sector: hybrid meta-heuristic-based shortest path routing. Journal of Ambient Intelligence and Smart Environments. 2021; 13(2):133-56.
[Google Scholar]
[64]Hariri A, Giannelos N, Arief B. Selective forwarding attack on iot home security kits. In computer security: ESORICS international workshops, CyberICPS, SECPRE, SPOSE, and ADIoT, Luxembourg city, 2020 (pp. 360-73). Springer International Publishing.
[Crossref] [Google Scholar]
[65]Saikia B, Majumder S. Analysis of performance vulnerability of MAC scheduling algorithms due to SYN flood attack in 5G NR mmWave. International Journal of Advanced Technology and Engineering Exploration. 2021; 8(82):1102-19.
[Crossref] [Google Scholar]