research-article

Sustainable Security for the Internet of Things Using Artificial Intelligence Architectures

Authors:

Celestine Iwendi,

Saif Ur Rehman,

Abdul Rehman Javed,

Gautam SrivastavaAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 21, Issue 3

Article No.: 73, Pages 1 - 22

https://doi.org/10.1145/3448614

Published: 16 June 2021 Publication History

Abstract

In this digital age, human dependency on technology in various fields has been increasing tremendously. Torrential amounts of different electronic products are being manufactured daily for everyday use. With this advancement in the world of Internet technology, cybersecurity of software and hardware systems are now prerequisites for major business’ operations. Every technology on the market has multiple vulnerabilities that are exploited by hackers and cyber-criminals daily to manipulate data sometimes for malicious purposes. In any system, the Intrusion Detection System (IDS) is a fundamental component for ensuring the security of devices from digital attacks. Recognition of new developing digital threats is getting harder for existing IDS. Furthermore, advanced frameworks are required for IDS to function both efficiently and effectively. The commonly observed cyber-attacks in the business domain include minor attacks used for stealing private data. This article presents a deep learning methodology for detecting cyber-attacks on the Internet of Things using a Long Short Term Networks classifier. Our extensive experimental testing show an Accuracy of 99.09%, F1-score of 99.46%, and Recall of 99.51%, respectively. A detailed metric representing our results in tabular form was used to compare how our model was better than other state-of-the-art models in detecting cyber-attacks with proficiency.

References

[1]

Reza Ahmadi, Karim Jahed, and Juergen Dingel. 2019. mCUTE: A model-level concolic unit testing engine for UML state machines. In Proceedings of the 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE’19). IEEE, 1182–1185.

Digital Library

[2]

Alif Ahmed, Farimah Farahmandi, and Prabhat Mishra. 2018. Directed test generation using concolic testing on RTL models. In Proceedings of the 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE’18). IEEE, 1538–1543.

[3]

Alif Ahmed and Prabhat Mishra. 2017. QUEBS: Qualifying event based search in concolic testing for validation of RTL models. In Proceedings of the 2017 IEEE International Conference on Computer Design (ICCD’17). IEEE, 185–192.

[4]

Waleed Alnumay, Uttam Ghosh, and Pushpita Chatterjee. 2019. A Trust-Based predictive model for mobile ad hoc network in internet of things. Sensors 19, 6 (2019), 1467.

[5]

Joseph Henry Anajemba, Yue Tang, Celestine Iwendi, Akpesiri Ohwoekevwo, Gautam Srivastava, and Ohyun Jo. 2020. Realizing efficient security and privacy in IoT networks. Sensors 20, 9 (2020), 2609.

[6]

Isha Batra, Sahil Verma, Arun Malik, Uttam Ghosh, Joel J.P.C. Rodrigues, Gia Nhu Nguyen, ASM Hosen, Vinayagam Mariappan, et al. 2020. Hybrid logical security framework for privacy preservation in the green internet of things. Sustainability 12, 14 (2020), 5542.

[7]

Sooyoung Cha, Seongjoon Hong, Junhee Lee, and Hakjoo Oh. 2018. Automatically generating search heuristics for concolic testing. In Proceedings of the 40th International Conference on Software Engineering. 1244–1254.

Digital Library

[8]

Mingjian Cui, Jianhui Wang, and Meng Yue. 2019. Machine learning-based anomaly detection for load forecasting under cyberattacks. IEEE Trans. Smart Grid 10, 5 (2019), 5724–5734.

[9]

Straight Edge. 2020. 5 Top Cybersecurity Threats & Their Solutions for 2020. Retrieved from https://www.straightedgetech.com/5-top-cybersecurity-threats-and-their-solutions-for-2020/.

[10]

Jamal El Hachem, Ali Sedaghatbaf, Elena Lisova, and Aida Causevic. 2019. Using bayesian networks for a cyberattacks propagation analysis in systems-of-systems. In Proceedings of the 2019 26th Asia-Pacific Software Engineering Conference (APSEC’19). IEEE, 363–370.

[11]

Mojtaba Eskandari, Zaffar Haider Janjua, Massimo Vecchio, and Fabio Antonelli. 2020. Passban IDS: An intelligent anomaly based intrusion detection system for IoT edge devices. IEEE Internet of Things Journal 7, 8 (2020), 6882–6897.

[12]

Adrien Facon, Sylvain Guilley, Xuan-Thuy Ngo, and Thomas Perianin. 2019. Hardware-enabled AI for embedded security: A new paradigm. In Proceedings of the 2019 3rd International Conference on Recent Advances in Signal Processing, Telecommunications & Computing (SigTelCom’19). IEEE, 80–84.

[13]

Gregory Falco, Arun Viswanathan, Carlos Caldera, and Howard Shrobe. 2018. A master attack methodology for an AI-based automated attack planner for smart cities. IEEE Access 6 (2018), 48360–48373.

[14]

Ian J. Goodfellow, Dumitru Erhan, Pierre Luc Carrier, Aaron Courville, Mehdi Mirza, Ben Hamner, Will Cukierski, Yichuan Tang, David Thaler, Dong-Hyun Lee, et al. 2015. Challenges in representation learning: A report on three machine learning contests. Neur. Netw. 64 (2015), 59–63.

Digital Library

[15]

Ai Gu, Zhenyu Yin, Yue Li, and Suo Li. 2018. Research on safety and security of cyber physical machine tool system. In Proceedings of the 2018 IEEE International Conference on Information and Automation (ICIA’18). IEEE, 1669–1673.

[16]

Zhitao Guan, Jing Li, Longfei Wu, Yue Zhang, Jun Wu, and Xiaojiang Du. 2017. Achieving efficient and secure data acquisition for cloud-supported internet of things in smart grid. IEEE IoT J. 4, 6 (2017), 1934–1944.

[17]

Zhitao Guan, Yue Zhang, Longfei Wu, Jun Wu, Jing Li, Yinglong Ma, and Jingjing Hu. 2019. APPA: An anonymous and privacy preserving data aggregation scheme for fog-enhanced IoT. J. Netw. Comput. Appl. 125 (2019), 82–92.

[18]

Hanan Hindy, Ethan Bayne, Miroslav Bures, Robert Atkinson, Christos Tachtatzis, and Xavier Bellekens. 2020. Machine learning based IoT intrusion detection system: An MQTT case study. arXiv:2006.15340. Retrieved from https://arxiv.org/abs/2006.15340.

[19]

ASM Sanwar Hosen, Saurabh Singh, Pradip Kumar Sharma, Uttam Ghosh, Jin Wang, In-Ho Ra, and Gi Hwan Cho. 2020. Blockchain-based transaction validation protocol for a secure distributed IoT network. IEEE Access (2020).

[20]

Celestine Iwendi, Zunera Jalil, Abdul Rehman Javed, Thippa Reddy, Rajesh Kaluri, Gautam Srivastava, and Ohyun Jo. 2020. KeySplitWatermark: Zero watermarking algorithm for software protection against cyber-attacks. IEEE Access 8 (2020), 72650–72660.

[21]

Celestine Iwendi, Gautam Srivastava, Suleman Khan, and Praveen Kumar Reddy Maddikunta. 2020. Cyberbullying detection solutions based on deep learning architectures. Multimedia Syst. (2020), 1–14.

[22]

Raghudeep Kannavara, Christopher J. Havlicek, Bo Chen, Mark R. Tuttle, Kai Cong, Sandip Ray, and Fei Xie. 2015. Challenges and opportunities with concolic testing. In Proceedings of the 2015 National Aerospace and Electronics Conference (NAECON’15). IEEE, 374–378.

[23]

Ghassan Kbar and Ammar Alazab. 2019. A comprehensive protection method for securing the organization’s network against cyberattacks. In Proceedings of the 2019 Cybersecurity and Cyberforensics Conference (CCC’19). IEEE, 118–122.

[24]

Kush Khanna, Bijaya Ketan Panigrahi, and Anupam Joshi. 2017. AI-based approach to identify compromised meters in data integrity attacks on smart grid. IET Gener. Transm. Distrib. 12, 5 (2017), 1052–1066.

[25]

Nakhyun Kim, Seulgi Lee, Hyeisun Cho, Byun-Ik Kim, and MoonSeog Jun. 2018. Design of a cyber threat information collection system for cyber attack correlation. In Proceedings of the 2018 International Conference on Platform Technology and Service (PlatCon’18). IEEE, 1–6.

[26]

Taeksu Kim, Jonghyun Park, Igor Kulida, and Yoonkyu Jang. 2014. Concolic testing framework for industrial embedded software. In Proceedings of the 2014 21st Asia-Pacific Software Engineering Conference, Vol. 2. IEEE, 7–10.

Digital Library

[27]

Yunho Kim, Yunja Choi, and Moonzoo Kim. 2018. Precise concolic unit testing of C programs using extended units and symbolic alarm filtering. In Proceedings of the 40th International Conference on Software Engineering. 315–326.

Digital Library

[28]

Yunho Kim, Dongju Lee, Junki Baek, and Moonzoo Kim. 2019. Concolic testing for high test coverage and reduced human effort in automotive industry. In Proceedings of the 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP’19). IEEE, 151–160.

Digital Library

[29]

Xiaohui Kuang, Hongyi Liu, Ye Wang, Qikun Zhang, Quanxin Zhang, and Jun Zheng. 2019. A CMA-ES-Based adversarial attack on black-box deep neural networks. IEEE Access 7 (2019), 172938–172947.

[30]

Hongbo Li, Sihuan Li, Zachary Benavides, Zizhong Chen, and Rajiv Gupta. 2018. COMPI: Concolic testing for MPI applications. In Proceedings of the 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS’18). IEEE, 865–874.

[31]

Hongliang Liang, Zhengyu Li, Minhuan Huang, and Xiaoxiao Pei. 2017. A novel method makes concolic system more effective. In Proceedings of the 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud’17). IEEE, 243–248.

[32]

Bin Lin, Kai Cong, Zhenkun Yang, Zhigang Liao, Tao Zhan, Christopher Havlicek, and Fei Xie. 2018. Concolic testing of SystemC designs. In Proceedings of the 2018 19th International Symposium on Quality Electronic Design (ISQED’18). IEEE, 1–7.

[33]

Xi Lin, Jianhua Li, Jun Wu, Haoran Liang, and Wu Yang. 2019. Making knowledge tradable in edge-AI enabled IoT: A consortium blockchain-based efficient and incentive approach. IEEE Trans. Industr. Inf. 15, 12 (2019), 6367–6378.

[34]

A Tawalbeh Lo’ai, Hala Tawalbeh, Houbing Song, and Yaser Jararweh. 2017. Intrusion and attacks over mobile networks and cloud health systems. In Proceedings of the 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS’17). IEEE, 13–17.

[35]

Yuxiang Ma, Yulei Wu, Jingguo Ge, and LI Jun. 2018. An architecture for accountable anonymous access in the Internet-of-Things network. IEEE Access 6 (2018), 14451–14461.

[36]

Rupak Majumdar and Koushik Sen. 2007. Hybrid concolic testing. In Proceedings of the 29th International Conference on Software Engineering (ICSE’07). IEEE, 416–426.

Digital Library

[37]

Wataru Matsuda, Mariko Fujimoto, Tomomi Aoyama, and Takuho Mitsunaga. 2019. Cyber security risk assessment on industry 4.0 using ICS testbed with AI and Cloud. In Proceedings of the 2019 IEEE Conference on Application, Information and Network Security (AINS’19). IEEE, 54–59.

[38]

Patrick McAfee, Mohamed Wiem Mkaouer, and Daniel E. Krutz. 2017. CATE: Concolic Android testing using Java pathfinder for Android applications. In Proceedings of the 2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft’17). IEEE, 213–214.

Digital Library

[39]

Khoi Khac Nguyen, Dinh Thai Hoang, Dusit Niyato, Ping Wang, Diep Nguyen, and Eryk Dutkiewicz. 2018. Cyberattack detection in mobile cloud computing: A deep learning approach. In Proceedings of the 2018 IEEE Wireless Communications and Networking Conference (WCNC’18). IEEE, 1–6.

[40]

Ian Perera, Jena Hwang, Kevin Bayas, Bonnie Dorr, and Yorick Wilks. 2018. Cyberattack prediction through public text analysis and mini-theories. In Proceedings of the 2018 IEEE International Conference on Big Data (Big Data’18). IEEE, 3001–3010.

[41]

Sonal Pinto and Michael S. Hsiao. 2017. RTL functional test generation using factored concolic execution. In Proceedings of the 2017 IEEE International Test Conference (ITC’17). IEEE, 1–10.

[42]

Xiao Qu and Brian Robinson. 2011. A case study of concolic testing tools and their limitations. In Proceedings of the 2011 International Symposium on Empirical Software Engineering and Measurement. IEEE, 117–126.

Digital Library

[43]

Pritam Roy, Sagar Chaki, and Pankaj Chauhan. 2019. High coverage concolic equivalence checking. In Proceedings of the 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE’19). IEEE, 462–467.

[44]

Jacob Sakhnini, Hadis Karimipour, Ali Dehghantanha, Reza M. Parizi, and Gautam Srivastava. 2019. Security aspects of internet of things aided smart grids: A bibliometric survey. In Internet of Things. 100111.

[45]

Prinkle Sharma, Hong Liu, Honggang Wang, and Shelley Zhang. 2017. Securing wireless communications of connected vehicles with artificial intelligence. In Proceedings of the 2017 IEEE International Symposium on Technologies for Homeland Security (HST’17). IEEE, 1–7.

[46]

Jose Costa Sapalo Sicato, Sushil Kumar Singh, Shailendra Rathore, and Jong Hyuk Park. 2020. A comprehensive analyses of intrusion detection system for IoT environment. J. Inf. Process. Syst. 16, 4 (2020), 975–990.

[47]

Gautam Srivastava, Reza M. Parizi, and Ali Dehghantanha. 2020. The future of blockchain technology in healthcare internet of things security. In Blockchain Cybersecurity, Trust and Privacy. Springer, 161–184.

[48]

Youcheng Sun, Xiaowei Huang, Daniel Kroening, James Sharp, Matthew Hill, and Rob Ashmore. 2019. DeepConcolic: Testing and debugging deep neural networks. In Proceedings of the 2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion’19). IEEE, 111–114.

Digital Library

[49]

Syeda Manjia Tahsien, Hadis Karimipour, and Petros Spachos. 2020. Machine learning based solutions for security of Internet of Things (IoT): A survey. J. Netw. Comput. Appl. (2020), 102630.

[50]

Muhammad Tariq, Muhammad Adnan, Gautam Srivastava, and Herald Vincent Poor. 2020. Instability detection and prevention in smart grids under asymmetric faults. (unpublished).

[51]

Xinyu Wang, Jun Sun, Zhenbang Chen, Peixin Zhang, Jingyi Wang, and Yun Lin. 2018. Towards optimal concolic testing. In Proceedings of the 40th International Conference on Software Engineering. 291–302.

Digital Library

[52]

Weizhong Yan, Lalit K. Mestha, and Masoud Abbaszadeh. 2019. Attack detection for securing cyber physical systems. IEEE IoT J. 6, 5 (2019), 8471–8481.

[53]

Zhongjiang Yao, Jingguo Ge, Yulei Wu, and Linjie Jian. 2019. A privacy preserved and credible network protocol. J. Parallel Distrib. Comput. 132 (2019), 150–159.

[54]

Bo Yin, Hao Yin, Yulei Wu, and Zexun Jiang. 2020. FDC: A secure federated deep learning mechanism for data collaborations in the internet of things. IEEE Internet of Things 7, 7 (2020), 6348–6359.

[55]

K. Yu, L. Tan, X. Shang, J. Huang, G. Srivastava, and P. Chatterjee. 2020. Efficient and privacy-preserving medical research support platform against COVID-19: A blockchain-based approach. IEEE Consumer Electronics Magazine 10, 2 (2020), 111–120.

[56]

Yiyun Zhou, Meng Han, Liyuan Liu, Jing Selena He, and Yan Wang. 2018. Deep learning approach for cyberattack detection. In Proceedings of the IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS’18). IEEE, 262–267.

[57]

Yuchen Zhou, Yang Liu, and Shiyan Hu. 2017. Smart home cyberattack detection framework for sponsor incentive attacks. IEEE Trans. Smart Grid 10, 2 (2017), 1916–1927.

Cited By

Verma S(2024)Harnessing the Strength of Digital Technologies for CybersecurityCases on Security, Safety, and Risk Management10.4018/979-8-3693-2675-6.ch001(1-20)Online publication date: 27-Sep-2024
https://doi.org/10.4018/979-8-3693-2675-6.ch001
Yaras SDener M(2024)IoT-Based Intrusion Detection System Using New Hybrid Deep Learning AlgorithmElectronics10.3390/electronics1306105313:6(1053)Online publication date: 12-Mar-2024
https://doi.org/10.3390/electronics13061053
Bao HZhao YZhang XWang GDuan JTian RMen JZhang M(2024)A Probabilistic and Distributed Validation Framework Based on Blockchain for Artificial Intelligence of ThingsIEEE Internet of Things Journal10.1109/JIOT.2023.327984911:1(17-28)Online publication date: 1-Jan-2024
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Index Terms

Sustainable Security for the Internet of Things Using Artificial Intelligence Architectures
1. Security and privacy
  1. Cryptography
    1. Symmetric cryptography and hash functions
      1. Block and stream ciphers
      2. Hash functions and message authentication codes
  2. Software and application security
    1. Web application security

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 21, Issue 3

August 2021

522 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3468071

Editor:
Ling Liu
Georgia Institute of Technology, USA

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Copyright © 2021 Association for Computing Machinery.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 16 June 2021

Accepted: 01 January 2021

Revised: 01 October 2020

Received: 01 July 2020

Published in TOIT Volume 21, Issue 3

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Cited By

Verma S(2024)Harnessing the Strength of Digital Technologies for CybersecurityCases on Security, Safety, and Risk Management10.4018/979-8-3693-2675-6.ch001(1-20)Online publication date: 27-Sep-2024
https://doi.org/10.4018/979-8-3693-2675-6.ch001
Yaras SDener M(2024)IoT-Based Intrusion Detection System Using New Hybrid Deep Learning AlgorithmElectronics10.3390/electronics1306105313:6(1053)Online publication date: 12-Mar-2024
https://doi.org/10.3390/electronics13061053
Bao HZhao YZhang XWang GDuan JTian RMen JZhang M(2024)A Probabilistic and Distributed Validation Framework Based on Blockchain for Artificial Intelligence of ThingsIEEE Internet of Things Journal10.1109/JIOT.2023.327984911:1(17-28)Online publication date: 1-Jan-2024
https://doi.org/10.1109/JIOT.2023.3279849
Kamalasekaran KSundramoorthy A(2024)Applications of chemically modified screen-printed electrodes in food analysis and quality monitoring: a reviewRSC Advances10.1039/D4RA02470B14:38(27957-27971)Online publication date: 2024
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Manivannan D(2024)Recent endeavors in machine learning-powered intrusion detection systems for the Internet of ThingsJournal of Network and Computer Applications10.1016/j.jnca.2024.103925229(103925)Online publication date: Sep-2024
https://doi.org/10.1016/j.jnca.2024.103925
Sarker IJanicke HFerrag MAbuadbba A(2024)Multi-aspect rule-based AI: Methods, taxonomy, challenges and directions towards automation, intelligence and transparent cybersecurity modeling for critical infrastructuresInternet of Things10.1016/j.iot.2024.10111025(101110)Online publication date: Apr-2024
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Kang LWang Y(2024)Efficient and accurate personalized product recommendations through frequent item set mining fusion algorithmHeliyon10.1016/j.heliyon.2024.e2504410:3(e25044)Online publication date: Feb-2024
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Huang HChen WFang WChen WIp WYung K(2024)A State of the Art Review on Artificial Intelligence-Enabled Cyber Security in Smart GridAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5606-3_4(38-48)Online publication date: 5-Aug-2024
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Chen JZhang XZheng HChen JZhang XZheng H(2024)Adversarial Attacks on GNN-Based Vertical Federated LearningAttacks, Defenses and Testing for Deep Learning10.1007/978-981-97-0425-5_3(35-54)Online publication date: 4-Jun-2024
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ALAmri SALAbri FSharma T(2023)Artificial Intelligence Deployment to Secure IoT in Industrial EnvironmentQuality Control - An Anthology of Cases10.5772/intechopen.104469Online publication date: 18-Jan-2023
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