INTRUSION DETECTION SYSTEMS USING BLOCKCHAIN TECHNOLOGY
Keywords:
Blockchain Technology, Intrusion Detection Systems, Threat Actors, Trust Management, Single Point Of FailureAbstract
As a result of the increasing frequency of cyber-attacks on all fronts, research towards more robust and flexible intrusion detection systems (IDS) are required to help combat and alleviate criminal attacks by threat actors. collaborative measures are proving to be a better form of IDS as compared to standard signature or anomaly IDS. Collaborative IDS however still face a fundamental trust challenge. ledger-based blockchain technology seeks to solve this problem using a Collaborative Intrusion Detection System (CIDS) incorporating blockchain technology to protect data integrity as well as securing accountability for all nodes in the network.
References
S. Greenstein, ‘‘The aftermath of the dyn DDOS attack,’’ IEEE Micro, vol. 39, no. 4, pp. 66–68, Jul. 2019.
Ehrenfeld, J.M.: WannaCry, cybersecurity and health information technology: a time to act. J. Med. Syst. 41(7), 104 (2017)
C. Baldwin. "Bitcoin worth $72 million stolen from Bitfinex exchange in Hong Kong." https://www.reuters.com/article/us-bitfinex-hacked-hongkong-idUSKCN10E0KP (accessed July, 2019).
F. S. Hardwick, A. Gioulis, R. N. Akram, and K. Markantonakis,"E-Voting with blockchain: an E-Voting protocol with decentralisation and voter privacy," in 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2018, pp. 1561-1567.
Keshk, M., Turnbull, B., Moustafa, N., Vatsalan, D. and Choo, K.K.R., 2019. A Privacy-Preserving Framework based Blockchain and Deep Learning for Protecting Smart Power
Networks. IEEE Transactions on Industrial Informatics, DOI: 10.1109/TII.2019.2957140
M. A. Khan and K. Salah, "IoT security: Review, blockchain solutions, and open challenges," Future Generation Computer Systems, vol. 82, pp. 395-411, 2018.
X. Yue, H. Wang, D. Jin, M. Li, and W. Jiang, "Healthcare data gateways: found healthcare intelligence on blockchain with novel privacy risk control," Journal of medical systems, vol. 40, no. 10, p. 218, 2016.
E. Karafiloski and A. Mishev, "Blockchain solutions for big data challenges: A literature review," in IEEE EUROCON 2017-17th International Conference on Smart Technologies, 2017: IEEE, pp. 763-768.
T. M. Fernández-Caramés and P. Fraga-Lamas, "A Review on the Use of Blockchain for the Internet of Things," IEEE Access, vol. 6, pp. 32979-33001, 2018.
F. Tian, "A supply chain traceability system for food safety based on HACCP, blockchain & Internet of things," in 2017 International Conference on Service Systems and Service Management, 2017: IEEE, pp. 1-6.
W. Meng, E. W. Tischhauser, Q. Wang, Y. Wang, and J. Han, "When intrusion detection meets blockchain technology: a review," IEEE Access, vol. 6, pp. 10179-10188, 2018.
A. Alketbi, Q. Nasir, and M. A. Talib, "Blockchain for government services—Use cases, security benefits and challenges," in 2018 15th Learning and Technology Conference
(L&T), 2018: IEEE, pp. 112-119
J. Hu, Host-Based Anomaly Intrusion Detection. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 235–255. Available: https://doi.org/10.1007/978-3-642-04117-4_13
Halamka, J.D., Lippman, A., Ekblaw, A.: The potential for blockchain to transform electronic health records (2017). https://hbr.org/2017/03/the-potential-for-blockchain-to-transform-electronic-health-records
Vasilomanolakis, E., Karuppayah, S., M ̈uhlh ̈auser, M., Fischer, M.: Taxonomy and survey of collaborative intrusion detection. ACM Comput. Surv. 47(4), 33 (2015)
Wood, G.: Ethereum: a secure decentralized generalized transaction ledger. Ethereum Project Yellow Paper 151 (2014)
E. Vasilomanolakis, S. Karuppayah, M. Mühlhäuser, and M. Fischer, “Taxonomy and survey of collaborative intrusion detection,” ACM Comput. Surv., vol. 47, no. 4,
pp. 55:1–55:33, May 2015. Available: http://doi.acm.org/10.1145/2716260
Vasilomanolakis, S. Karuppayah, M. Mühlhäuser, and M. Fischer, “Taxonomy and survey of collaborative intrusion detection,” ACM Comput. Surv., vol. 47, no. 4, pp. 55:1–55:33, May 2015. Available: http://doi.acm.org/10.1145/2716260
Alexopoulos, E. Vasilomanolakis, N. R. Ivánkó, and M. Mühlhäuser, “Towards blockchain-based collaborative intrusion detection systems,” in Critical Information Infrastructures Security, G. D’Agostino and A. Scala, Eds. Cham: Springer International Publishing, 2018, pp. 107–118.
Antonopoulos, A.M.: Mastering Bitcoin: Unlocking Digital Cryptocurrencies O’Reilly Media, Inc., Sebastopol (2014)
D'Agostino, Gregorio; Scala, Antonio (2018). Towards Blockchain-Based Collaborative Intrusion Detection Systems, 10.1007/978-3-319-99843-5(Chapter 10), 107–118. doi:10.1007/978-3-319-99843-5_10
H. Okada, S. Yamasaki, and V. Bracamonte, “Proposed classification of blockchains based on authority and incentive dimensions,” in 2017 19th International Conference on Advanced Communication Technology (ICACT), Feb 2017, pp. 593–597
Baliga, A.: Understanding Blockchain Consensus Models. Technical report. Persistent Systems Ltd. (2017)
R. Khonde, S. and Ulagamuthalvi, V. (2022) “Blockchain: Secured solution for signature transfer in distributed intrusion detection system,” Computer Systems Science and Engineering, 40(1), pp. 37–51. Available at: https://doi.org/10.32604/csse.2022.017130.
Moustafa, N. and Slay, J. (2015) “UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 Network Data Set),” 2015 Military Communications and Information Systems Conference (MilCIS) [Preprint]. Available at: https://doi.org/10.1109/milcis.2015.7348942.
Demers, A., et al.: Epidemic algorithms for replicated database maintenance. In: Proceedings of the sixth annual ACM Symposium on Principles of distributed computing, pp. 1–12. ACM (1987)
Khonde, S. R., & Ulagamuthalvi, V. (2022). Hybrid intrusion detection system using blockchain framework. Eurasip Journal on Wireless Communications and Networking, 2022(1). https://doi.org/10.1186/s13638-022-02089-4
Srinivas Pulyala Reddy,Vinay Dutt Jangampet, and Avinash Gupta Desetty. "Defending the Next Frontier: Artificial Intelligence and the Future of Cybersecurity Warfare." Turkish Journal of Computer and Mathematics Education (TURCOMAT), vol. 11, no. 1, 2020, pp. 1047-1050. https://www.turcomat.org/index.php/turkbilmat/article/view/14321.
T. Proffitt, How Can You Build and Leverage SNORT IDS Metrics to Reduce Risk? SANS Institute, Sep2013. Available:https://www.sans.org/reading-room/whitepapers/tools/paper/34350
“top(1) – Linux manual page.” Available: http://man7.org/linux/man-pages/man1/ top.1.html “web3.eth – web3.js 1.0.0 documentation.” Available: https://web3js.readthedocs.io/ en/1.0/web3-eth.html#eth-sendtransaction-return