Possibility of protection against unauthorized interference in telecommunication systems
Keywords:
Prevention, Interloper, Information, Protocols, PrivacyAbstract
Historically, the concept of ownership has dictated that individuals and groups tend to protect valuable resources. Regardless of how much protection is given to the property, there is always a weak point, where the security provided can fail at certain points. This general notion has guided the concept of systems security and defined the disciplines in cyber security and especially that of computer networks. Computer network security consists of three principles: prevention, detection and response. Although these three are the basic components of security, the main focus is on detection and prevention resources because if we are able to detect and prevent all security threats, then there is no need for reaction and response. Intrusion detection is a technique of detecting unauthorized access to a computer system or a computer network. An intrusion into a system is an attempt by an outsider to gain illegal access to the system. Intrusion prevention, on the other hand, is the art of preventing unauthorized access to a system's resources. The two processes are related in a sense, where intrusion detection passively watches for intrusions into the system, and intrusion prevention actively filters network traffic to prevent intrusion attempts. In the continuation of the treatment, we will focus on these two processes.
References
1. Chand, N., Mishra, P., Krishna, C. R., Pilli, E. S., & Govil, M. C. (2016, April). A comparative analysis of SVM and its stacking with other classification algorithm for intrusion detection. In 2016 International Conference on Advances in Computing, Communication, & Automation (ICACCA)(Spring), 1-6. IEEE. https://doi.org/10.1109/ICACCA.2016.7578859
2. Kizza, J. M. (2017). Guide to Computer Network Security. https://doi.org/10.1007/978-3-319-55606-2
3. Saheed, Y. K., Abiodun, A. I., Misra, S., Holone, M. K., & Colomo-Palacios, R. (2022). A machine learning-based intrusion detection for detecting internet of things network attacks. Alexandria Engineering Journal, 61(12), 9395-9409. https://doi.org/10.1016/j.aej.2022.02.063
4. SANS Institute (2016). The History and Evolution of Intrusion Detection. https://www.sans.org/reading-room/whitepapers/detection/history-evolution-intrusion-detection-344
5. Sharma, R. K., & Pippal, R. S. (2020, September). Malicious Attack and Intrusion Prevention in IoT Network Using Blockchain Based Security Analysis. In 2020 12th International Conference on Computational Intelligence and Communication Networks (CICN), 380-385. IEEE. https://doi.org/10.1109/CICN49253.2020.9242610
6. Mitchell, R., & Chen, I. R. (2014). A survey of intrusion detection techniques for cyber-physical systems. ACM Computing Surveys (CSUR), 46(4), 1-29. https://doi.org/10.1145/2542049
7. Kumar, R., Kumar, P., Tripathi, R., Gupta, G. P., Garg, S., & Hassan, M. M. (2022). A distributed intrusion detection system to detect DDoS attacks in blockchain-enabled IoT network. Journal of Parallel and Distributed Computing, 164, 55-68. https://doi.org/10.1016/j.jpdc.2022.01.030
8. http://resources.infosecinstitute.com/internet-things-much-exposed-cyber-threats
9. Mishra, N., & Pandya, S. (2021). Internet of things applications, security challenges, attacks, intrusion detection, and future visions: A systematic review. IEEE Access, 9, 59353-59377. https://doi.org/10.1109/ACCESS.2021.3073408
10. Liu, Y. S., Lai, Y. K., Wang, Z. H., & Yan, H. B. (2019). A new learning approach to malware classification using discriminative feature extraction. IEEE Access, 7, 13015-13023. https://doi.org/10.1109/ACCESS.2019.2892500
11. Masduki, B. W., Ramli, K., Saputra, F. A., & Sugiarto, D. (2015, August). Study on implementation of machine learning methods combination for improving attacks detection accuracy on Intrusion Detection System (IDS). In 2015 International Conference on Quality in Research (QiR), 56-64. IEEE. https://doi.org/10.1109/QiR.2015.7374895
12. Basati, A., & Faghih, M. M. (2022). PDAE: Efficient network intrusion detection in IoT using parallel deep auto-encoders. Information Sciences, 598, 57-74. https://doi.org/10.1016/j.ins.2022.03.065
13. McHugh, J. (2000). Testing intrusion detection systems: a critique of the 1998 and 1999 darpa intrusion detection system evaluations as performed by lincoln laboratory. ACM Transactions on Information and System Security (TISSEC), 3(4), 262-294. https://doi.org/10.1145/382912.382923
14. Al-Taleb, N., & Saqib, N. A. (2020, September). Attacks detection and prevention systems for IoT networks: a survey. In 2020 International Conference on Computing and Information Technology (ICCIT-1441) 1-5. IEEE. https://doi.org/10.1109/ICCIT-144147971.2020.9213770
15. http://www.combofix.org/what-it-is-network-intrusion-detection-system.php.Dec-2015
16. Axelsson, S. (2005). Intrusion detection systems: a survey and taxonomy. 2000. Chalmers University of Technology: Goteborg, Sweden.
17. Liao, H. J., Lin, C. H. R., Lin, Y. C., & Tung, K. Y. (2013). Intrusion detection system: A comprehensive review. Journal of Network and Computer Applications, 36(1), 16-24. https://doi.org/10.1016/j.jnca.2012.09.004
18. Sans Penetration Testing (2001). Host-vs. Network-Based Intrusion Detection Systems. https://cyber-defense.sans.org/resources/papers/gsec/host-vs-network-based-intrusion-detection-systems.
19. SANS Institute InfoSec Reading Room (2001). Application of Neural Networks to Intrusion Detection. https://www.sans.org/reading-room/whitepapers/detection/application-neural-networks-intrusion-detection.
20. Soniya, S. S., & Vigila, S. M. C. (2016, March). Intrusion detection system: Classification and techniques. In 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT) (pp. 1-7). IEEE. https://doi.org/10.1109/ICCPCT.2016.7530231
21. Basholli, F., Daberdinİ, A., & Basholli, A. (2023). Detection and prevention of intrusions into computer systems. Advanced Engineering Days (AED), 6, 138-141.

