Abstract
Recent studies have pulled tons of research in the domain of cloud security and various intrusion detection systems (IDSs). This is because of advancement in the different types of attacks on computer systems. Distributed denial of service (DDoS) attack is one of them wherein the attackers can compromise the cloud system by exploiting vulnerabilities. Initially, during the multi-step exploration, vulnerability with low frequency along with the virtual machine which is identified and compromised are included in DDoS attacks. In this context, various IDSs have been surveyed with different countermeasure techniques including some effective techniques to minimize the malicious activities within end systems or networks. The main aim of IDSs is to detect different attacks within networks and end systems or to be precise against any information systems which are very difficult to maintain in a secure state for a long duration. Some studies have shown that the use of host-based systems and the network-based systems help to improve the attack detection. This paper focuses on the study of various well-known IDS and various techniques to minimize malicious activities within the system.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chung, C.-J., Cui, J. S., Khatkar, P., & Huang, D. (2013). Non-intrusive process-based monitoring system to mitigate and prevent VM vulnerability explorations. In 2013 9th International Conference Conference on Collaborative Computing: Networking, Applications and Worksharing (Collaboratecom) (pp. 21–30). IEEE.
Yongli, Z., Yungui, Z., Weiming, T., & Hongzhi, C. (2013). An improved feature selection algorithm based on MAHALANOBIS distance for network intrusion detection. In 2013 International Conference on Sensor Network Security Technology and Privacy Communication System (SNS & PCS) (pp. 69–73). IEEE.
Humphrey, M., Emerson, R., & Beekwilder, N. (2016). Unified, multi-level intrusion detection in private cloud infrastructures. In IEEE International Conference on Smart Cloud (SmartCloud) (pp. 11–15). IEEE.
Koli, M. S., & Chavan, M. K. (2017). An advanced method for detection of botnet traffic using intrusion detection system. In 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT) (pp. 481–485). IEEE.
Alrajeh, N. A., Khan, S., & Shams, B. (2013). Intrusion detection systems in wireless sensor networks: A review. International Journal of Distributed Sensor Networks, 9(5), 167575.
Wong, K., Dillabaugh, C., Seddigh, N., & Nandy, B. (2017). Enhancing Suricata intrusion detection system for cyber security in SCADA networks. In 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) (pp. 1–5). IEEE.
Roschke, S., Cheng, F., & Meinel, C. (2011). A new alert correlation algorithm based on attack graph. In Computational Intelligence in Security for Information Systems (pp. 58–67). Berlin: Springer.
Sheyner, O., Haines, J., Jha, S., Lippmann, R., & Wing, J. M. (2002). Automated generation and analysis of attack graphs. In 2002 IEEE Symposium on Security and privacy, 2002. Proceedings (pp. 273–284). IEEE.
Ou, X., Boyer, W. F., & McQueen, M. A. (2006). A scalable approach to attack graph generation. In Proceedings of the 13th ACM Conference on Computer and Communications Security (pp. 336–345). ACM.
Souissi, S. (2015). Toward a novel rule-based attack description and response language. In 2015 11th International Conference on Information Assurance and Security (IAS) (pp. 44–49). IEEE.
Abduvaliyev, A., Pathan, A.-S. K., Zhou, J., Roman, R., & Wong, W.-C. (2013). On the vital areas of intrusion detection systems in wireless sensor networks. IEEE Communications Surveys & Tutorials, 15(3), 1223–1237.
Zonouz, S. A., Khurana, H., Sanders, W. H., & Yardley, T. M. (2014). RRE: A game-theoretic intrusion response and recovery engine. IEEE Transactions on Parallel and Distributed Systems, 25(2), 395–406.
Roy, A., Kim, D. S., & Trivedi, K. S. (2010). Cyber security analysis using attack countermeasure trees. In Proceedings of the Sixth Annual Workshop on Cyber Security and Information Intelligence Research (p. 28). ACM.
Chung, C.-J., Khatkar, P., Xing, T., Lee, J., & Huang, D. (2013). Nice: Network intrusion detection and countermeasure selection in virtual network systems. IEEE Transactions on Dependable and Secure Computing, 10(4), 198–211.
Vaarandi, R. (2013). Detecting anomalous network traffic in organizational private networks. In 2013 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA) (pp. 285–292). IEEE.
Mishra, P., Pilli, E. S., Varadharajan, V., & Tupakula, U. (2017). Out-VM monitoring for malicious network packet detection in cloud. In Asia Security and Privacy (ISEASP), 2017 ISEA (pp. 1–10). IEEE.
Payne, B. D., Martim, D. P. A., & Lee, W. (2007). Secure and flexible monitoring of virtual machines. In Computer Security Applications Conference, 2007. ACSAC 2007. Twenty-Third Annual (pp. 385–397). IEEE.
Stefanova, Z., & Ramachandran, K. (2017). Network attribute selection, classification and accuracy (NASCA) procedure for intrusion detection systems. In 2017 IEEE International Symposium on Technologies for Homeland Security (HST) (pp. 1–7). IEEE.
Ingle, L., & Pakle, G. K. (2016). NIDSV: Network based intrusion detection and counter-measure excerption in virtual environment using AODV protocol. In International Conference on Inventive Computation Technologies (ICICT) (Vol. 3, pp. 1–6). IEEE.
Wang, Z., & Zhu, Y. (2017). A centralized HIDS framework for private cloud. In 2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) (pp. 115–120). IEEE.
Jin, R., He, X., & Dai, H. (2017). On the tradeoff between privacy and utility in collaborative intrusion detection systems—a game theoretical approach. In Proceedings of the Hot Topics in Science of Security: Symposium and Bootcamp (pp. 45–51). ACM.
Singhal, A., & Ou, X. (2017). Security risk analysis of enterprise networks using probabilistic attack graphs. In Network Security Metrics (pp. 53–73). Berlin: Springer.
Mitchell, R., & Chen, R. (2016). Modeling and analysis of attacks and counter defense mechanisms for cyber physical systems. IEEE Transactions on Reliability, 65(1), 350–358.
Xu, J., Yuan, X., Yu, A., Kim, J. H., Kim, T., & Zhang, J. (2016). Developing and evaluating a hands-on lab for teaching local area network vulnerabilities. In Frontiers in Education Conference (FIE), 2016 IEEE (pp. 1–4). IEEE.
Yan, Q., & Yu, F. R. (2015). Distributed denial of service attacks in software-defined networking with cloud computing. IEEE Communications Magazine, 53(4), 52–59.
Jiang, X., Wang, X., & Xu, D. (2007). Stealthy malware detection through VMM-based out-of-the-box semantic view reconstruction. In Proceedings of the 14th ACM Conference on Computer and communications Security (pp. 128–138). ACM.
Ning, P., Cui, Y., & Reeves, D. S. (2002). Constructing attack scenarios through correlation of intrusion alerts. In Proceedings of the 9th ACM Conference on Computer and Communications Security (pp. 245–254). ACM.
Yun, Y., Xi-shan, X., & Zhi-chang, Q. (2011). A probabilistic computing approach of attack graph-based nodes in large-scale network. Procedia Environmental Sciences, 10, 3–8.
Hong, J. B., & Kim, D. S. (2016). Assessing the effectiveness of moving target defenses using security models. IEEE Transactions on Dependable and Secure Computing, 13(2), 163–177.
Roy, A., Kim, D. S., Trivedi, K. S. (2012). Scalable optimal countermeasure selection using implicit enumeration on attack countermeasure trees. In 2012 42nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) (pp. 1–12). IEEE.
Padhy, R. P., Patra, M. R., & Satapathy, S. C. (2011). Cloud computing: Security issues and research challenges. International Journal of Computer Science and Information Technology & Security (IJCSITS), 1(2), 136–146.
Ateniese, G., & Mangard, S. (2001). A new approach to DNS security (DNSSEC). In Proceedings of the 8th ACM conference on Computer and Communications Security (pp. 86–95). ACM.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rajput, D., Thakkar, A. (2019). A Survey on Different Network Intrusion Detection Systems and CounterMeasure. In: Shetty, N., Patnaik, L., Nagaraj, H., Hamsavath, P., Nalini, N. (eds) Emerging Research in Computing, Information, Communication and Applications. Advances in Intelligent Systems and Computing, vol 906. Springer, Singapore. https://doi.org/10.1007/978-981-13-6001-5_41
Download citation
DOI: https://doi.org/10.1007/978-981-13-6001-5_41
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6000-8
Online ISBN: 978-981-13-6001-5
eBook Packages: EngineeringEngineering (R0)