Visible to the public Sequential event-based detection of network attacks on CSE CIC IDS 2018 data set – Application of GSP and IPAM Algorithm

TitleSequential event-based detection of network attacks on CSE CIC IDS 2018 data set – Application of GSP and IPAM Algorithm
Publication TypeConference Paper
Year of Publication2022
AuthorsNisha, T N, Pramod, Dhanya
Conference Name2022 International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS)
KeywordsBehavioral sciences, Benchmark testing, composability, CSE-CIC-IDS 2018 data set, Data models, DDoS Attacks, feature extraction, Generalized Sequential Patterns (GSP), IDS, Intelligent systems, Intrusion detection by Event Analysis, IPAM, knowledge based anomaly detection, Network security, Prediction algorithms, probabilistic attack prediction, pubcrawl, resilience, Resiliency, security, security events, Sequential event patterns
AbstractNetwork attacks are always a nightmare for the network administrators as it eats away a huge wavelength and disturbs the normal working of many critical services in the network. Network behavior based profiling and detection is considered to be an accepted method; but the modeling data and method is always a big concern. The network event-based profiling is getting acceptance as they are sequential in nature and the sequence depicts the behavior of the system. This sequential network events can be analyzed using different techniques to create a profile for anomaly detection. In this paper we examine the possibility of two techniques for sequential event analysis using Modified GSP and IPAM algorithm. We evaluate the performance of these algorithms on the CSE-CIC-IDS 2018 data set to benchmark the performance. This experiment is different from other anomaly-based detection which evaluates the features of the dataset to detect the abnormalities. The performance of the algorithms on the dataset is then confirmed by the pattern evolving from the analysis and the indications it provides for early detection of network attacks.
DOI10.1109/IC3SIS54991.2022.9885438
Citation Keynisha_sequential_2022