Name
Title | ||
|---|---|---|
Using Pattern-of-Life as Contextual Information for Anomaly-Based Intrusion Detection Systems. |
Abstract | ||
|---|---|---|
As the complexity of cyber-attacks keeps increasing, new robust detection mechanisms need to be developed. The next generation of Intrusion Detection Systems (IDSs) should be able to adapt their detection characteristics based not only on the measureable network traffic, but also on the available high-level information related to the protected network. To this end, we make use of the Pattern-of-Life (PoL) of a computer network as the main source of high-level information. We propose two novel approaches that make use of a Fuzzy Cognitive Map (FCM) to incorporate the PoL into the detection process. There are four main aims of the work. First, to evaluate the efficiency of the proposed approaches in identifying the presence of attacks. Second, to identify which of the proposed approaches to integrate an FCM into the IDS framework produces the best results. Third, to identify which of the metrics used in the design of the FCM produces the best detection results. Fourth, to evidence the improved detection performance that contextual information can offer in IDSs. The results that we present verify that the proposed approaches improve the effectiveness of our IDS by reducing the total number of false alarms; providing almost perfect detection rate (i.e., 99.76%) and only 6.33% false positive rate, depending on the particular metric combination. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ACCESS.2017.2762162 | IEEE ACCESS |
Keywords | Field | DocType |
Basic probability assignment,contextual information,Dempster-Shafer theory,Fuzzy cognitive maps,intrusion detection systems,network security,pattern-of-life,port scanning attack | False positive rate,Data mining,Computer science,Computer network,Artificial intelligence,Intrusion detection system,Contextual information,Network security,Fuzzy cognitive map,Knowledge engineering,Hidden Markov model,Dempster–Shafer theory,Machine learning | Journal |
Volume | ISSN | Citations |
5 | 2169-3536 | 1 |
PageRank | References | Authors |
0.38 | 16 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francisco J. Aparicio-Navarro | 1 | 27 | 5.46 |
| Konstantinos G. Kyriakopoulos | 2 | 24 | 11.04 |
| Yu Gong | 3 | 231 | 18.46 |
| David J. Parish | 4 | 144 | 17.88 |
| Jonathon A. Chambers | 5 | 56 | 6.96 |