Name
Abstract | ||
|---|---|---|
AbstractInternet-enabled devices or Internet of Things as it has been prevailed are increasing exponentially every day. The lack of security standards in the manufacturing of these devices along with the haste of the manufacturers to increase their market share in this area has created a very large network of vulnerable devices that can be easily recruited as bot members and used to initiate very large volumetric Distributed Denial of Service DDoS attacks. The significance of the problem can be easily acknowledged due to the large number of cases regarding attacks on institutions, enterprises and even countries which have been recently revealed. In the current paper a novel method is introduced, which is based on a data mining technique that can analyze incoming IP traffic details and early warn the network administrator about a potentially developing DDoS attack. The method can scale depending on the availability of the infrastructure from a conventional laptop computer to a complex cloud infrastructure. Based on the hardware configuration as it is proved with the experiments the method can easily monitor and detect abnormal network traffic of several Gbps in real time using the minimum hardware equipment. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.4018/IJCWT.2017070104 | Periodicals |
Keywords | DocType | Volume |
All Repeated Patterns Detection, ARPaD, Data Mining, DDoS, Distributed Denial of Service, LERP-RSA, Suffix Array | Journal | 7 |
Issue | ISSN | Citations |
3 | 1947-3435 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Konstantinos F. Xylogiannopoulos | 1 | 18 | 7.74 |
| Panagiotis Karampelas | 2 | 34 | 15.16 |
| Reda Alhajj | 3 | 1919 | 205.67 |