Name
Abstract | ||
|---|---|---|
As the influence of Internet and networking technologies as communication medium advance and expand across the globe, cyber attacks also grow accordingly. Anomaly detection systems (ADSs) are employed to scrutinize information such as packet behaviours coming from various locations on network to find those intrusive activities as fast as possible with precision. Unfortunately, besides minimizing false alarms; the performance issues related to heavy computational process has become drawbacks to be resolved in this kind of detection systems. In this work, a novel Signature-Based Anomaly Detection Scheme (SADS) which could be applied to scrutinize packet headers' behaviour patterns more precisely and promptly is proposed. Integratingdata mining classifiers such as Naive Bayes and Random Forest can beutilized to decrease false alarms as well as generate signatures based on detection resultsfor future prediction and reducing processing time. Results from a number of experiments using DARPA 1999 and ISCX 2012 benchmark dataset have validated that SADS own better detection capabilities with lower processing duration as contrast to conventional anomaly-based detection method. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ISBAST.2014.7013127 | Biometrics and Security Technologies |
Keywords | Field | DocType |
data mining,digital signatures,pattern classification,SADS,data mining classifier,signature-based anomaly intrusion detection scheme,Anomaly detection system,Naïve Bayes,Random Forest,packet header | Data mining,Anomaly detection,Naive Bayes classifier,Computer science,Network packet,Anomaly-based intrusion detection system,Artificial intelligence,Header,Random forest,Intrusion detection system,Machine learning,The Internet | Conference |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Warusia Yassin | 1 | 3 | 0.73 |
| Nur Izura Udzir | 2 | 164 | 28.44 |
| Azizol Abdullah | 3 | 54 | 16.07 |
| Mohd Taufik Abdullah | 4 | 28 | 6.27 |
| Hazura Zulzalil | 5 | 47 | 6.38 |
| Zaiton Muda | 6 | 23 | 2.58 |