Detecting security anomalies from internet traffic using the MA-RMSE algorithms

Many detection techniques against worms, denial of service attacks and botnets on the Internet have been developed. It is difficult to detect these threats if the malicious traffic has insufficient intensity, which is usually the case. To make the problem worse, legitimate Internet services behaving like worm and complexity network environments undermines the efficiency of the detection techniques. This paper proposes an entropy-based Internet threats detection approach that determines and reports the traffic complexity parameters when changes in the traffic complexity content may indicate a malicious network event. Based on the experiment, the proposed method is efficient and produces less false positive and false negative alarms with a faster detection time.