Biblio

Network traffic anomaly detection is of critical importance in cybersecurity due to the massive and rapid growth of sophisticated computer network attacks. Indeed, the more new Internet-related technologies are created, the more elaborate the attacks become. Among all the contemporary high-level attacks, dictionary-based brute-force attacks (BFA) present one of the most unsurmountable challenges. We need to develop effective methods to detect and mitigate such brute-force attacks in realtime. In this paper, we investigate SSH and FTP brute-force attack detection by using the Long Short-Term Memory (LSTM) deep learning approach. Additionally, we made use of machine learning (ML) classifiers: J48, naive Bayes (NB), decision table (DT), random forest (RF) and k-nearest-neighbor (k-NN), for additional detection purposes. We used the well-known labelled dataset CICIDS2017. We evaluated the effectiveness of the LSTM and ML algorithms, and compared their performance. Our results show that the LSTM model outperforms the ML algorithms, with an accuracy of 99.88%.

Denial-of-Service (DoS) and probe attacks are growing more modern and sophisticated in order to evade detection by Intrusion Detection Systems (IDSs) and to increase the potent threat to the availability of network services. Detecting these attacks is quite tough for network operators using misuse-based IDSs because they need to see through attackers and upgrade their IDSs by adding new accurate attack signatures. In this paper, we proposed a novel signal and image processing-based method for detecting network probe and DoS attacks in which prior knowledge of attacks is not required. The method uses a time-frequency representation technique called S-transform, which is an extension of Wavelet Transform, to reveal abnormal frequency components caused by attacks in a traffic signal (e.g., a time-series of the number of packets). Firstly, S-Transform converts the traffic signal to a two-dimensional image which describes time-frequency behavior of the traffic signal. The frequencies that behave abnormally are discovered as abnormal regions in the image. Secondly, Otsu's method is used to detect the abnormal regions and identify time that attacks occur. We evaluated the effectiveness of the proposed method with several network probe and DoS attacks such as port scans, packet flooding attacks, and a low-intensity DoS attack. The results clearly indicated that the method is effective for detecting the probe and DoS attack streams which were generated to real-world Internet.