Biblio

Botnets are one of the most destructive threats against the cyber security. Recently, HTTP protocol is frequently utilized by botnets as the Command and Communication (C&C) protocol. In this work, we aim to detect HTTP based botnet activity based on botnet behaviour analysis via machine learning approach. To achieve this, we employ flow-based network traffic utilizing NetFlow (via Softflowd). The proposed botnet analysis system is implemented by employing two different machine learning algorithms, C4.5 and Naive Bayes. Our results show that C4.5 learning algorithm based classifier obtained very promising performance on detecting HTTP based botnet activity.

Distributed Denial of Service (DDoS) attacks are one of the challenging network security problems to address. The existing defense mechanisms against DDoS attacks usually filter the attack traffic at the victim side. The problem is exacerbated when there are spoofed IP addresses in the attack packets. In this case, even if the attacking traffic can be filtered by the victim, the attacker may reach the goal of blocking the access to the victim by consuming the computing resources or by consuming a big portion of the bandwidth to the victim. This paper proposes a Trace back-based Defense against DDoS Flooding Attacks (TDFA) approach to counter this problem. TDFA consists of three main components: Detection, Trace back, and Traffic Control. In this approach, the goal is to place the packet filtering as close to the attack source as possible. In doing so, the traffic control component at the victim side aims to set up a limit on the packet forwarding rate to the victim. This mechanism effectively reduces the rate of forwarding the attack packets and therefore improves the throughput of the legitimate traffic. Our results based on real world data sets show that TDFA is effective to reduce the attack traffic and to defend the quality of service for the legitimate traffic.