Over the last few years, the need for programmable networks has captured the interest of industrialists and academicians. It has led to the development of a paradigm called software defined network (SDN). It separates the network intelligence into the control plane and forwarding logic into the data plane. This architecture gives scope to various security issues of which denial of service (DoS) is the most common and challenging to detect. This paper focuses on the detection and mitigation of a slow DoS attack called Slowloris on Apache2 server in SDN based networks. The proposed solution is called Slowloris detection and mitigation mechanism (SDMM). Mininet, an emulator, and SimpleHTTPServer are used for simulation and the same is implemented using Zodiac FX OpenFlow switch, Ryu controller and Apache2 server. SDMM algorithm detects and mitigates prolonged Slowloris attack in typical networks as well as in slow networks with low bandwidth and high delay in 240-280s with an accuracy of 100% and 98% respectively. It uses expectation of burst size as a key factor for detection.

<span lang="EN-US">Slow denial of service attack (DoS) is a tricky issue in software-defined network (SDN) as it uses less bandwidth to attack a server. In this paper, a slow-rate DoS attack called Slowloris is detected and mitigated on Apache2 and Nginx servers using a methodology called an intelligent system for slow DoS detection using machine learning (ISSDM) in SDN. Data generation module of ISSDM generates dataset with response time, the number of connections, timeout, and pattern match as features. Data are generated in a real environment using Apache2, Nginx server, Zodiac FX OpenFlow switch and Ryu controller. Monte Carlo simulation is used to estimate threshold values for attack classification. Further, ISSDM performs header inspection using regular expressions to mark flows as legitimate or attacked during data generation. The proposed feature selection module of ISSDM, called blended statistical and information gain (BSIG), selects those features that contribute best to classification. These features are used for classification by various machine learning and deep learning models. Results are compared with feature selection methods like Chi-square, T-test, and information gain.</span>

<p>The extensive use of the internet has resulted in novel technologies and protocol improvisation. Hypertext transfer protocol/1.1 (HTTP/1.1) is widely adapted on the internet. However, HTTP/2 is found to be more efficient over transport control protocol (TCP). The HTTP/2 protocol can withstand the payload overhead when compared to HTTP/1.1 by multiplexing multiple requests. However, both the protocols are highly susceptible to application-level denial of service (DoS) attacks. In this research, a slow-rate DoS attack called Slowloris is detected over Apache2 servers enabled with both versions of HTTP in traditional networks and software defined networks (SDN). Server metrics such as server connection time to the webpage, latency in receiving a response from the server, page load time, response-response gap, and inter-packet arrival time at the server are monitored to analyze attack activity. A Monte Carlo simulation is used to estimate threshold values for server connection time and latency for attack detection. This work is implemented in a lab environment using virtual machines, Ryu controller, zodiac FX OpenFlow switch and Apache2 servers. This study also highlights SDN's security benefits over traditional networks.</p>