SDN Security Mechanism to Detect Malicious Activities


Setup and management of data networks have become less laborious with the advent of software-defined networking (SDN).

There was no magical emergence of this technology. A more accurate explanation is that it arose from efforts to simplify network programming. Distributed system networking. The SDN architecture relies heavily on the separation of the control and data planes. Data-forwarding devices have no agency since control is removed from them. Instead of deciding which forwarding policy to apply depending on the destination of the message, flows are utilized instead. The sequence of packets traveling from a source to an endpoint is called a flow. Each packet inside a flow must conform to the same service standards at the forwarding nodes. These days, the control logic is stored outside, in the form of the SDN controller. As for the latter, it is a network operating system (software platform) that will operate on general-purpose processors and offer the required abstraction to make it simpler to develop forwarding devices based on an abstract, logically centralized network view. Software applications that operate on top of network operating systems allow for the creation of networks. The infrastructure layer of SDN is analogous to the data plane layer of conventional networks; the control layer is analogous to the kernel module that executes applications; and the management layer stores enhanced network capabilities. Administrators of a network’s infrastructure would no longer need to manually establish rules in order to permit connections if they used this tiered approach (Figure 1).

Figure 1: a) Both programmable traditional networks (b) and control-plane-less software-defined networks

Taking advantage of SDN’s programmability and centralized control, researchers will propose developing a security mechanism for SDNs. In the proposed technique, system calls performed by applications using the SDN network would be routinely compared to the predicted behavior of the benchmark baseline. If anything out of the ordinary occurs, it will become apparent in the second comparison.


Next-generation networks will be vastly improved by the use of software-defined networking (SDN). It enables rapid service rollouts for a variety of use cases by providing the programmability required for dynamic network configuration. Its programmability means that network managers may employ other apps to implement a rich set of new networking capabilities. However, assaults on SDN networks pose a variety of security issues due to their programmability.


The purpose of this work is to develop an SDN security system capable of identifying harmful software and actions. The investigation of any fake applications is the project’s secondary objective. To do this, we will monitor system calls made by SDN applications and compare the data to a standard.


Researchers recommend developing a security solution for the SDN that makes advantage of the SDN’s programmability and centralized control to identify malicious applications. In the proposed technique, system calls performed by applications using the SDN network would be routinely compared to the predicted behavior of the benchmark baseline. If anything out of the ordinary occurs, it will become apparent in the second comparison. The outcomes of an experiment using a proof-of-concept prototype of an SDN framework built on Open Switch and a mininet emulator are presented.


In this paper, the authors propose developing and implementing an SDN security system to aid in the detection of malicious activities. SDN’s programmability and centralized administration inspired academics to propose a method that statistically compares the system call utilization of the various SDN applications deployed to baseline/benchmark data on a regular basis. This allows us to detect any dangerous software or out-of-the-ordinary actions.


In this research, we advocated for the development and deployment of an SDN security mechanism to aid in the detection of hostile activities inside SDN networks. The proposed security architecture takes advantage of SDN’s programmability and centralized administration. It also monitors the system calls made by active SDN applications. Harmful programs, when they execute malicious code or do other actions, will make a different number of system calls than legitimate ones. We demonstrated how to spot suspicious behavior like assaults by contrasting the features of system calls as they happen in real time with historical norms and benchmarks. It’s possible that improvements in materials and construction techniques may extend the life of this design. This might be achieved by integrating it with an existing SDN monitoring framework that takes a more holistic view of the network and considers other traffic characteristics, such as the delay between two packets, in addition to the latency between them. It may be possible to detect unusual activity simply by keeping track of how long it takes a packet to enter and exit a node or how long it takes for a new packet to arrive. A more intelligent implementation of the design concept is possible. The second choice may be accomplished with the help of machine learning. Machine learning is used for a variety of purposes, including attack prediction and application classification. Machine learning algorithms can detect and prepare for malware threats. The design is able to forecast the likelihood of dangerous software by analyzing the timing and frequency of various system calls. Also considered by this machine learning technique are the resources (time and hardware) required by each program. Incorporating all of these characteristics into a single profile might make it simpler to identify illicit activity. Likewise, by analyzing the inner workings of each app, a highly accurate classifier may be developed using machine learning that can identify potentially harmful programs from those that pose no threat.

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