Efficient anomaly detection and mitigation in software defined networking environment

Abstract

A Computer network or data communication network is a telecommunication network that allows computers to exchange data. Computer networks are typically built from a large number of network devices such as routers, switches and numerous types of middle boxes with many complex protocols implemented on them. They need to accomplish very complex tasks with access to very limited tools. As a result, network management and performance tuning is quite challenging. Software-Defined Networking (SDN) is an emerging architecture purporting to be adaptable , cost-effective, dynamic and manageable pursuing to be suitable for the high-bandwidth, changing nature of today's applications. SDN architectures decouples network control and forwarding functions, making network control to become directly programmable and the underlying infrastructure to be abstracted from applications and network services. The network security is a prominent feature of the network ensuring accountability, confidentiality, integrity, and protection against many external and internal threats. An Intrusion Detection System (IDS) is a type of security software designed to automatically alert administrators when someone or something is trying to compromise information system through malicious activities or through security policy violations. Security violation in SDN environment needs to be identified to prevent the system from an attack. The proposed work aims to detect the attacks on SDN environment. Detecting anomalies on SDN environment will be more manageable and efficient.

Artificial intelligence enabled software‐defined networking: a comprehensive overview

“…A large variety of meta-heuristic algorithms such as ACO [183][184][185][186][187][188][189][190][191], EAs [192][193][194], GAs [195][196][197][198][199][200][201][202][203], PSO [204][205][206][207][208][209], SA [210][211][212], bee colony optimisation-based [213,214], whale optimisation [215,216], FFO [217], BA [85], TLBO [87] and GWO [207] were used in SDN.…”

“…The experimental results showed that FFA produced efficient and accurate results. [85] employed BBA for feature selection in an SDN-based intrusion detection system. Based on the experiments conducted on NSL-KDD dataset, the selected features achieved a detection rate of 90.9, 91.1, 80.2 and 98.1% for DoS, Probe, R2L and U2R attack types, respectively, with low false positive rates when used with J48 DT.…”

“…BA [83] is a new meta‐heuristic method based on the fascinating capability of microbats to find their prey and distinguish between different types of insects even in complete darkness by decreasing the loudness and increasing the rate of emitted ultrasonic sound that bounces back from the surrounding objects [84, 85]. An artificial bat has three vectors: position vector, velocity vector and frequency vector.…”

Machine learning‐based IDS for software‐defined 5G network

“…It performs C4.5 algorithm to build the decision tree for classifying the traffic. The proposed work in [17] focuses on identifying network anomalies efficiently under SDN environment using bat algorithm metaheuristic method for feature extraction and entropy-based decision tree for traffic classification. In [18], Kim et al propose a three-layer recurrent neural network (NN) acting as a neural classifier for misuse detection.…”

A bat algorithm for SDN network scheduling

“…Sathya's proposed work aims to detect the attacks on SDN environment by using a heuristic algorithm inspired by the echolocation behavior of bats. Detecting anomalies in SDN environment will be more manageable and efficient [15].…”