GA-PSO-Optimized Neural-Based Control Scheme for Adaptive Congestion Control to Improve Performance in Multimedia Applications

Active queue control aims to improve the overall communication network throughput while providing lower delay and small packet loss rate. The basic idea is to actively trigger packet dropping (or marking provided by explicit congestion notification (ECN)) before buffer overflow. In this paper, two artificial neural networks (ANN)-based control schemes are proposed for adaptive queue control in TCP communication networks. The structure of these controllers is optimized using genetic algorithm (GA) and the output weights of ANNs are optimized using particle swarm optimization (PSO) algorithm. The controllers are radial bias function (RBF)-based, but to improve the robustness of RBF controller, an error-integral term is added to RBF equation in the second scheme. Experimental results show that GA- PSO-optimized improved RBF (I-RBF) model controls network congestion effectively in terms of link utilization with a low packet loss rate and outperform Drop Tail, proportional-integral (PI), random exponential marking (REM), and adaptive random early detection (ARED) controllers.Comment: arXiv admin note: text overlap with arXiv:1711.0635

Flow-based anomaly detection using neural network optimized with GSA algorithm

Reliable high-speed networks are essential to provide quality services to ever growing Internet applications. A Network Intrusion Detection System (NIDS) is an important tool to protect computer networks from attacks. Traditional packet-based NIDSs are time-intensive as they analyze all network packets. A state-of-the-art NIDS should be able to handle a high volume of traffic in real time. Flow-based intrusion detection is an effective method for high speed networks since it inspects only packet headers. The existence of new attacks in the future is another challenge for intrusion detection. Anomaly-based intrusion detection is a well-known method capable of detecting unknown attacks. In this paper, we propose a flow-based anomaly detection system. Artificial Neural Network (ANN) is an important approach for anomaly detection. We used a Multi-Layer Perceptron (MLP) neural network with one hidden layer. We investigate the use of a Gravitational Search Algorithm (GSA) in optimizing interconnection weights of a MLP network. Our proposed GSA-based flow anomaly detection system (GFADS) is trained with a flow-based data set. The trained system can classify benign and malicious flows with 99.43% accuracy. We compare the performance of GSA with traditional gradient descent training algorithms and a particle swarm optimization (PSO) algorithm. The results show that GFADS is effective in flow-based anomaly detection. Finally, we propose a four-feature subset as the optimal set of features.</p