RNN-ABC: A New Swarm Optimization Based Technique for Anomaly Detection

Abstract

The exponential growth of internet communications and increasing dependency of users upon software-based systems for most essential, everyday applications has raised the importance of network security. As attacks are on the rise, cybersecurity should be considered as a prime concern while developing new networks. In the past, numerous solutions have been proposed for intrusion detection; however, many of them are computationally expensive and require high memory resources. In this paper, we propose a new intrusion detection system using a random neural network and an artificial bee colony algorithm (RNN-ABC). The model is trained and tested with the benchmark NSL-KDD data set. Accuracy and other metrics, such as the sensitivity and specificity of the proposed RNN-ABC, are compared with the traditional gradient descent algorithm-based RNN. While the overall accuracy remains at 95.02%, the performance is also estimated in terms of mean of the mean squared error (MMSE), standard deviation of MSE (SDMSE), best mean squared error (BMSE), and worst mean squared error (WMSE) parameters, which further confirms the superiority of the proposed scheme over the traditional methods.

Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks

“…Furthermore, RNN algorithms have been used in various applications including Heating, Ventilation, and Air Conditioning (HVAC) systems [34][35][36]; nonoccupied buildings' energy prediction [37]; image pattern recognition [38]; and intrusion detection systems [12,39,40] all with significant results. Moreover, according to the results of Cerkez et al [41] and Abdelbaki et al [42], simple encounters can represent neurons in RNN and, hence, easy hardware implementation.…”

An Adversarial Approach for Intrusion Detection Systems Using Jacobian Saliency Map Attacks (JSMA) Algorithm

“…RNNs have been used extensively for pattern recognition [19]. However, a little research has been reported to analyse the effectiveness of RNNs for intrusion detection systems using NSL-KDD dataset, such as our previous work at [3]. RNN has recently got colossal attention due to its less complexity and better generalisation capabilities.…”

“…Network security is now considered as one of the primary issues faced by modern computer networks. A significant extension of intelligent services to the users has raised strong concerns among the research communities to design and develop efficient intrusion detection systems (IDS) [3]. Not only does it enforce the safety of the communication system, but it could also be useful as an alternative to traditional firewalls.…”

“…Interested reader can further understand the network operation in [18] and our previously published work at [3,20,21].…”

“…For this purpose, we have utilized "One-hot Encoding" where these features are converted to the designated numeric values. Many of the researchers [3,20] utilized feature selection by using the gain ratio (GR), correlation-based feature extraction (CFS) and information gain (IG), which helps reduce the complexity of classifier. For this study, we use the complete feature space of the dataset and performance is estimated.…”