| Title | Intrusion Detection Using Swarm Intelligence |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Qureshi, Ayyaz-Ul-Haq, Larijani, Hadi, Javed, Abbas, Mtetwa, Nhamoinesu, Ahmad, Jawad |
| Conference Name | 2019 UK/ China Emerging Technologies (UCET) |
| Keywords | anomaly-based intrusion detection scheme, Artificial Bee Colony, artificial bee colony algorithm, Biological neural networks, communication technologies, composability, compositionality, computer network security, Cyber Attacks, denial-of-service, hybrid multilayer perceptron based intrusion detection system, insecure channel, Internet of Things, Internet-of-Things devices, Intrusion detection, IoT device, IoT security, learning (artificial intelligence), man-in-middle, mean square error methods, mean squared error, multilayer perceptrons, Neural networks, Neurons, NSL-KDD, NSL-KDD Train, particle swarm optimization, Prediction algorithms, pubcrawl, random neural network based system, recurrent neural nets, Recurrent neural networks, RNN-ABC, sensitive information, SQL, SQL Injection, swarm intelligence, Training |
| Abstract | Recent advances in networking and communication technologies have enabled Internet-of-Things (IoT) devices to communicate more frequently and faster. An IoT device typically transmits data over the Internet which is an insecure channel. Cyber attacks such as denial-of-service (DoS), man-in-middle, and SQL injection are considered as big threats to IoT devices. In this paper, an anomaly-based intrusion detection scheme is proposed that can protect sensitive information and detect novel cyber-attacks. The Artificial Bee Colony (ABC) algorithm is used to train the Random Neural Network (RNN) based system (RNN-ABC). The proposed scheme is trained on NSL-KDD Train+ and tested for unseen data. The experimental results suggest that swarm intelligence and RNN successfully classify novel attacks with an accuracy of 91.65%. Additionally, the performance of the proposed scheme is also compared with a hybrid multilayer perceptron (MLP) based intrusion detection system using sensitivity, mean of mean squared error (MMSE), the standard deviation of MSE (SDMSE), best mean squared error (BMSE) and worst mean squared error (WMSE) parameters. All experimental tests confirm the robustness and high accuracy of the proposed scheme. |
| DOI | 10.1109/UCET.2019.8881840 |
| Citation Key | qureshi_intrusion_2019 |
Intrusion Detection Using Swarm Intelligence