Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 3 of 3
Full-Text Articles in Engineering
Recurrent Neural Network Architectures Toward Intrusion Detection, Wafaa Anani
Recurrent Neural Network Architectures Toward Intrusion Detection, Wafaa Anani
Electronic Thesis and Dissertation Repository
Recurrent Neural Networks (RNN) show a remarkable result in sequence learning, particularly in architectures with gated unit structures such as Long Short-term Memory (LSTM). In recent years, several permutations of LSTM architecture have been proposed mainly to overcome the computational complexity of LSTM. In this dissertation, a novel study is presented that will empirically investigate and evaluate LSTM architecture variants such as Gated Recurrent Unit (GRU), Bi-Directional LSTM, and Dynamic-RNN for LSTM and GRU specifically on detecting network intrusions. The investigation is designed to identify the learning time required for each architecture algorithm and to measure the intrusion prediction accuracy. …
Insecs: An Intelligent Network Security System, Nadun Rajasinghe
Insecs: An Intelligent Network Security System, Nadun Rajasinghe
Electronic Thesis and Dissertation Repository
There are new challenges in network security, introduced by the nature of modern networks like IoT systems, Cloud systems, and other distributed systems. System resource limitations in IoT, delays in processing the large stream of data from Cloud and distributed system, incapability to handle multi-step attacks due to delay in updates, limited datasets used for Intrusion Detection System (IDS) training which impacts the system performance are some of the pressing issues. To address these challenges, the author proposes Intelligent Network Security Systems, a framework that can handle these issues and also be as accurate as a commercial grade IDS. The …
Real-Time Intrusion Detection Using Multidimensional Sequence-To-Sequence Machine Learning And Adaptive Stream Processing, Gobinath Loganathan
Real-Time Intrusion Detection Using Multidimensional Sequence-To-Sequence Machine Learning And Adaptive Stream Processing, Gobinath Loganathan
Electronic Thesis and Dissertation Repository
A network intrusion is any unauthorized activity on a computer network. There are host-based and network-based Intrusion Detection Systems (IDS's), of which there are each signature-based and anomaly-based detection methods. An anomalous network behavior can be defined as an intentional violation of the expected sequence of packets. In a real-time network-based IDS, incoming packets are treated as a stream of data. A stream processor takes any stream of data or events and extracts interesting patterns on the fly. This representation allows applying statistical anomaly detection using sequence prediction algorithms as well as using a stream processor to perform signature-based intrusion …