Performance enhancement of intrusion detection system using bagging ensemble technique with feature selection
- Authors: Rashid, Md Mamunur , Kamruzzaman, Joarder , Ahmed, Mohiuddin , Islam, Nahina , Wibowo, Santoso , Gordon, Steven
- Date: 2020
- Type: Text , Conference paper
- Relation: 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020
- Full Text: false
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- Description: An intrusion detection system's (IDS) key role is to recognise anomalous activities from both inside and outside the network system. In literature, many machine learning techniques have been proposed to improve the performance of IDS. To create a good IDS, a single classifier might not be powerful enough. To overcome this bottleneck researchers focus on hybrid/ensemble techniques. Such methods are more complex and computation intensive, but they provide greater accuracy and lower false alarm rates (FAR). In this paper, we propose a bagging ensemble that improves the performance of IDS in terms of accuracy and FAR where the NSL-KDD dataset has been used to classify benign and abnormal traffic. We have also applied the information gain-based feature selection method to select highly relevant features for improving the accuracy of the proposed technique and achieved 84.93 % accuracy and 2.45 % FAR on the test dataset. © 2020 IEEE.
A Reinforcement learning based algorithm towards energy efficient 5G Multi-tier network
- Authors: Islam, Nahina , Alazab, Ammar , Alazab, Mamoun
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 2019 Cybersecurity and Cyberforensics Conference (CCC); Melbourne, Vic; 8th-9th May, 2019 p. 96-101
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- Reviewed:
- Description: Energy efficiency is a key factor in the next generation wireless communication systems. Sleep mode implementation in multi-tier 5G networks has proven to be a very good approach for improving the energy efficiency. In this paper, we propose a novel reinforcement learning based decision making algorithm to implement sleep mode in the base stations (BSs) used in multi-tier 5G networks. We propose a Markovian Decision process (MDP) based algorithm to switch between three different power consumption modes of a BS for improving the energy efficiency of the 5G network. The MDP based approach intelligently switches between the states of the BS based on the offered traffic whilst maintaining a prescribed minimum channel rate per user. Our results show that there is a significant gain in the energy efficiency when using our proposed MDP algorithm together with the three-state BSs. We have also shown the energy-delay tradeoff in order to design a delay aware network.
Energy efficient and delay aware 5g multi-tier network
- Authors: Islam, Nahina , Alazab, Ammar , Agbinya, Johnson
- Date: 2019
- Type: Text , Journal article
- Relation: Remote sensing Vol. 11, no. 9 (2019), p. 1019
- Full Text: false
- Reviewed:
- Description: Multi-tier heterogeneous Networks (HetNets) with dense deployment of small cells in 5G networks are expected to effectively meet the ever increasing data traffic demands and offer improved coverage in indoor environments. However, HetNets are raising major concerns to mobile network operators such as complex distributed control plane management, handover management issue, increases latency and increased energy expenditures. Sleep mode implementation in multi-tier 5G networks has proven to be a very good approach for reducing energy expenditures. In this paper, a Markov Decision Process (MDP)-based algorithm is proposed to switch between three different power consumption modes of a base station (BS) for improving the energy efficiency and reducing latency in 5G networks. The MDP-based approach intelligently switches between the states of the BS based on the offered traffic while maintaining a prescribed minimum channel rate per user. Simulation results show that the proposed MDP algorithm together with the three-state BSs results in a significant gain in terms of energy efficiency and latency.