A Deep Learning Based Intrusion Detection System on GPUs
Title | A Deep Learning Based Intrusion Detection System on GPUs |
Publication Type | Conference Paper |
Year of Publication | 2019 |
Authors | Karatas, G., Demir, O., Sahingoz, O. K. |
Conference Name | 2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) |
Date Published | June 2019 |
Publisher | IEEE |
ISBN Number | 978-1-7281-1624-2 |
Keywords | Access controls, authorisation, Big Data, composability, cyber world, Deep Learning, deep learning algorithms, deep learning based intrusion detection system, dynamic intrusion detection mechanism, encryption mechanisms, firewalls, GPUs, graphics processing units, IDS, IDS on GPU, IDS system, information security market, Intrusion detection, intrusion detection system, learning (artificial intelligence), market computers, modern learning mechanism, neural nets, neural network approach, optimization functions, parallel computing, password protections mechanisms, pubcrawl, real-world operations, resilience, Resiliency, security breaches |
Abstract | In recent years, almost all the real-world operations are transferred to cyber world and these market computers connect with each other via Internet. As a result of this, there is an increasing number of security breaches of the networks, whose admins cannot protect their networks from the all types of attacks. Although most of these attacks can be prevented with the use of firewalls, encryption mechanisms, access controls and some password protections mechanisms; due to the emergence of new type of attacks, a dynamic intrusion detection mechanism is always needed in the information security market. To enable the dynamicity of the Intrusion Detection System (IDS), it should be updated by using a modern learning mechanism. Neural Network approach is one of the mostly preferred algorithms for training the system. However, with the increasing power of parallel computing and use of big data for training, as a new concept, deep learning has been used in many of the modern real-world problems. Therefore, in this paper, we have proposed an IDS system which uses GPU powered Deep Learning Algorithms. The experimental results are collected on mostly preferred dataset KDD99 and it showed that use of GPU speed up training time up to 6.48 times depending on the number of the hidden layers and nodes in them. Additionally, we compare the different optimizers to enlighten the researcher to select the best one for their ongoing or future research. |
URL | https://ieeexplore.ieee.org/document/9042132 |
DOI | 10.1109/ECAI46879.2019.9042132 |
Citation Key | karatas_deep_2019 |
- information security market
- security breaches
- Resiliency
- resilience
- real-world operations
- pubcrawl
- password protections mechanisms
- parallel computing
- optimization functions
- neural network approach
- neural nets
- modern learning mechanism
- market computers
- learning (artificial intelligence)
- intrusion detection system
- Intrusion Detection
- Access controls
- IDS system
- IDS on GPU
- IDS
- graphics processing units
- GPUs
- firewalls
- encryption mechanisms
- dynamic intrusion detection mechanism
- deep learning based intrusion detection system
- deep learning algorithms
- deep learning
- cyber world
- composability
- Big Data
- authorisation