A deep learning approach to network intrusion detection

Purpose of academic writing

DEEP LEARNING APPROACHES FOR NETWORK INTRUSION DETECTION by GABRIEL C. FERNÁNDEZ, B.S. THESIS Presented to the Graduate Faculty of The University of Texas at San Antonio In Partial Fulfillment Of the Requirements For the Degree of MASTER OF SCIENCE IN COMPUTER SCIENCE COMMITTEE MEMBERS: Shouhuai Xu, Ph.D., Chair Greg White, Ph.D. Wenbo Wu, Ph.D. deep-learning and rule-based systems. The objective of this IDS is to detect malicious attacks and ensure CAN security in real time. Deep Learning has already been used in CAN IDS and is already proven to be a successful algorithm when it comes to extensive datasets but comes with the cost of high computational requirements. Intrusion detection plays an important role in ensuring information security, and the key technology is to accurately identify various attacks in the network. In this paper, we explore how to model an intrusion detection system based on deep learning, and we propose a deep learning approach for intrusion detection using recurrent neural networks (RNN-IDS). We show that Deep Neural Networks (DNNs) can outperform other machine learning based intrusion detection systems, while being robust in the presence of dynamic IP addresses. We also show that Autoencoders can be effective for network anomaly detection. Sep 25, 2018 · Network Intrusion Detection using Deep Learning: A Feature Learning Approach (SpringerBriefs on Cyber Security Systems and Networks) - Kindle edition by Kim, Kwangjo, Aminanto, Muhamad Erza, Tanuwidjaja, Harry Chandra. Download it once and read it on your Kindle device, PC, phones or tablets. A Network Intrusion Detection System (NIDS) helps system administrators to detect network security breaches in their organizations. However, many challenges arise while developing a flexible and efficient NIDS for unforeseen and unpredictable attacks. We propose a deep learning based approach for developing such an efficient and flexible NIDS. Network-based intrusion detection systems (NIDS) are devices intelligently distributed within networks that passively inspect traffic traversing the devices on which they sit. NIDS can be hardware or software-based systems and, depending on the manufacturer of the system, can attach to various...Previously many deep learning approaches are shown to be effective for NSL KDD datasets [2, 4,5,6,9,11,12,13,14,15,17,18,19,22]. Stacked autoencoders were used in IEEE 802.11 network platforms to... Intrusion detection with deep learning. The stochastic nature and scarcity of intrusions renders it difficult to extract from existing datasets (e.g. retrospective analysis of video streams) a pattern relating a person’s trajectory tracked over time to an actual act of intrusion attempt. May 2019. iv. Deep learning approaches for network intrusion detection. Deep neural network models are trained using two more recent intrusion detection datasets that overcome limitations of other intrusion detection datasets which have been commonly used in the past.Sep 19, 2019 · In recent years, the rapid development of deep learning technology and its great success in the field of imagery have provided a new solution for network intrusion detection. By visualizing the network data, this paper proposes an intrusion detection method based on deep learning and transfer learning, which transforms the intrusion detection ... We show that Deep Neural Networks (DNNs) can outperform other machine learning based intrusion detection systems, while being robust in the presence of dynamic IP addresses. We also show that Autoencoders can be effective for network anomaly detection. In this study, deep learning technique is used in a hybrid network-based Intrusion Detection System (IDS) to detect intrusion on network. The performance of the proposed technique is evaluated on ... Aug 15, 2020 · The intrusion detection data set is a general big data set, which is directly input into the existing various machine learning models to train the intrusion detection classifier. And various current learning methods can be broadly classified into three types: traditional machine learning based method, deep learning based method, and hybrid method. Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyberattacks at the network-level and the host-level in a timely and automatic manner. However, many challenges arise since malicious attacks are continually changing and are...Sep 24, 2018 · Deep Learning Approach Combining Sparse Autoencoder With SVM for Network Intrusion Detection Abstract: Network intrusion detection systems (NIDSs) provide a better solution to network security than other traditional network defense technologies, such as firewall systems. We show that Deep Neural Networks (DNNs) can outperform other machine learning based intrusion detection systems, while being robust in the presence of dynamic IP addresses. We also show that Autoencoders can be effective for network anomaly detection. In this paper, we propose a deep learning (DL) approach for a network intrusion detection system (DeepIDS) in the SDN architecture. Our models are trained and tested with the NSL-KDD dataset and achieved an accuracy of 80.7% and 90% for a Fully Connected Deep Neural Network (DNN) and a Gated Recurrent Neural Network (GRU-RNN), respectively. Network Intrusion Detection System using Deep Learning Techniques. MIT License. In this project, we aim to explore the capabilities of various deep-learning frameworks in detecting and classifying network intursion traffic with an eye towards designing a ML-based intrusion detection...A novel intrusion detection system (IDS) using a deep neural network (DNN) is proposed to enhance the security of in-vehicular network. Intrusion Detection with Machine Learning. Intrusion detection techniques have been actively studied to help the conventional network resist malicious...Intrusion detection plays an important role in ensuring information security, and the key technology is to accurately identify various attacks in the network. In this paper, we explore how to model an intrusion detection system based on deep learning, and we propose a deep learning approach for intrusion detection using recurrent neural networks (RNN-IDS). September 29, 2018 Books. Network Intrusion Detection using Deep Learning: A Feature Learning Approach by This book presents recent advances in intrusion detection systems (IDSs) using state-of-the-art deep learning methods. It also provides a systematic overview of classical machine...A deep learning approach for network intrusion detection system. In Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS) Applying long short-term memory recurrent neural networks to intrusion detection.A Deep Learning Approach to Network Intrusion Detection Nathan Shone, Tran Nguyen Ngoc, Vu Dinh Phai, Qi Shi Abstract—Network Intrusion Detection Systems (NIDSs) play a crucial role in defending computer networks. However, there are deep-learning and rule-based systems. The objective of this IDS is to detect malicious attacks and ensure CAN security in real time. Deep Learning has already been used in CAN IDS and is already proven to be a successful algorithm when it comes to extensive datasets but comes with the cost of high computational requirements. In this study, deep learning technique is used in a hybrid network-based Intrusion Detection System (IDS) to detect intrusion on network. The performance of the proposed technique is evaluated on ... A deep learning approach for network intrusion detection system. In Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS) Applying long short-term memory recurrent neural networks to intrusion detection.Recently, deep learning methods have also been applied in network intrusion detection systems, as it has been seen that the deep learning methods could successfully solve many problems faced in the... We show that Deep Neural Networks (DNNs) can outperform other machine learning based intrusion detection systems, while being robust in the presence of dynamic IP addresses. We also show that Autoencoders can be effective for network anomaly detection. Deep learning is capable of automatically finding correlation in the data, so it is a promising method for the next generation of intrusion detection. Deep learning can be used to efficiently detect zero-day attacks and so we can acquire a high detection rate. Oct 15, 2018 · A deep learning approach for network intrusion detection system. In: Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS).