Network Intrusion Detection Using Deep Learning (original) (raw)

A Proposed Method for Detecting Network Intrusion Using Deep Learning Approach

2023 IEEE 3rd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA)

NIDSs, also known as network intrusion detection systems, are essential for protecting computer networks. Nonetheless, there are concerns about the viability and sustainability of current methods for meeting the needs of modern networks. These issues are more specifically connected to the decreased detection accuracy and the increased human involvement needed. This paper introduces a novel deep-learning intrusion detection method to address these problems. We use a deep learning method by creating a Deep Neural Network (DNN) model for intrusion detection systems and training it using the NSLKDD Dataset. From the 41 features in the NSL-KDD Dataset, we only use 37 basic features in this work. We demonstrate from various studies that the deep learning approach has much potential for use in NIDs. In this paper, we show the effectiveness of our method and compare it to a few previous studies in terms of accuracy, precision, recall, and f-measure values.

An Improved Network Intrusion Detection Based on Deep Neural Network

IOP Conference Series: Materials Science and Engineering, 2019

Network intrusion detection is of great significance for network security in Local Area Network (LAN). Traditional methods such as firewalls do not completely protect against attacks on the LAN due to lack of continuous learning. Recently, the ability of convolutional neural networks (CNN) to extract features in the field of computer vision has received extensive attention. CNN can automatically extract effective complex features to adapt to constantly changing environments, which is especially important in network intrusion detection. In this paper, we focus on network security in the LAN. We propose an approach based on CNN to implement intrusion detection in LAN. This approach can effectively identify network attacks and has an accuracy of 98.34% on the KDD99 dataset. The experimental results show that the proposed approach based on the CNN has high accuracy in intrusion detection.

An Intelligent Approach for Intrusion Detection using Convolutional Neural Network

Journal of network security computer networks, 2022

Intrusion Detection Systems (IDS) is one of the important aspects of cyber security that can detect the anomalies in the network traffic. IDS are a part of Second defense line of a system that can be deployed along with other security measures such as access control, authentication mechanisms and encryption techniques to secure the systems against cyber-attacks. However, IDS suffers from the problem of handling large volume of data and in detecting zero-day attacks (new types of attacks) in a real-time traffic environment. To overcome this problem, an intelligent Deep Learning approach for Intrusion Detection is proposed based on Convolutional Neural Network (CNN-IDS). Initially, the model is trained and tested under a new real-time traffic dataset, CSE-CIC-IDS 2018 dataset. Then, the performance of CNN-IDS model is studied based on three important performance metrics namely, accuracy / training time, detection rate and false alarm rate. Finally, the experimental results are compared with those of various Deep Discriminative models including Recurrent Neural network (RNN), Deep Neural Network (DNN) etc., proposed for IDS under the same dataset. The Comparative results show that the proposed CNN-IDS model is very much suitable for modelling a classification model both in terms of binary and multi-class classification with higher detection rate, accuracy, and lower false alarm rate. The CNN-IDS model improves the accuracy of intrusion detection and provides a new research method for intrusion detection.

Network Intrusion Detection System using Deep Learning

Procedia Computer Science, 2021

The widespread use of interconnectivity and interoperability of computing systems have become an indispensable necessity to enhance our daily activities. Simultaneously, it opens a path to exploitable vulnerabilities that go well beyond human control capability. The vulnerabilities deem cyber-security mechanisms essential to assume communication exchange. Secure communication requires security measures to combat the threats and needs advancements to security measures that counter evolving security threats. This paper proposes the use of deep learning architectures to develop an adaptive and resilient network intrusion detection system (IDS) to detect and classify network attacks. The emphasis is how deep learning or deep neural networks (DNNs) can facilitate flexible IDS with learning capability to detect recognized and new or zero-day network behavioral features, consequently ejecting the systems intruder and reducing the risk of compromise. To demonstrate the model's effectiveness, we used the UNSW-NB15 dataset, reflecting real modern network communication behavior with synthetically generated attack activities.

A Method For Network Intrusion Detection Using Deep Learning

Journal of Student Research

In an increasingly digitally reliant world, organizations are facing the ever more challenging problem of how to best defend their digital information and infrastructure. Current non-machine learning methods for detecting network intrusion, like signature-based and anomaly-based algorithms, are slow and unreliable. Signature based detection holds signatures, or known information and warning signs, about a known attack and compares them to the current flow of data. If a signature matches with the network activity, users and network administrators are notified. Anomaly based detection is where the system monitors current network traffic and compares it to a set baseline traffic. Again, if any unusual traffic occurs, members of the network are notified. In this research, new advancements in deep learning algorithms are used to bolster the defenses of digital networks. Neural networks are used to create a multi-class classifier, which will determine whether the network activity is a cer...

IJERT-Intrusion Detection System using Deep Learning

International Journal of Engineering Research and Technology (IJERT), 2021

https://www.ijert.org/intrusion-detection-system-using-deep-learning https://www.ijert.org/research/intrusion-detection-system-using-deep-learning-IJERTCONV9IS05002.pdf Intrusion Detection System (IDS) defined as a Device or software application which monitors the network or system activities and finds if there is any malicious activity occur. Outstanding growth and usage of internet raises concerns about how to communicate and protect the digital information safely. In today's world hackers use different types of attacks for getting the valuable information. Many of the intrusion detection techniques, methods and algorithms help to detect those several attacks. The main objective of this paper is to provide a complete study about the intrusion detection, types of intrusion detection methods, types of attacks, different tools and techniques, research needs, challenges and finally develop the IDS Tool for Research Purpose That tool are capable of detect and prevent the intrusion from the intruder.

A Novel Intrusion Detection System Using Deep Learning

Advances in Intelligent Systems and Computing, 2021

The expanding utilization of the Internet has enlarged dangers and new attacks for quite a while. Altogether to recognize oddity in a network, the intrusion detection system has been proven to be a significant segment of secure networks. Machine learning model learns every time it predicts an output, and this property empowers them to distinguish the network pattern and find whether they are ordinary or noxious. There is an expanding demand for dependable and genuine dataset among the examined network. In this article, a comprehensive examination of the CSE-CIC-IDS2018 dataset is made. During the research, numerous issues and deficiencies in a dataset were found. Solutions to fix those issues led to a model different from the existing solutions. The model consisted of two components-principal component analysis and deep neural network. After pre-processing the dataset, it gave F1-score of 0.99, making it robust than other existing models.

DCNN-IDS: Deep Convolutional Neural Network Based Intrusion Detection System

Springer, Singapore, 2019

In the present era, cyberspace is growing tremendously, and the intrusion detection system (IDS) plays a key role in it to ensure information security. The IDS, which works at the network and host level, should be capable of identifying various malicious attacks. The job of network-based IDS is to differentiate between normal and malicious traffic data and raise an alert in case of an attack. Apart from the traditional signature and anomaly-based approaches, many researchers have employed various deep learning (DL) techniques for detecting intrusion, as DL models are capable of extracting salient features automatically from the input data. The application of deep convolutional neural networks (DCNN), which is utilized quite often for solving research problems in image processing and vision fields, is not explored much for IDS. In this paper, a DCNN architecture for IDS which is trained on KDDCUP 99 data set, is proposed. This work also shows that the DCNN-IDS model performs superior when compared with other existing works.

A network intrusion detection method based on deep learning with higher accuracy

Procedia Computer Science, 2020

The traditional network intrusion detection methods have the problem of long distance dependency. It is easy to ignore contextual information. Moreover, the current data dimension is too high and the feature extraction process is complex, which is not conducive to the requirements of real-time and accuracy of intrusion detection. For the above two problems, this paper presents a new network intrusion detection method based on Auto-Encoder network(AN) and long-term memory neural network (LSTM). First, KDDcup99 data set is used and pre-processed. And an Auto-Encoder network model is constructed by superimposing multiple auto-encoder networks to map high-dimensional data to low-dimensional space. Then the LSTM model optimized the cell structure was used to extract features, train data and predict intrusion detection types. The experimental results show that compared with several classical methods, the accuracy of network intrusion detection is improved by 2% on average, and the false alarm rates are lower.

Network Attacks Detection using Deep neural network

Over the past decades, Internet and information technologies have elevated security issues due to huge use of networks. Because of this advance information and communication and sharing information the threats of cyber security has been increasing daily. Intrusion Detection System (IDS) consider one of the most critical security component which detects network security breaches in organizations. However, a lot of challenges raise while implementing dynamics and effective NIDS for unknown and unpredictable attacks. Considering machine learning approach to develop an effective and flexible IDS. A proposed deep neural network model to increase the effectiveness of intrusions detection system.