Network Intrusion Detection System using Deep Learning

Ashiku, Lirim; Dağli, Ci̇han H.

doi:10.1016/j.procs.2021.05.025

Cited by 177 publications

(52 citation statements)

References 15 publications

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“…This determines the length of training and the model's generalization ability. According to S. Keskar and coworkers, using large data sets reduces the generalization ability of models [51,58,59]. As a result, big data models lose generalizability.…”

Section: Hyperparametersmentioning

confidence: 99%

Augmenting IoT Intrusion Detection System Performance Using Deep Neural燦etwork

Sayed¹,

Shoaib²,

Ahmed³

et al. 2023

Computers, Materials &Amp; Continua

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Due to their low power consumption and limited computing power, Internet of Things (IoT) devices are difficult to secure. Moreover, the rapid growth of IoT devices in homes increases the risk of cyber-attacks. Intrusion detection systems (IDS) are commonly employed to prevent cyberattacks. These systems detect incoming attacks and instantly notify users to allow for the implementation of appropriate countermeasures. Attempts have been made in the past to detect new attacks using machine learning and deep learning techniques, however, these efforts have been unsuccessful. In this paper, we propose two deep learning models to automatically detect various types of intrusion attacks in IoT networks. Specifically, we experimentally evaluate the use of two Convolutional Neural Networks (CNN) to detect nine distinct types of attacks listed in the NF-UNSW-NB15-v2 dataset. To accomplish this goal, the network stream data were initially converted to twodimensional images, which were then used to train the neural network models. We also propose two baseline models to demonstrate the performance of the proposed models. Generally, both models achieve high accuracy in detecting the majority of these nine attacks.

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Section: Hyperparametersmentioning

confidence: 99%

Augmenting IoT Intrusion Detection System Performance Using Deep Neural燦etwork

Sayed¹,

Shoaib²,

Ahmed³

et al. 2023

Computers, Materials &Amp; Continua

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“…In [38] developed an adaptive and resilient model for NIDS based on deep learning architectures to improve the detection and classify network attacks. The focus is on how deep learning or Deep Neural Networks (DNNs) can facilitate flexible IDS with the ability to learn to detect recognized and new or zero network behavioral features, thereby taking out intrusive systems and reducing compromise risks.…”

Section: Related Workmentioning

confidence: 99%

Cyber Intrusion Detection System Based on a Multiobjective Binary Bat Algorithm for Feature Selection and Enhanced Bat Algorithm for Parameter Optimization in Neural Networks

et al. 2022

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The staggering development of cyber threats has propelled experts, professionals and specialists in the field of security into the development of more dependable protection systems, including effective intrusion detection system (IDS) mechanisms which are equipped for boosting accurately detected threats and limiting erroneously detected threats simultaneously. Nonetheless, the proficiency of the IDS framework depends essentially on extracted features from network traffic and an effective classifier of the traffic into abnormal or normal traffic. The prime impetus of this study is to increase the performance of the IDS on networks by building a two-phase framework to reinforce and subsequently enhance detection rate and diminish the rate of false alarm. The initial stage utilizes the developed algorithm of a proficient wrapperapproach-based feature selection which is created on a multi-objective BAT algorithm (MOBBAT). The subsequent stage utilizes the features obtained from the initial stage to categorize the traffic based on the newly upgraded BAT algorithm (EBAT) for training multilayer perceptron (EBATMLP), to improve the IDS performance. The resulting methodology is known as the (MOB-EBATMLP). The efficiency of our proposition has been assessed by utilizing the mainstream benchmarked datasets: NLS-KDD, ISCX2012, UNSW-NB15, KDD CUP 1999, and CICIDS2017 which are established as standard datasets for evaluating IDS. The outcome of our experimental analysis demonstrates a noteworthy advancement in network IDS above other techniques. INDEX TERMSIntrusion detection system (IDS), bat algorithm (BAT), metaheuristic algorithm (MA), feature selection (FS), multi-objective optimization (MOO), multilayer perceptron (MLP)

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“…The methods implemented are MinMax, Robust scaler, Standard Scaler, L2 standardization, and Yeo-Johnson. The MinMax [39] approach can mathematically be represented as (1).…”

Section: B Data Transformationmentioning

confidence: 99%

“…T HE pervasive use of interconnected computer systems has become an irreplaceable aspect of organizational and daily life activities. Concurrently, it had led to concerns about the online privacy and security of the users [1] [2]. As per recent surveys, the reported cyberattacks in 2021 were approximately 5.1 billion [3] [4].…”

Section: Introductionmentioning

confidence: 99%

Tier-Based Optimization for Synthesized Network Intrusion Detection System

Siddiqi

Pak

2022

IEEE Access

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The innovation and evolution of hacking methodologies have led to a sharp rise in cyber attacks, highlighting the need for enhanced network security approaches. Network intrusion detection systems based on machine learning are playing a significant role in the domain of network security. However, designing an optimal framework for a network intrusion detection system is an ongoing concern. In this study, an optimal framework for a network intrusion detection system based on image processing is proposed. The framework is a fusion of augmented feature selection flow with an image transformation and enhancement methodology. Initially, the proposed framework reduces the number of features to achieve overall efficiency. Later, the non-image data is transformed into images. The transformed images are then enhanced for achieving effective anomaly detection based on a deep-learning classifier. The proposed method is implemented on three diverse benchmark datasets of intrusion detection. To illustrate the efficiency of the proposed framework it is compared with some of the most recent publications on imageprocessing-based network intrusion detection systems.

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Network Intrusion Detection System using Deep Learning

Cited by 177 publications

References 15 publications

Augmenting IoT Intrusion Detection System Performance Using Deep Neural燦etwork

Augmenting IoT Intrusion Detection System Performance Using Deep Neural燦etwork

Cyber Intrusion Detection System Based on a Multiobjective Binary Bat Algorithm for Feature Selection and Enhanced Bat Algorithm for Parameter Optimization in Neural Networks

Tier-Based Optimization for Synthesized Network Intrusion Detection System

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