RETRACTED CHAPTER: Assessing Deep Neural Network and Shallow for Network Intrusion Detection Systems in Cyber Security

Mandru, Deena Babu; Safali, M. Aruna; Sai, N. Raghavendra; Kumar, G. Sai Chaitanya

doi:10.1007/978-981-16-3728-5_52

Cited by 16 publications

(4 citation statements)

References 14 publications

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“…In a parallel line of research, Zhou et al [24]. introduced MR-DepressNet, a groundbreaking deep regression network that harnesses visual features to estimate depression severity.…”

Section: Background Studymentioning

confidence: 99%

ADHD Disease Detection in Children Using Pose Estimation Technique

Charan,

Sathvika,

Reddy

et al. 2024

Int. J. Res. Publ. Rev.

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“…In a parallel line of research, Zhou et al [24]. introduced MR-DepressNet, a groundbreaking deep regression network that harnesses visual features to estimate depression severity.…”

Section: Background Studymentioning

confidence: 99%

ADHD Disease Detection in Children Using Pose Estimation Technique

Charan,

Sathvika,

Reddy

et al. 2024

Int. J. Res. Publ. Rev.

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“…The author [12] proposed a novel model named assessing deep neural network and shallow for network intrusion detection systems in cyber security (ADNN). After a detailed study of several network intrusion detection models such as DNN1, DNN2, DNN3, DNN4, DNN5, adaboost, decision tree, K-nearest neighbour, linear regression, naïve bayes, random forest, support vector machine-linear and radial basis function (RBF) kernel models with DARPA/KDDCup-'99 datasets, a 7-layer based deep learning neural network architecture is proposed in ADNN work for network intrusion detection.…”

Section: Related Workmentioning

confidence: 99%

Parallel ABILSTM and CBIGRU Ensemble Network Intrusion Detection System

2024

IJIES

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Perpetual improvements are happening in communication networks, both in hardware and software. This constant improvement provides better communication speeds and improves the user experience. The concept of smart cities is a boon that evolves towards the complete automation of the city and the responsible conservation of natural resources. Modern communication technologies support the gradual growth of smart cities simultaneously; the changes in the network architectures open a gateway to attackers, making the cyber network vulnerable. Manual construction and enhancement of network security schemes and protocols are tedious, time-consuming processes that may not be applicable in real time. This dynamic non-deterministic problem can be solved by combining the combination of Artificial Intelligence techniques that can do marvels in anomaly detection in a typical cyber network behaviour. This work, named parallel ABILSTM and CBIGRU ensemble network intrusion detection system (PACENIDS), is intended to use an ensemble of Altered Bi-directional Long Short-Term Memory (ABILSTM) and Customized Bi-directional Gated Recurrent Unit (CBIGRU) to improve the detection of real-time network intrusion attempts with more accuracy and to prevent the network from intimidating attacks on time. The parallel operational nature of the proposed algorithm ensures a swifter performance towards attack detection. This paper uses an impact based fuzzy feature selection algorithm to improve the performance of the proposed approach. The NSL-KDD dataset is used to evaluate the suggested approach. The proposed PACENIDS achieves 96.59% for binary classification, 94.47% and 97.67% for multiclass without and with feature selection, respectively. The experimental result shows that the suggested ensemble approach increases accuracy and precision and reduces the false alarm rate in the target intruder detection system.

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“…Additionally, many problems can be automatically fixed by ANNs without the need for human intervention. As a result, your team will spend less time investigating false positives and neutralizing small threats [32].…”

Section: Ids/ipss (Intrusion Detection and Prevention Systems)mentioning

confidence: 99%

Artificial Neural Networks: An Overview

Qamar,

Ali Zardari

2023

Mesopotamian Journal of Computer Science

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Neural networks, also known as artificial neural networks (ANNs) or artificially generated neural networks (SNNs) are a subset of machine learning that provide the foundation of deep learning techniques. Their name and form are inspired by the human brain, and they replicate the way real neurons communicate with one another. Artificial neural networks (ANNs) are massively parallel systems comprised of a huge number of interconnected basic processors. This paper discuss about the artificial neural network and its basic types. This article explains the ANN and its basic outlines the fundamental neuron and the artificial computer model. It describes network structures and learning methods, as well as some of the most popular ANNs.

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RETRACTED CHAPTER: Assessing Deep Neural Network and Shallow for Network Intrusion Detection Systems in Cyber Security

Cited by 16 publications

References 14 publications

ADHD Disease Detection in Children Using Pose Estimation Technique

ADHD Disease Detection in Children Using Pose Estimation Technique

Parallel ABILSTM and CBIGRU Ensemble Network Intrusion Detection System

Artificial Neural Networks: An Overview

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