Optimization of Intrusion Detection Systems Determined by Ameliorated HNADAM-SGD Algorithm
Information security is of pivotal concern for consistently streaming information over the widespread internetwork. The bottleneck flow of incoming and outgoing data traffic introduces the issues of malicious activities taken place by intruders, hackers and attackers in the form of authenticity obst...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/4/507 |
| _version_ | 1797480973908049920 |
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| author | Shyla Shyla Vishal Bhatnagar Vikram Bali Shivani Bali |
| author_facet | Shyla Shyla Vishal Bhatnagar Vikram Bali Shivani Bali |
| author_sort | Shyla Shyla |
| collection | DOAJ |
| description | Information security is of pivotal concern for consistently streaming information over the widespread internetwork. The bottleneck flow of incoming and outgoing data traffic introduces the issues of malicious activities taken place by intruders, hackers and attackers in the form of authenticity obstruction, gridlocking data traffic, vandalizing data and crashing the established network. The issue of emerging suspicious activities is managed by the domain of Intrusion Detection Systems (IDS). The IDS consistently monitors the network for the identification of suspicious activities, and generates alarm and indication in the presence of malicious threats and worms. The performance of IDS is improved by using different machine learning algorithms. In this paper, the Nesterov-Accelerated Adaptive Moment Estimation–Stochastic Gradient Descent (HNADAM-SDG) algorithm is proposed to determine the performance of Intrusion Detection Systems IDS. The algorithm is used to optimize IDS systems by hybridization and tuning of hyperparameters. The performance of algorithm is compared with other classification algorithms such as logistic regression, ridge classifier and ensemble algorithms where the experimental analysis and computations show the improved accuracy with 99.8%, sensitivity with 99.7%, and specificity with 99.5%. |
| first_indexed | 2024-03-09T22:07:54Z |
| format | Article |
| id | doaj.art-d6a5b90c8cef4690a53d532936b6079f |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T22:07:54Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-d6a5b90c8cef4690a53d532936b6079f2023-11-23T19:38:21ZengMDPI AGElectronics2079-92922022-02-0111450710.3390/electronics11040507Optimization of Intrusion Detection Systems Determined by Ameliorated HNADAM-SGD AlgorithmShyla Shyla0Vishal Bhatnagar1Vikram Bali2Shivani Bali3NSUT East Campus (Formerly Ambedkar Institute of Advanced Communication Technologies and Research), Guru Gobind Singh Indraprastha University, New Delhi 110031, IndiaNSUT East Campus Formerly Ambedkar Institute of Advanced Communication Technologies and Research, New Delhi 110001, IndiaDepartment of Computer Science and Engineering, JSS Academy of Technical Education, Noida 201309, IndiaJaipuria Institute for Management, Noida 201309, IndiaInformation security is of pivotal concern for consistently streaming information over the widespread internetwork. The bottleneck flow of incoming and outgoing data traffic introduces the issues of malicious activities taken place by intruders, hackers and attackers in the form of authenticity obstruction, gridlocking data traffic, vandalizing data and crashing the established network. The issue of emerging suspicious activities is managed by the domain of Intrusion Detection Systems (IDS). The IDS consistently monitors the network for the identification of suspicious activities, and generates alarm and indication in the presence of malicious threats and worms. The performance of IDS is improved by using different machine learning algorithms. In this paper, the Nesterov-Accelerated Adaptive Moment Estimation–Stochastic Gradient Descent (HNADAM-SDG) algorithm is proposed to determine the performance of Intrusion Detection Systems IDS. The algorithm is used to optimize IDS systems by hybridization and tuning of hyperparameters. The performance of algorithm is compared with other classification algorithms such as logistic regression, ridge classifier and ensemble algorithms where the experimental analysis and computations show the improved accuracy with 99.8%, sensitivity with 99.7%, and specificity with 99.5%.https://www.mdpi.com/2079-9292/11/4/507Intrusion Detection System (IDS)HNADAM-SDG (Hybrid Nesterov-Accelerated Adaptive Moment Estimation–Stochastic Gradient Descent)Network-Based Intrusion Detection System (NIDS) |
| spellingShingle | Shyla Shyla Vishal Bhatnagar Vikram Bali Shivani Bali Optimization of Intrusion Detection Systems Determined by Ameliorated HNADAM-SGD Algorithm Electronics Intrusion Detection System (IDS) HNADAM-SDG (Hybrid Nesterov-Accelerated Adaptive Moment Estimation–Stochastic Gradient Descent) Network-Based Intrusion Detection System (NIDS) |
| title | Optimization of Intrusion Detection Systems Determined by Ameliorated HNADAM-SGD Algorithm |
| title_full | Optimization of Intrusion Detection Systems Determined by Ameliorated HNADAM-SGD Algorithm |
| title_fullStr | Optimization of Intrusion Detection Systems Determined by Ameliorated HNADAM-SGD Algorithm |
| title_full_unstemmed | Optimization of Intrusion Detection Systems Determined by Ameliorated HNADAM-SGD Algorithm |
| title_short | Optimization of Intrusion Detection Systems Determined by Ameliorated HNADAM-SGD Algorithm |
| title_sort | optimization of intrusion detection systems determined by ameliorated hnadam sgd algorithm |
| topic | Intrusion Detection System (IDS) HNADAM-SDG (Hybrid Nesterov-Accelerated Adaptive Moment Estimation–Stochastic Gradient Descent) Network-Based Intrusion Detection System (NIDS) |
| url | https://www.mdpi.com/2079-9292/11/4/507 |
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