Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures
<p dir="ltr">Computer viruses, malicious, and other hostile attacks can affect a computer network. Intrusion detection is a key component of network security as an active defence technology. Traditional intrusion detection systems struggle with issues like poor accuracy, ineffective...
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2023
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| _version_ | 1864513532407578624 |
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| author | Irfan Ali Kandhro (17541876) |
| author2 | Sultan M. Alanazi (17541879) Fayyaz Ali (17541882) Asadullah Kehar (17541885) Kanwal Fatima (17541888) Mueen Uddin (4903510) Shankar Karuppayah (17541891) |
| author2_role | author author author author author author |
| author_facet | Irfan Ali Kandhro (17541876) Sultan M. Alanazi (17541879) Fayyaz Ali (17541882) Asadullah Kehar (17541885) Kanwal Fatima (17541888) Mueen Uddin (4903510) Shankar Karuppayah (17541891) |
| author_role | author |
| dc.creator.none.fl_str_mv | Irfan Ali Kandhro (17541876) Sultan M. Alanazi (17541879) Fayyaz Ali (17541882) Asadullah Kehar (17541885) Kanwal Fatima (17541888) Mueen Uddin (4903510) Shankar Karuppayah (17541891) |
| dc.date.none.fl_str_mv | 2023-01-20T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2023.3238664 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Detection_of_Real-Time_Malicious_Intrusions_and_Attacks_in_IoT_Empowered_Cybersecurity_Infrastructures/24717438 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Cybersecurity and privacy Distributed computing and systems software Machine learning Intrusion detection Intrusion detection system (IDS) Deep learning Security Security attacks Internet of Things Machine learning Computer security Cybersecurity Anomaly detection |
| dc.title.none.fl_str_mv | Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Computer viruses, malicious, and other hostile attacks can affect a computer network. Intrusion detection is a key component of network security as an active defence technology. Traditional intrusion detection systems struggle with issues like poor accuracy, ineffective detection, a high percentage of false positives, and an inability to handle new types of intrusions. To address these issues, we propose a deep learning-based novel method to detect cybersecurity vulnerabilities and breaches in cyber-physical systems. The proposed framework contrasts the unsupervised and deep learning-based discriminative approaches. This paper presents a generative adversarial network to detect cyber threats in IoT-driven IICs networks. The results demonstrate a performance increase of approximately 95% to 97% in terms of accuracy, reliability, and efficiency in detecting all types of attacks with a dropout value of 0.2 and an epoch value of 25. The output of well-known state-of-the-art DL classifiers achieved the highest true rate (TNR) and highest detection rate (HDR) when detecting the following attacks: (BruteForceXXS, BruteForceWEB, DoS_Hulk_Attack, and DOS_LOIC_HTTP_Attack) on the NSL-KDD, KDDCup99, and UNSW-NB15 datasets. It also maintained the confidentiality and integrity of users’ and systems’ sensitive information during the training and testing phases.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3238664" target="_blank">https://dx.doi.org/10.1109/access.2023.3238664</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_7ddefa4466a7182b03ca360ec1b9de7c |
| identifier_str_mv | 10.1109/access.2023.3238664 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24717438 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity InfrastructuresIrfan Ali Kandhro (17541876)Sultan M. Alanazi (17541879)Fayyaz Ali (17541882)Asadullah Kehar (17541885)Kanwal Fatima (17541888)Mueen Uddin (4903510)Shankar Karuppayah (17541891)Information and computing sciencesCybersecurity and privacyDistributed computing and systems softwareMachine learningIntrusion detectionIntrusion detection system (IDS)Deep learningSecuritySecurity attacksInternet of ThingsMachine learningComputer securityCybersecurityAnomaly detection<p dir="ltr">Computer viruses, malicious, and other hostile attacks can affect a computer network. Intrusion detection is a key component of network security as an active defence technology. Traditional intrusion detection systems struggle with issues like poor accuracy, ineffective detection, a high percentage of false positives, and an inability to handle new types of intrusions. To address these issues, we propose a deep learning-based novel method to detect cybersecurity vulnerabilities and breaches in cyber-physical systems. The proposed framework contrasts the unsupervised and deep learning-based discriminative approaches. This paper presents a generative adversarial network to detect cyber threats in IoT-driven IICs networks. The results demonstrate a performance increase of approximately 95% to 97% in terms of accuracy, reliability, and efficiency in detecting all types of attacks with a dropout value of 0.2 and an epoch value of 25. The output of well-known state-of-the-art DL classifiers achieved the highest true rate (TNR) and highest detection rate (HDR) when detecting the following attacks: (BruteForceXXS, BruteForceWEB, DoS_Hulk_Attack, and DOS_LOIC_HTTP_Attack) on the NSL-KDD, KDDCup99, and UNSW-NB15 datasets. It also maintained the confidentiality and integrity of users’ and systems’ sensitive information during the training and testing phases.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3238664" target="_blank">https://dx.doi.org/10.1109/access.2023.3238664</a></p>2023-01-20T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2023.3238664https://figshare.com/articles/journal_contribution/Detection_of_Real-Time_Malicious_Intrusions_and_Attacks_in_IoT_Empowered_Cybersecurity_Infrastructures/24717438CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/247174382023-01-20T03:00:00Z |
| spellingShingle | Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures Irfan Ali Kandhro (17541876) Information and computing sciences Cybersecurity and privacy Distributed computing and systems software Machine learning Intrusion detection Intrusion detection system (IDS) Deep learning Security Security attacks Internet of Things Machine learning Computer security Cybersecurity Anomaly detection |
| status_str | publishedVersion |
| title | Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures |
| title_full | Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures |
| title_fullStr | Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures |
| title_full_unstemmed | Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures |
| title_short | Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures |
| title_sort | Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures |
| topic | Information and computing sciences Cybersecurity and privacy Distributed computing and systems software Machine learning Intrusion detection Intrusion detection system (IDS) Deep learning Security Security attacks Internet of Things Machine learning Computer security Cybersecurity Anomaly detection |