AI for Cybersecurity: Robust models for Authentication, Threat and Anomaly Detection
Cybersecurity models include provisions for legitimate user and agent authentication, as well as algorithms for detecting external threats, such as intruders and malicious software. In particular, we can define a continuum of cybersecurity measures ranging from user identification to risk-based and...
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| Format: | Online |
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| Idioma: | anglès |
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MDPI - Multidisciplinary Digital Publishing Institute
2023
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| Accés en línia: | ONIX_20230808_9783036582641_27 |
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| collection | Directory of Open Access Books |
| description | Cybersecurity models include provisions for legitimate user and agent authentication, as well as algorithms for detecting external threats, such as intruders and malicious software. In particular, we can define a continuum of cybersecurity measures ranging from user identification to risk-based and multilevel authentication, complex application and network monitoring, and anomaly detection. We refer to this as the “anomaly detection continuum”. Machine learning and other artificial intelligence technologies can provide powerful tools for addressing such issues, but the robustness of the obtained models is often ignored or underestimated. On the one hand, AI-based algorithms can be replicated by malicious opponents, and attacks can be devised so that they will not be detected (evasion attacks). On the other hand, data and system contexts can be modified by attackers to influence the countermeasures obtained from machine learning and render them ineffective (active data poisoning). This Special Issue presents ten papers that can be grouped under five main topics: (1) Cyber–Physical Systems (CPSs), (2) Intrusion Detection, (3) Malware Analysis, (4) Access Control, and (5) Threat intelligence.AI is increasingly being used in cybersecurity, with three main directions of current research: (1) new areas of cybersecurity are being addressed, such as CPS security and threat intelligence; (2) more stable and consistent results are being presented, sometimes with surprising accuracy and effectiveness; and (3) the presence of an AI-aware adversary is recognized and analyzed, producing more robust solutions. |
| format | Online |
| id | doab-20.500.12854ir-112521 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1125212024-04-11T15:10:56Z AI for Cybersecurity: Robust models for Authentication, Threat and Anomaly Detection Bergadano, Francesco Giacinto, Giorgio Internet of Things cybersecurity cyber threats malware detection machine learning network traffic cooperative intelligent transportation systems (cITSs) IDS vehicular ad-hoc networks (VANET) adaptive model deep belief network (DBN) NIDS deep learning false negative rate artificial neural network MITRE ATT&CK Matrix techniques classification BERT-based multi-labeling formal ontology risk identification vulnerability portable executable malware tree-based ensemble performance comparison statistical significance test adversarial examples face recognition mask matrix targeted attack non-targeted attack anomaly detection attack detection cyber-physical system datasets evaluation metrics biometric cryptosystem iris identification error-correcting codes intrusion detection smart grid neural networks n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials Cybersecurity models include provisions for legitimate user and agent authentication, as well as algorithms for detecting external threats, such as intruders and malicious software. In particular, we can define a continuum of cybersecurity measures ranging from user identification to risk-based and multilevel authentication, complex application and network monitoring, and anomaly detection. We refer to this as the “anomaly detection continuum”. Machine learning and other artificial intelligence technologies can provide powerful tools for addressing such issues, but the robustness of the obtained models is often ignored or underestimated. On the one hand, AI-based algorithms can be replicated by malicious opponents, and attacks can be devised so that they will not be detected (evasion attacks). On the other hand, data and system contexts can be modified by attackers to influence the countermeasures obtained from machine learning and render them ineffective (active data poisoning). This Special Issue presents ten papers that can be grouped under five main topics: (1) Cyber–Physical Systems (CPSs), (2) Intrusion Detection, (3) Malware Analysis, (4) Access Control, and (5) Threat intelligence.AI is increasingly being used in cybersecurity, with three main directions of current research: (1) new areas of cybersecurity are being addressed, such as CPS security and threat intelligence; (2) more stable and consistent results are being presented, sometimes with surprising accuracy and effectiveness; and (3) the presence of an AI-aware adversary is recognized and analyzed, producing more robust solutions. 2023-08-08T15:24:50Z 2023-08-08T15:24:50Z 2023 book ONIX_20230808_9783036582641_27 9783036582641 9783036582658 https://directory.doabooks.org/handle/20.500.12854/112521 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/7647 https://mdpi.com/books/pdfview/book/7647 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-8265-8 10.3390/books978-3-0365-8265-8 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036582641 9783036582658 208 Basel open access |
| spellingShingle | Internet of Things cybersecurity cyber threats malware detection machine learning network traffic cooperative intelligent transportation systems (cITSs) IDS vehicular ad-hoc networks (VANET) adaptive model deep belief network (DBN) NIDS deep learning false negative rate artificial neural network MITRE ATT&CK Matrix techniques classification BERT-based multi-labeling formal ontology risk identification vulnerability portable executable malware tree-based ensemble performance comparison statistical significance test adversarial examples face recognition mask matrix targeted attack non-targeted attack anomaly detection attack detection cyber-physical system datasets evaluation metrics biometric cryptosystem iris identification error-correcting codes intrusion detection smart grid neural networks n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials AI for Cybersecurity: Robust models for Authentication, Threat and Anomaly Detection |
| title | AI for Cybersecurity: Robust models for Authentication, Threat and Anomaly Detection |
| title_full | AI for Cybersecurity: Robust models for Authentication, Threat and Anomaly Detection |
| title_fullStr | AI for Cybersecurity: Robust models for Authentication, Threat and Anomaly Detection |
| title_full_unstemmed | AI for Cybersecurity: Robust models for Authentication, Threat and Anomaly Detection |
| title_short | AI for Cybersecurity: Robust models for Authentication, Threat and Anomaly Detection |
| title_sort | ai for cybersecurity robust models for authentication threat and anomaly detection |
| topic | Internet of Things cybersecurity cyber threats malware detection machine learning network traffic cooperative intelligent transportation systems (cITSs) IDS vehicular ad-hoc networks (VANET) adaptive model deep belief network (DBN) NIDS deep learning false negative rate artificial neural network MITRE ATT&CK Matrix techniques classification BERT-based multi-labeling formal ontology risk identification vulnerability portable executable malware tree-based ensemble performance comparison statistical significance test adversarial examples face recognition mask matrix targeted attack non-targeted attack anomaly detection attack detection cyber-physical system datasets evaluation metrics biometric cryptosystem iris identification error-correcting codes intrusion detection smart grid neural networks n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials |
| topic_facet | Internet of Things cybersecurity cyber threats malware detection machine learning network traffic cooperative intelligent transportation systems (cITSs) IDS vehicular ad-hoc networks (VANET) adaptive model deep belief network (DBN) NIDS deep learning false negative rate artificial neural network MITRE ATT&CK Matrix techniques classification BERT-based multi-labeling formal ontology risk identification vulnerability portable executable malware tree-based ensemble performance comparison statistical significance test adversarial examples face recognition mask matrix targeted attack non-targeted attack anomaly detection attack detection cyber-physical system datasets evaluation metrics biometric cryptosystem iris identification error-correcting codes intrusion detection smart grid neural networks n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials |
| url | ONIX_20230808_9783036582641_27 |