{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T16:33:57Z","timestamp":1780763637320,"version":"3.54.1"},"reference-count":84,"publisher":"Springer Science and Business Media LLC","issue":"33","license":[{"start":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T00:00:00Z","timestamp":1759881600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T00:00:00Z","timestamp":1759881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100019186","name":"HORIZON EUROPE Innovative Europe","doi-asserted-by":"publisher","award":["830943"],"award-info":[{"award-number":["830943"]}],"id":[{"id":"10.13039\/100019186","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007751","name":"Akademia G\u00f3rniczo-Hutnicza im. Stanislawa Staszica","doi-asserted-by":"publisher","award":["\u201cExcellence initiative - research university\u201d"],"award-info":[{"award-number":["\u201cExcellence initiative - research university\u201d"]}],"id":[{"id":"10.13039\/501100007751","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Artificial intelligence, machine learning, and cybersecurity are the topics of discussion of contemporary information technology sector and computing research. This study investigates the integration of machine learning-based artificial intelligence in the context of cybersecurity. This paper presents an overview of the recent literature, focusing on selected popular areas related to the challenges and opportunities that such implementations introduce. The authors also assess how selected problems related to the application of machine learning algorithms affect the real effectiveness represented by the resulting models. To support this analysis, an experimental study was conducted using a real-world cybersecurity system. This demonstration illustrates the practical implementation of a machine learning-based software solution in cybersecurity and highlights the potential challenges encountered during such implementations.<\/jats:p>","DOI":"10.1007\/s00521-025-11604-9","type":"journal-article","created":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T15:05:41Z","timestamp":1759935941000},"page":"27931-27956","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Cybersecurity challenges and opportunities of machine learning-based artificial intelligence"],"prefix":"10.1007","volume":"37","author":[{"given":"Pawel","family":"Czaja","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bartlomiej","family":"Gdowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcin","family":"Niemiec","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wim","family":"Mees","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikolai","family":"Stoianov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Konstantinos","family":"Votis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vyacheslav","family":"Kharchenko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vasilis","family":"Katos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matteo","family":"Merialdo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,8]]},"reference":[{"key":"11604_CR1","doi-asserted-by":"publisher","unstructured":"Kharchenko V, Illiashenko O, Fesenko H, Babeshko I (2022) AI cybersecurity assurance for autonomous transport systems: scenario, model, and IMECA-based analysis. In: Multimedia communications, services and security, pp 66\u201379. https:\/\/doi.org\/10.1007\/978-3-031-20215-5_6","DOI":"10.1007\/978-3-031-20215-5_6"},{"key":"11604_CR2","unstructured":"Meyer N, Hammerschmidt M, Weiger W (2022) AI and the vulnerable. In: Pacific Asia conference on information systems (PACIS) 2022 Proceedings. https:\/\/aisel.aisnet.org\/pacis2022\/167"},{"key":"11604_CR3","doi-asserted-by":"publisher","unstructured":"Cotroneo D, Improta C, Liguori P, Natella R (2024) Vulnerabilities in AI code generators: exploring targeted data poisoning attacks. In: Proceedings of the 32nd IEEE\/ACM international conference on program comprehension, pp 280\u2013292.https:\/\/doi.org\/10.1145\/3643916.3644416","DOI":"10.1145\/3643916.3644416"},{"key":"11604_CR4","doi-asserted-by":"publisher","unstructured":"de Azambuja AJG, Plesker C, Sch\u00fctzer K, Anderl R, Schleich B, Almeida VR (2023) Artificial intelligence-based cyber security in the context of industry 4.0\u2014a survey. Electronics 12(8) https:\/\/doi.org\/10.3390\/electronics12081920","DOI":"10.3390\/electronics12081920"},{"key":"11604_CR5","doi-asserted-by":"publisher","unstructured":"Yu N, Tuttle Z, Thurnau CJ, Mireku E (2020) AI-powered GUI attack and its defensive methods. In: Proceedings of the 2020 ACM southeast conference, pp 79\u201386. https:\/\/doi.org\/10.1145\/3374135.3385270","DOI":"10.1145\/3374135.3385270"},{"key":"11604_CR6","unstructured":"Mudgal A (2020) Intrusion-detection-system. https:\/\/github.com\/mudgalabhay\/Intrusion-Detection-System. Available 9 Dec 2024"},{"key":"11604_CR7","unstructured":"Gdowski B, Czaja P (2025) cybersecurity_challenges_and_opportunities_of_AI. https:\/\/github.com\/bgdowski-agh\/cybersecurity_challenges_and_opportunities_of_AI. Available 9 Dec 2024"},{"key":"11604_CR8","doi-asserted-by":"publisher","first-page":"74720","DOI":"10.1109\/ACCESS.2020.2987435","volume":"8","author":"M Xue","year":"2020","unstructured":"Xue M, Yuan C, Wu H, Zhang Y, Liu W (2020) Machine learning security: threats, countermeasures, and evaluations. IEEE Access 8:74720\u201374742. https:\/\/doi.org\/10.1109\/ACCESS.2020.2987435","journal-title":"IEEE Access"},{"key":"11604_CR9","doi-asserted-by":"publisher","unstructured":"Liu Z, Liu Z, Yang X (2023) Poisoning attack based on data feature selection in federated learning. In: 2023 13th international conference on cloud computing, data science and engineering (Confluence), pp 106\u2013110. https:\/\/doi.org\/10.1109\/Confluence56041.2023.10048854","DOI":"10.1109\/Confluence56041.2023.10048854"},{"key":"11604_CR10","doi-asserted-by":"publisher","unstructured":"Al-Qudah R, Aloqaily M, Ouni B, Guizani M, Lestable T (2023) An incremental gray-box physical adversarial attack on neural network training. In: ICC 2023\u2014IEEE international conference on communications, pp 45\u201350. https:\/\/doi.org\/10.1109\/ICC45041.2023.10278837","DOI":"10.1109\/ICC45041.2023.10278837"},{"key":"11604_CR11","doi-asserted-by":"publisher","unstructured":"Sasaki S, Hidano S, Uchibayashi T, Suganuma T, Hiji M, Kiyomoto S (2019) On embedding backdoor in malware detectors using machine learning. In: 2019 17th international conference on privacy, security and trust (PST), pp 1\u20135. https:\/\/doi.org\/10.1109\/PST47121.2019.8949034","DOI":"10.1109\/PST47121.2019.8949034"},{"key":"11604_CR12","doi-asserted-by":"publisher","unstructured":"Xu T, Li Y, Jiang Y, Xia S-T (2023) BATT: backdoor attack with transformation-based triggers. In: ICASSP 2023\u20132023 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1\u20135. https:\/\/doi.org\/10.1109\/ICASSP49357.2023.10096034","DOI":"10.1109\/ICASSP49357.2023.10096034"},{"key":"11604_CR13","unstructured":"Chathoth AK, Lee S (2025) PCAP-backdoor: backdoor poisoning generator for network traffic in CPS\/IoT environments. https:\/\/arxiv.org\/abs\/2501.15563"},{"key":"11604_CR14","doi-asserted-by":"publisher","unstructured":"Zhou H, He L (2022) Network traffic adversarial example generation method based on one pixel attack. In: 2022 18th international conference on computational intelligence and security (CIS), pp 162\u2013165. https:\/\/doi.org\/10.1109\/CIS58238.2022.00041","DOI":"10.1109\/CIS58238.2022.00041"},{"key":"11604_CR15","doi-asserted-by":"publisher","unstructured":"Zou A, Wang Z, Carlini N, Nasr M, Kolter JZ, Fredrikson M (2023) Universal and transferable adversarial attacks on aligned language models. arXiv preprint https:\/\/doi.org\/10.48550\/arXiv.2307.15043","DOI":"10.48550\/arXiv.2307.15043"},{"key":"11604_CR16","doi-asserted-by":"publisher","unstructured":"Chen J, Gungor O, Shang Z, Li E, Rosing T, (2025) DYNAMITE: dynamic defense selection for enhancing machine learning-based intrusion detection against adversarial attacks. In: IEEE security and privacy workshops (SPW). IEEE computer society Los Alamitos, CA, USA, pp 213\u2013219. https:\/\/doi.org\/10.1109\/SPW67851.2025.00028","DOI":"10.1109\/SPW67851.2025.00028"},{"key":"11604_CR17","doi-asserted-by":"publisher","unstructured":"Koball C, Wang Y (2025) Evasion attacks on tree-based machine learning models. In: 2025 IEEE international conference on consumer electronics (ICCE), pp 1\u20136. https:\/\/doi.org\/10.1109\/ICCE63647.2025.10929896","DOI":"10.1109\/ICCE63647.2025.10929896"},{"key":"11604_CR18","doi-asserted-by":"publisher","first-page":"994","DOI":"10.48550\/arXiv.2110.15122","volume":"34","author":"X Jin","year":"2021","unstructured":"Jin X, Chen P-Y, Hsu C-Y, Yu C-M, Chen T (2021) CAFE: catastrophic data leakage in vertical federated learning. Adv Neural Inf Process Syst 34:994\u20131006. https:\/\/doi.org\/10.48550\/arXiv.2110.15122","journal-title":"Adv Neural Inf Process Syst"},{"key":"11604_CR19","doi-asserted-by":"publisher","unstructured":"Veprytska O, Kharchenko V (2022) AI powered attacks against AI powered protection: classification, scenarios and risk analysis. In: 2022 12th international conference on dependable systems, services and technologies (DESSERT), pp 1\u20137. https:\/\/doi.org\/10.1109\/DESSERT58054.2022.10018770","DOI":"10.1109\/DESSERT58054.2022.10018770"},{"issue":"1","key":"11604_CR20","doi-asserted-by":"publisher","first-page":"2037254","DOI":"10.1080\/08839514.2022.2037254","volume":"36","author":"BG Guembe","year":"2022","unstructured":"Guembe BG, Azeta A, Misra S, Osamor VC, Fernandez-Sanz L, Pospelova V (2022) The emerging threat of AI-driven cyber attacks: a review. Appl Artif Intell 36(1):2037254. https:\/\/doi.org\/10.1080\/08839514.2022.2037254","journal-title":"Appl Artif Intell"},{"key":"11604_CR21","doi-asserted-by":"publisher","first-page":"80218","DOI":"10.1109\/ACCESS.2023.3300381","volume":"11","author":"M Gupta","year":"2023","unstructured":"Gupta M, Akiri C, Aryal K, Parker E, Praharaj L (2023) From ChatGPT to ThreatGPT: impact of generative AI in cybersecurity and privacy. IEEE Access 11:80218\u201380245. https:\/\/doi.org\/10.1109\/ACCESS.2023.3300381","journal-title":"IEEE Access"},{"key":"11604_CR22","doi-asserted-by":"publisher","unstructured":"Raza M, Jayasinghe ND, Muslam MMA (2021) A comprehensive review on email spam classification using machine learning algorithms. In: 2021 international conference on information networking (ICOIN), pp 327\u2013332. https:\/\/doi.org\/10.1109\/ICOIN50884.2021.9334020","DOI":"10.1109\/ICOIN50884.2021.9334020"},{"key":"11604_CR23","doi-asserted-by":"publisher","unstructured":"Leghris C, Elaeraj O, Renault E (2019) Improved security intrusion detection using intelligent techniques. In: 2019 international conference on wireless networks and mobile communications (WINCOM), pp 1\u20135. https:\/\/doi.org\/10.1109\/WINCOM47513.2019.8942553","DOI":"10.1109\/WINCOM47513.2019.8942553"},{"key":"11604_CR24","doi-asserted-by":"publisher","unstructured":"Enigo VSF, Ganesh KT, Raj NNV, Sandeep D (2020) Hybrid intrusion detection system for detecting new attacks using machine learning. In: 2020 5th international conference on communication and electronics systems (ICCES), pp 567\u2013572. https:\/\/doi.org\/10.1109\/ICCES48766.2020.9137888","DOI":"10.1109\/ICCES48766.2020.9137888"},{"key":"11604_CR25","doi-asserted-by":"publisher","unstructured":"Barach J (2024) Enhancing intrusion detection with CNN attention using NSL-KDD dataset. In: 2024 artificial intelligence for business (AIxB), pp 15\u201320. https:\/\/doi.org\/10.1109\/AIxB62249.2024.00009","DOI":"10.1109\/AIxB62249.2024.00009"},{"key":"11604_CR26","doi-asserted-by":"publisher","unstructured":"Sun X, Zhang D, Qin H, Tang J, Yeh K-H (2022) Bridging the last-mile gap in network security via generating intrusion-specific detection patterns through machine learning. Sec Commun Netw. https:\/\/doi.org\/10.1155\/2022\/3990386","DOI":"10.1155\/2022\/3990386"},{"key":"11604_CR27","doi-asserted-by":"publisher","unstructured":"Ze-Dong Z, Hao-Tong S, Song-Jie W (2022) Network anomaly detection based on traffic clustering with group-entropy similarity. In: 2022 international symposium on networks, computers and communications (ISNCC), pp 1\u20135. https:\/\/doi.org\/10.1109\/ISNCC55209.2022.9851762","DOI":"10.1109\/ISNCC55209.2022.9851762"},{"key":"11604_CR28","doi-asserted-by":"publisher","unstructured":"Duan H, Hao M, Zhang X (2024) Anomaly detection method of telecommunication traffic data based on improved dynamic clustering algorithm. In: 2024 IEEE 6th advanced information management, communicates, electronic and automation control conference (IMCEC), vol 6, pp 675\u2013680. https:\/\/doi.org\/10.1109\/IMCEC59810.2024.10575279","DOI":"10.1109\/IMCEC59810.2024.10575279"},{"key":"11604_CR29","doi-asserted-by":"publisher","unstructured":"Liang D, Liu Q, Zhao B, Zhu Z, Liu D (2019) A clustering-SVM ensemble method for intrusion detection system. In: 2019 8th international symposium on next generation electronics (ISNE), pp 1\u20133. https:\/\/doi.org\/10.1109\/ISNE.2019.8896514","DOI":"10.1109\/ISNE.2019.8896514"},{"key":"11604_CR30","doi-asserted-by":"publisher","unstructured":"Younisse R, Al-Haija QA (2023) An empirical study on utilizing online K-means clustering for intrusion detection purposes. In: 2023 international conference on smart applications, communications and networking (SmartNets), pp 1\u20135. https:\/\/doi.org\/10.1109\/SmartNets58706.2023.10215737","DOI":"10.1109\/SmartNets58706.2023.10215737"},{"key":"11604_CR31","doi-asserted-by":"publisher","unstructured":"Chen Y (2023) Construction of a computer network fault analysis and intrusion detection system based on K-means clustering algorithm. In: 2023 8th international conference on information systems engineering (ICISE), pp 70\u201375. https:\/\/doi.org\/10.1109\/ICISE60366.2023.00022","DOI":"10.1109\/ICISE60366.2023.00022"},{"key":"11604_CR32","doi-asserted-by":"publisher","unstructured":"Liu Z, Luo M, Ma B (2022) Network intrusion detection based on RBF neural networks and fuzzy cluster. In: 2022 9th international conference on dependable systems and their applications (DSA), pp 236\u2013240. https:\/\/doi.org\/10.1109\/DSA56465.2022.00040","DOI":"10.1109\/DSA56465.2022.00040"},{"key":"11604_CR33","doi-asserted-by":"publisher","unstructured":"Mi T, Zhong L, Huang Y, Liu Y, Yu G (2023) A grid-based fuzzy C-means clustering algorithm with unknown number of clusters. In: 2023 5th international conference on artificial intelligence and computer applications (ICAICA), pp 182\u2013185. https:\/\/doi.org\/10.1109\/ICAICA58456.2023.10405508","DOI":"10.1109\/ICAICA58456.2023.10405508"},{"key":"11604_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/s44196-025-00750-6","author":"P Mamatha","year":"2025","unstructured":"Mamatha P, Balaji S, Anuraghav S (2025) Development of hybrid intrusion detection system leveraging ensemble stacked feature selectors and learning classifiers to mitigate the dos attacks. Int J Comput Intell Syst. https:\/\/doi.org\/10.1007\/s44196-025-00750-6","journal-title":"Int J Comput Intell Syst"},{"key":"11604_CR35","doi-asserted-by":"publisher","unstructured":"D\u2019Ambrosio G, Li W (2021) AdversarialDroid: a deep learning based malware detection approach for android system against adversarial example attacks. In: 2021 IEEE MIT undergraduate research technology conference (URTC), pp 1\u20135. https:\/\/doi.org\/10.1109\/URTC54388.2021.9701615","DOI":"10.1109\/URTC54388.2021.9701615"},{"key":"11604_CR36","doi-asserted-by":"publisher","unstructured":"Garg V, Yadav RK (2019) Malware detection based on API calls frequency. In: 2019 4th international conference on information systems and computer networks (ISCON), pp 400\u2013404. https:\/\/doi.org\/10.1109\/ISCON47742.2019.9036219","DOI":"10.1109\/ISCON47742.2019.9036219"},{"key":"11604_CR37","doi-asserted-by":"publisher","unstructured":"Mahdavifar S, Abdul\u00a0Kadir AF, Fatemi R, Alhadidi D, Ghorbani AA (2020) Dynamic android malware category classification using semi-supervised deep learning. In: 2020 IEEE the international conference on dependable, autonomic and secure computing, international conference on pervasive intelligence and computing, international conference on cloud and big data computing, international conference on cyber science and technology congress (DASC\/PiCom\/CBDCom\/CyberSciTech), pp 515\u2013522. https:\/\/doi.org\/10.1109\/DASC-PICom-CBDCom-CyberSciTech49142.2020.00094","DOI":"10.1109\/DASC-PICom-CBDCom-CyberSciTech49142.2020.00094"},{"key":"11604_CR38","doi-asserted-by":"publisher","unstructured":"Mercaldo F, Martinelli F, Santone A (2023) a fuzzy deep learning network for dynamic mobile malware detection. In: 2023 IEEE international conference on fuzzy systems (FUZZ), pp 1\u20137. https:\/\/doi.org\/10.1109\/FUZZ52849.2023.10309778","DOI":"10.1109\/FUZZ52849.2023.10309778"},{"key":"11604_CR39","doi-asserted-by":"publisher","unstructured":"Rashid J, Mahmood T, Nisar MW, Nazir T (2020) Phishing detection using machine learning technique. In: 2020 first international conference of smart systems and emerging technologies (SMARTTECH), pp 43\u201346. https:\/\/doi.org\/10.1109\/SMART-TECH49988.2020.00026","DOI":"10.1109\/SMART-TECH49988.2020.00026"},{"key":"11604_CR40","doi-asserted-by":"publisher","unstructured":"Salahdine F, El\u00a0Mrabet Z, Kaabouch N (2021) Phishing attacks detection a machine learning-based approach. In: 2021 IEEE 12th annual ubiquitous computing, electronics and mobile communication conference (UEMCON), pp 0250\u20130255. https:\/\/doi.org\/10.1109\/UEMCON53757.2021.9666627","DOI":"10.1109\/UEMCON53757.2021.9666627"},{"key":"11604_CR41","doi-asserted-by":"publisher","first-page":"36805","DOI":"10.1109\/ACCESS.2023.3252366","volume":"11","author":"A Karim","year":"2023","unstructured":"Karim A, Shahroz M, Mustofa K, Belhaouari SB, Joga SRK (2023) Phishing detection system through hybrid machine learning based on URL. IEEE Access 11:36805\u201336822. https:\/\/doi.org\/10.1109\/ACCESS.2023.3252366","journal-title":"IEEE Access"},{"key":"11604_CR42","doi-asserted-by":"publisher","unstructured":"Khorashadizadeh R, Jassbi SJ, Yari A (2022) Provide an improved model for detecting persian SMS spam by integrating deep learning and machine learning models. In: 2022 8th international conference on web research (ICWR), pp 137\u2013142. https:\/\/doi.org\/10.1109\/ICWR54782.2022.9786238","DOI":"10.1109\/ICWR54782.2022.9786238"},{"issue":"1","key":"11604_CR43","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1007\/s13278-023-01108-6","volume":"13","author":"M Sumathi","year":"2023","unstructured":"Sumathi M, Raja SP (2023) Machine learning algorithm-based spam detection in social networks. Soc Netw Anal Min 13(1):104. https:\/\/doi.org\/10.1007\/s13278-023-01108-6","journal-title":"Soc Netw Anal Min"},{"issue":"2","key":"11604_CR44","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1109\/MSMC.2023.3343950","volume":"10","author":"H Byeon","year":"2024","unstructured":"Byeon H, Jha S, Keshta I, Bhatt MW, Singh PP, Jindal L, Vijaya Lakshmi TR (2024) Spam text detection over social media usage: a supervised sampling approach for the social web of things. IEEE Syst Man Cybern Mag 10(2):32\u201339. https:\/\/doi.org\/10.1109\/MSMC.2023.3343950","journal-title":"IEEE Syst Man Cybern Mag"},{"key":"11604_CR45","doi-asserted-by":"publisher","unstructured":"Sjarif NNA, Chuprat S, Mahrin MN, Ahmad NA, Ariffin A, Senan FM, Zamani NA, Saupi A (2019) Endpoint detection and response: why use machine learning? In: 2019 international conference on information and communication technology convergence (ICTC), pp 283\u2013288. https:\/\/doi.org\/10.1109\/ICTC46691.2019.8939836","DOI":"10.1109\/ICTC46691.2019.8939836"},{"key":"11604_CR46","doi-asserted-by":"publisher","unstructured":"AlMasri T, Snober MA, Al-Haija QA (2022) IDPS-SDN-ML: an intrusion detection and prevention system using software-defined networks and machine learning. In: 2022 1st international conference on smart technology, applied informatics, and engineering (APICS), pp 133\u2013137. https:\/\/doi.org\/10.1109\/APICS56469.2022.9918804","DOI":"10.1109\/APICS56469.2022.9918804"},{"key":"11604_CR47","doi-asserted-by":"publisher","unstructured":"Iwabuchi M, Nakamura A (2024) A heuristics and machine learning hybrid approach to adaptive cyberattack detection. In: 2024 international conference on artificial intelligence, computer, data sciences and applications (ACDSA), pp 1\u20137. https:\/\/doi.org\/10.1109\/ACDSA59508.2024.10467929","DOI":"10.1109\/ACDSA59508.2024.10467929"},{"issue":"5","key":"11604_CR48","doi-asserted-by":"publisher","first-page":"8182","DOI":"10.1109\/JIOT.2019.2935189","volume":"6","author":"F Meneghello","year":"2019","unstructured":"Meneghello F, Calore M, Zucchetto D, Polese M, Zanella A (2019) IoT: internet of threats? A survey of practical security vulnerabilities in real IoT devices. IEEE Internet Things J 6(5):8182\u20138201. https:\/\/doi.org\/10.1109\/JIOT.2019.2935189","journal-title":"IEEE Internet Things J"},{"key":"11604_CR49","doi-asserted-by":"publisher","unstructured":"Susilo B, Sari RF (2021) Intrusion detection in software defined network using deep learning approach. In: 2021 IEEE 11th annual computing and communication workshop and conference (CCWC), pp 0807\u20130812. https:\/\/doi.org\/10.1109\/CCWC51732.2021.9375951","DOI":"10.1109\/CCWC51732.2021.9375951"},{"key":"11604_CR50","doi-asserted-by":"publisher","unstructured":"Bhardwaj K, Chen W, Marculescu R (2020) INVITED: new directions in distributed deep learning: bringing the network at forefront of IoT design. In: 2020 57th ACM\/IEEE design automation conference (DAC), pp 1\u20136. https:\/\/doi.org\/10.1109\/DAC18072.2020.9218628","DOI":"10.1109\/DAC18072.2020.9218628"},{"key":"11604_CR51","doi-asserted-by":"publisher","unstructured":"Alrefaei A, Ilyas M (2024) Ensemble deep learning model based on multi-class classification technique to detect cyber attacks in IoT environment. In: 2024 international conference on smart computing, IoT and machine learning (SIML), pp 174\u2013179. https:\/\/doi.org\/10.1109\/SIML61815.2024.10578143","DOI":"10.1109\/SIML61815.2024.10578143"},{"key":"11604_CR52","doi-asserted-by":"publisher","unstructured":"Alsarhan A, AlJamal M, Harfoushi O, Aljaidi M, Barhoush MM, Mansour N, Okour S, Abu\u00a0Ghazalah S, Al-Fraihat D (2024) Optimizing cyber threat detection in IoT: A study of artificial bee colony (abc)-based hyperparameter tuning for machine learning. Technologies 12(10) https:\/\/doi.org\/10.3390\/technologies12100181","DOI":"10.3390\/technologies12100181"},{"key":"11604_CR53","doi-asserted-by":"publisher","unstructured":"Lanitha B, Azath H, Beulah\u00a0David D, Chandra\u00a0Blessie E, Jayapradha A, Sheeba\u00a0Rani S (2021) BoT-IoT based denial of service detection with deep learning. In: 2021 fifth international conference on I-SMAC (IoT in social, mobile, analytics and cloud) (I-SMAC), pp 221\u2013225. https:\/\/doi.org\/10.1109\/I-SMAC52330.2021.9640789","DOI":"10.1109\/I-SMAC52330.2021.9640789"},{"key":"11604_CR54","doi-asserted-by":"publisher","unstructured":"Gandhi R, Li Y (2021) Comparing machine learning and deep learning for IoT Botnet detection. In: 2021 IEEE international conference on smart computing (SMARTCOMP), pp 234\u2013239. https:\/\/doi.org\/10.1109\/SMARTCOMP52413.2021.00053","DOI":"10.1109\/SMARTCOMP52413.2021.00053"},{"key":"11604_CR55","doi-asserted-by":"publisher","unstructured":"Jeelani F, Rai DS, Maithani A, Gupta S (2022) The detection of IoT botnet using machine learning on IoT-23 dataset. In: 2022 2nd international conference on innovative practices in technology and management (ICIPTM), vol 2, pp 634\u2013639. https:\/\/doi.org\/10.1109\/ICIPTM54933.2022.9754187","DOI":"10.1109\/ICIPTM54933.2022.9754187"},{"key":"11604_CR56","doi-asserted-by":"publisher","first-page":"20717","DOI":"10.1109\/ACCESS.2021.3054129","volume":"9","author":"AB Nassif","year":"2021","unstructured":"Nassif AB, Talib MA, Nasir Q, Albadani H, Dakalbab FM (2021) Machine learning for cloud security: a systematic review. IEEE Access 9:20717\u201320735. https:\/\/doi.org\/10.1109\/ACCESS.2021.3054129","journal-title":"IEEE Access"},{"issue":"6","key":"11604_CR57","doi-asserted-by":"publisher","first-page":"3956","DOI":"10.1109\/TNSE.2021.3110101","volume":"9","author":"H Zhang","year":"2022","unstructured":"Zhang H, Gao P, Yu J, Lin J, Xiong NN (2022) Machine learning on cloud with blockchain: a secure, verifiable and fair approach to outsource the linear regression. IEEE Trans Netw Sci Eng 9(6):3956\u20133967. https:\/\/doi.org\/10.1109\/TNSE.2021.3110101","journal-title":"IEEE Trans Netw Sci Eng"},{"key":"11604_CR58","doi-asserted-by":"publisher","unstructured":"Archana V, Manjunatha S (2025) Machine learning based XSS attack identification and prevention in cloud environments. In: 2025 international conference on knowledge engineering and communication systems (ICKECS), pp 1\u20137. https:\/\/doi.org\/10.1109\/ICKECS65700.2025.11035840","DOI":"10.1109\/ICKECS65700.2025.11035840"},{"key":"11604_CR59","doi-asserted-by":"publisher","unstructured":"Fidel G, Bitton R, Shabtai A (2020) When explainability meets adversarial learning: detecting adversarial examples using SHAP signatures. In: 2020 international joint conference on neural networks (IJCNN), pp 1\u20138. https:\/\/doi.org\/10.1109\/IJCNN48605.2020.9207637","DOI":"10.1109\/IJCNN48605.2020.9207637"},{"key":"11604_CR60","doi-asserted-by":"publisher","unstructured":"Scalas M, Giacinto G (2020) On the role of explainable machine learning for secure smart vehicles. In: 2020 AEIT international conference of electrical and electronic technologies for automotive (AEIT AUTOMOTIVE), pp 1\u20136. https:\/\/doi.org\/10.23919\/AEITAUTOMOTIVE50086.2020.9307431","DOI":"10.23919\/AEITAUTOMOTIVE50086.2020.9307431"},{"key":"11604_CR61","doi-asserted-by":"publisher","unstructured":"Yoshizawa T, Aghabagherloo A, Husz\u00e1k \u00c1, Ujv\u00e1rosi C, Singel\u00e9e D, Preneel B (2024) Security-focused training model of reinforcement learning in autonomous vehicles. In: 2024 IEEE vehicular networking conference (VNC), pp 215\u2013218. https:\/\/doi.org\/10.1109\/VNC61989.2024.10575985","DOI":"10.1109\/VNC61989.2024.10575985"},{"key":"11604_CR62","doi-asserted-by":"publisher","unstructured":"Mladenova T, Valova I (2022) Research on the ability to detect fake news in users of social networks. In: 2022 international congress on human\u2013computer interaction, optimization and robotic applications (HORA), pp 01\u201304. https:\/\/doi.org\/10.1109\/HORA55278.2022.9799905","DOI":"10.1109\/HORA55278.2022.9799905"},{"key":"11604_CR63","doi-asserted-by":"publisher","first-page":"71517","DOI":"10.1109\/ACCESS.2023.3294613","volume":"11","author":"M Park","year":"2023","unstructured":"Park M, Chai S (2023) Constructing a user-centered fake news detection model by using classification algorithms in machine learning techniques. IEEE Access 11:71517\u201371527. https:\/\/doi.org\/10.1109\/ACCESS.2023.3294613","journal-title":"IEEE Access"},{"key":"11604_CR64","doi-asserted-by":"publisher","unstructured":"Nirmala M, Navya R (2024) Machine learning techniques to detect a fake news in an articles. In: 2024 second international conference on networks, multimedia and information technology (NMITCON), pp 1\u20136. https:\/\/doi.org\/10.1109\/NMITCON62075.2024.10699063","DOI":"10.1109\/NMITCON62075.2024.10699063"},{"key":"11604_CR65","doi-asserted-by":"publisher","unstructured":"Ashish Sonia Arora M, Hemraj Rana A, Gupta G (2024) n analysis and identification of fake news using machine learning techniques. In: 2024 11th international conference on computing for sustainable global development (INDIACom), pp 634\u2013638. https:\/\/doi.org\/10.23919\/INDIACom61295.2024.10498879","DOI":"10.23919\/INDIACom61295.2024.10498879"},{"key":"11604_CR66","doi-asserted-by":"publisher","unstructured":"Saranya SS, Kanimozhi N, Kavitha MN, Atchayaprakassh KS, Bharani\u00a0Kumar S, Ragul KK (2022) Authentic news prediction in machine learning using passive aggressive algorithm. In: 2022 second international conference on artificial intelligence and smart energy (ICAIS), pp 372\u2013376. https:\/\/doi.org\/10.1109\/ICAIS53314.2022.9743010","DOI":"10.1109\/ICAIS53314.2022.9743010"},{"key":"11604_CR67","doi-asserted-by":"publisher","unstructured":"Shan S, Ding W, Passananti J, Wu S, Zheng H, Zhao BY (2024) Nightshade: prompt-specific poisoning attacks on text-to-image generative models. In: 2024 IEEE symposium on security and privacy (SP), pp 807\u2013825. https:\/\/doi.org\/10.1109\/SP54263.2024.00207","DOI":"10.1109\/SP54263.2024.00207"},{"issue":"2","key":"11604_CR68","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TDSC.2022.3222972","volume":"21","author":"G Ren","year":"2024","unstructured":"Ren G, Wu J, Li G, Li S, Guizani M (2024) Protecting intellectual property with reliable availability of learning models in AI-based cybersecurity services. IEEE Trans Dependable Secure Comput 21(2):600\u2013617. https:\/\/doi.org\/10.1109\/TDSC.2022.3222972","journal-title":"IEEE Trans Dependable Secure Comput"},{"key":"11604_CR69","unstructured":"Damiani J (2019) A voice Deepfake was used to scam A CEO Out Of \\$243,000. https:\/\/www.forbes.com\/sites\/jessedamiani\/2019\/09\/03\/a-voice-deepfake-was-used-to-scam-a-ceo-out-of-243000. Available 9 Dec 2024"},{"key":"11604_CR70","unstructured":"Google: Frequently Asked Questions. https:\/\/research.google.com\/colaboratory\/ faq.html. Available 9 Dec 2024 (2018)"},{"key":"11604_CR71","doi-asserted-by":"publisher","unstructured":"Agarwal H, Singh A, Rajeswari D (2021) Deepfake detection using SVM. In: 2021 second international conference on electronics and sustainable communication systems (ICESC), pp 1245\u20131249. https:\/\/doi.org\/10.1109\/ICESC51422.2021.9532627","DOI":"10.1109\/ICESC51422.2021.9532627"},{"key":"11604_CR72","doi-asserted-by":"publisher","unstructured":"Rafique R, Nawaz M, Kibriya H, Masood M (2021) DeepFake detection using error level analysis and deep learning. In: 2021 4th international conference on computing and information sciences (ICCIS), pp 1\u20134. https:\/\/doi.org\/10.1109\/ICCIS54243.2021.9676375","DOI":"10.1109\/ICCIS54243.2021.9676375"},{"key":"11604_CR73","doi-asserted-by":"publisher","first-page":"69031","DOI":"10.1109\/ACCESS.2022.3185121","volume":"10","author":"J Kang","year":"2022","unstructured":"Kang J, Ji S-K, Lee S, Jang D, Hou J-U (2022) Detection enhancement for various Deepfake types based on residual noise and manipulation traces. IEEE Access 10:69031\u201369040. https:\/\/doi.org\/10.1109\/ACCESS.2022.3185121","journal-title":"IEEE Access"},{"key":"11604_CR74","doi-asserted-by":"publisher","unstructured":"Sun C, Jia S, Hou S, Lyu S (2023) AI-synthesized voice detection using neural vocoder artifacts. In: 2023 IEEE\/CVF conference on computer vision and pattern recognition workshops (CVPRW), pp 904\u2013912. https:\/\/doi.org\/10.1109\/CVPRW59228.2023.00097","DOI":"10.1109\/CVPRW59228.2023.00097"},{"key":"11604_CR75","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.procs.2023.01.283","volume":"219","author":"M Mcuba","year":"2023","unstructured":"Mcuba M, Singh A, Ikuesan RA, Venter H (2023) The effect of deep learning methods on deepfake audio detection for digital investigation. Procedia Comput Sci 219:211\u2013219","journal-title":"Procedia Comput Sci"},{"key":"11604_CR76","doi-asserted-by":"publisher","unstructured":"Khan A, Malik KM (2023) Securing voice biometrics: one-shot learning approach for audio Deepfake detection. In: 2023 IEEE international workshop on information forensics and security (WIFS), pp 1\u20136. https:\/\/doi.org\/10.1109\/WIFS58808.2023.10374968","DOI":"10.1109\/WIFS58808.2023.10374968"},{"key":"11604_CR77","doi-asserted-by":"publisher","unstructured":"Confido A, Ntagiou EV, Wallum M (2022) Reinforcing penetration testing using AI. In: 2022 IEEE aerospace conference (AERO), pp 1\u201315. https:\/\/doi.org\/10.1109\/AERO53065.2022.9843459","DOI":"10.1109\/AERO53065.2022.9843459"},{"key":"11604_CR78","unstructured":"The European Space Agency (ESA): streamlining security testing and security risk management as part of a secure system engineering framework at ESA. https:\/\/gsaw.org\/wp-content\/uploads\/2019\/03\/2019s04wallum.pdf. Available 9 Dec 2024 (2020)"},{"key":"11604_CR79","doi-asserted-by":"publisher","first-page":"134052","DOI":"10.1109\/ACCESS.2021.3116468","volume":"9","author":"TR Lee","year":"2021","unstructured":"Lee TR, Teh JS, Jamil N, Yan JLS, Chen J (2021) Lightweight block cipher security evaluation based on machine learning classifiers and active S-boxes. IEEE Access 9:134052\u2013134064. https:\/\/doi.org\/10.1109\/ACCESS.2021.3116468","journal-title":"IEEE Access"},{"key":"11604_CR80","doi-asserted-by":"publisher","unstructured":"Jeong O, Martin AD, Ahamadzadeh E, Moon I (2024) Robust cryptosystem identification under various operation modes using deep recurrent neural networks. In: 2024 IEEE 4th international conference on electronic communications, internet of things and big data (ICEIB), pp 619\u2013623. https:\/\/doi.org\/10.1109\/ICEIB61477.2024.10602570","DOI":"10.1109\/ICEIB61477.2024.10602570"},{"key":"11604_CR81","doi-asserted-by":"publisher","unstructured":"Tolba Z, Derdour M, Dehimi NEH (2022) Machine learning based cryptanalysis techniques: perspectives, challenges and future directions. In: 2022 4th international conference on pattern analysis and intelligent systems (PAIS), pp 1\u20137. https:\/\/doi.org\/10.1109\/PAIS56586.2022.9946889","DOI":"10.1109\/PAIS56586.2022.9946889"},{"key":"11604_CR82","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/5452870","author":"MA Ferrag","year":"2019","unstructured":"Ferrag MA, Maglaras L, Derhab A (2019) Authentication and authorization for mobile IoT devices using biofeatures: recent advances and future trends. Secur Commun Netw. https:\/\/doi.org\/10.1155\/2019\/5452870","journal-title":"Secur Commun Netw"},{"key":"11604_CR83","doi-asserted-by":"publisher","unstructured":"Ghoualmi L, Benkechkache MEA (2022) Feature selection based on machine learning algorithms: a weighted score feature importance approach for facial authentication. In: 2022 3rd international informatics and software engineering conference (IISEC), pp 1\u20135. https:\/\/doi.org\/10.1109\/IISEC56263.2022.9998240","DOI":"10.1109\/IISEC56263.2022.9998240"},{"key":"11604_CR84","doi-asserted-by":"publisher","first-page":"13277","DOI":"10.1109\/ACCESS.2024.3356351","volume":"12","author":"AAS AlQahtani","year":"2024","unstructured":"AlQahtani AAS, Alshayeb T, Nabil M, Patooghy A (2024) Leveraging machine learning for Wi\u2013Fi-based environmental continuous two-factor authentication. IEEE Access 12:13277\u201313289. https:\/\/doi.org\/10.1109\/ACCESS.2024.3356351","journal-title":"IEEE Access"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11604-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11604-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11604-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T16:28:08Z","timestamp":1762014488000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11604-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,8]]},"references-count":84,"journal-issue":{"issue":"33","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["11604"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11604-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,8]]},"assertion":[{"value":"8 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no financial or non-financial conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}