Since the birth of artificial intelligence, theory and technology are increasingly mature, and the application field is also expanding. Some artificial intelligence technologies, such as deep learning, can help develop resilient machine learning to mitigate adversarial learning attacks. This paper uses a money lending case to explain the application of artificial intelligence in building resilient machine learning, and we use Generative Adversarial Network as an example. Moreover, we discuss the definition of resilient machine learning and give a review of adversarial machine learning attacks and threat actors.
{"title":"Artificial Intelligence Technologies in Building Resilient Machine Learning","authors":"Guangye Dai, S. Sthapit, G. Epiphaniou, C. Maple","doi":"10.1049/icp.2021.2405","DOIUrl":"https://doi.org/10.1049/icp.2021.2405","url":null,"abstract":"Since the birth of artificial intelligence, theory and technology are increasingly mature, and the application field is also expanding. Some artificial intelligence technologies, such as deep learning, can help develop resilient machine learning to mitigate adversarial learning attacks. This paper uses a money lending case to explain the application of artificial intelligence in building resilient machine learning, and we use Generative Adversarial Network as an example. Moreover, we discuss the definition of resilient machine learning and give a review of adversarial machine learning attacks and threat actors.","PeriodicalId":254750,"journal":{"name":"Competitive Advantage in the Digital Economy (CADE 2021)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123789217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Claudia Cristina, Evandro Pioli Moro, C. Maple, G. Epiphaniou
Recent advances in information and communications technologies, providing increased bandwidth in mobile environments, unprecedented computational power and miniaturisation of electronic devices, are helping realise a hyper-connected world. Specifically, developments in artificial intelligence and the Internet of Things are affording opportunities for enhanced customer service and competitive advantage for businesses and governments alike. However, given the significant value of these emerging services, and the data that underpins them, there is an increasingly urgent need to ensure effective protection. In this paper we present a novel architecture, built upon enhanced gateways, distributed ledger technology and machine learning, to identify, understand, and respond to emerging attacks on this critical infrastructure, thereby maintaining competitive advantage for businesses and realising the benefits of a digital economy for governments. ,
{"title":"I-TRACE: PROTECTING CYBER-PHYSICAL INFRASTRUCTURE THROUGH ENHANCED GATEWAYS, DLT AND MACHINE LEARNING","authors":"Claudia Cristina, Evandro Pioli Moro, C. Maple, G. Epiphaniou","doi":"10.1049/icp.2021.2430","DOIUrl":"https://doi.org/10.1049/icp.2021.2430","url":null,"abstract":"Recent advances in information and communications technologies, providing increased bandwidth in mobile environments, unprecedented computational power and miniaturisation of electronic devices, are helping realise a hyper-connected world. Specifically, developments in artificial intelligence and the Internet of Things are affording opportunities for enhanced customer service and competitive advantage for businesses and governments alike. However, given the significant value of these emerging services, and the data that underpins them, there is an increasingly urgent need to ensure effective protection. In this paper we present a novel architecture, built upon enhanced gateways, distributed ledger technology and machine learning, to identify, understand, and respond to emerging attacks on this critical infrastructure, thereby maintaining competitive advantage for businesses and realising the benefits of a digital economy for governments. ,","PeriodicalId":254750,"journal":{"name":"Competitive Advantage in the Digital Economy (CADE 2021)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126831212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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