2020
DOI: 10.1109/access.2020.2968045
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Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity

Abstract: Cybersecurity is a fast-evolving discipline that is always in the news over the last decade, as the number of threats rises and cybercriminals constantly endeavor to stay a step ahead of law enforcement. Over the years, although the original motives for carrying out cyberattacks largely remain unchanged, cybercriminals have become increasingly sophisticated with their techniques. Traditional cybersecurity solutions are becoming inadequate at detecting and mitigating emerging cyberattacks. Advances in cryptogra… Show more

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Cited by 188 publications
(103 citation statements)
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References 140 publications
(169 reference statements)
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“…Due to the availability of huge amount of labeled data, and ability to work in a decentralized fashion, these techniques can be utilized for users' privacy preservation with enhanced usefulness. The heterogeneous federated transfer learning (HFTL) framework [298], privacy-preserving deep learning (PPDL) technique [299], deep transfer learning (DTL) method [300], adaptive privacy preserving federated learning (APPFL) method [301], block-chain-enable privacy preserving (BCEPP) architectures [302], [303], secure collaborative few-shot learning (SCFSL) framework [304], searchable encryption (SE) methods leveraging ciphertext-policy attribute-based encryption (CP-ABE) [305], [306], data resource protection solution leveraging smart contracts [307], improving cyber security solutions utilizing AI's potential [308], and computational intelligence based methods for information security [309], to name a few have already been used in practical applications related to the PPDP. Hence, devising robust and lightweight techniques which involve less parameters and can co-work with the traditional anonymization approaches to scale up privacy preservation with enhanced data utility is a promising area of research for the future.…”
Section: Promising Open Research Directionsmentioning
confidence: 99%
“…Due to the availability of huge amount of labeled data, and ability to work in a decentralized fashion, these techniques can be utilized for users' privacy preservation with enhanced usefulness. The heterogeneous federated transfer learning (HFTL) framework [298], privacy-preserving deep learning (PPDL) technique [299], deep transfer learning (DTL) method [300], adaptive privacy preserving federated learning (APPFL) method [301], block-chain-enable privacy preserving (BCEPP) architectures [302], [303], secure collaborative few-shot learning (SCFSL) framework [304], searchable encryption (SE) methods leveraging ciphertext-policy attribute-based encryption (CP-ABE) [305], [306], data resource protection solution leveraging smart contracts [307], improving cyber security solutions utilizing AI's potential [308], and computational intelligence based methods for information security [309], to name a few have already been used in practical applications related to the PPDP. Hence, devising robust and lightweight techniques which involve less parameters and can co-work with the traditional anonymization approaches to scale up privacy preservation with enhanced data utility is a promising area of research for the future.…”
Section: Promising Open Research Directionsmentioning
confidence: 99%
“…Internet [93,94,100,107,110,[114][115][116][117]120] Lastly, regarding agricultural cyber-security, researchers intensify their efforts in designing Machine Learning (ML) and Deep Learning (DL) methods for anomaly behavior detection and network analysis of intrusion detection systems (IDS) [124]. Traditional Machine and Deep Learning applications applied for cybersecurity in agriculture include [125,126]: The emergence of such methods has enabled efficient handling and analysis of large amounts of data (e.g., files extracted from server logs, communication equipment, security solutions and blogs related to information security, in different types of structured and unstructured formats), so as to support a variety of conceptual models, security activities and knowledge generation mechanisms. This leads to efficient decision making or automation of security responses without the need for human interception [127].…”
Section: Privacy Confidentially Availabilitymentioning
confidence: 99%
“…Lastly, regarding agricultural cyber-security, researchers intensify their efforts in designing Machine Learning (ML) and Deep Learning (DL) methods for anomaly behavior detection and network analysis of intrusion detection systems (IDS) [ 124 ]. Traditional Machine and Deep Learning applications applied for cybersecurity in agriculture include [ 125 , 126 ]: Support Vector Machine (SVM); K-Nearest Neighbor; Decision Trees; Deep Belief Network (DBN); Recurrent Neural Networks (RNN); Convolutional Neural Networks (CNN); Artificial Neural Networks (ANN); Self-Organizing Maps (SOMs); Natural Language Processing (NLP); Biologically inspired techniques, such as Deep Neural Networks (DNN) and/or Generative Adversarial Networks (GANs). …”
Section: Mitigation Measures and Strategies For Security Threats Imentioning
confidence: 99%
“…Technological advancements occurring in the field of cybersecurity emphasize on the application of Artificial Intelligence (AI) techniques to improve the security landscape [1]. Over the years, both adversaries as well as the research community have been relying on AI approaches to offend and defend computer networks.…”
Section: Introductionmentioning
confidence: 99%