Survey of AI in Cybersecurity for Information Technology Management

Cybersecurity has become an emerging challenge for business information management in recent years. Artificial Intelligence (AI) is widely used in different field, but it is still relatively new in cybersecurity. However, the applications in cybersecurity is crucial for everyone’s daily life. In this paper, we introduce the current state of AI in cybersecurity field, and then describe several case studies and applications of AI to help the community including engineering managers and leaders, researchers, educators, innovators, entrepreneurs, and students to better understand this field, such as the challenges and unresolved issues of AI in cybersecurity. Managerial implications and policy recommendations are provided for business and government.

[1]  Xinyi Liu,et al.  Deep-Resp-Forest: A deep forest model to predict anti-cancer drug response. , 2019, Methods.

[2]  Hao Lv,et al.  Identify origin of replication in Saccharomyces cerevisiae using two-step feature selection technique , 2018, Bioinform..

[3]  Miao Sun,et al.  QAcon: single model quality assessment using protein structural and contact information with machine learning techniques , 2016, Bioinform..

[4]  Erhan Guven,et al.  A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection , 2016, IEEE Communications Surveys & Tutorials.

[5]  Jilong Li,et al.  Large-scale model quality assessment for improving protein tertiary structure prediction , 2015, Bioinform..

[6]  Balachandran Manavalan,et al.  Machine-Learning-Based Prediction of Cell-Penetrating Peptides and Their Uptake Efficiency with Improved Accuracy. , 2018, Journal of proteome research.

[7]  Dong Si,et al.  Artificial Intelligence for Bioinformatics: Applications in Protein Folding Prediction , 2019, 2019 IEEE Technology & Engineering Management Conference (TEMSCON).

[8]  Quan Zou.  Latest Machine Learning Techniques for Biomedicine and Bioinformatics , 2019 .

[9]  T. Daim,et al.  A Research and Development Decision Model for Pharmaceutical Industry: Case of China , 2018 .

[10]  Balachandran Manavalan,et al.  mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides , 2019, International journal of molecular sciences.

[11]  Hüseyin Çakir,et al.  Applications of Artificial Intelligence Techniques to Combating Cyber Crimes: A Review , 2015, ArXiv.

[12]  Tugrul U. Daim,et al.  Exploring the impact of technology foresight studies on innovation: Case of BRIC countries , 2012 .

[13]  Dong Si,et al.  Deep Learning to Predict Protein Backbone Structure from High-Resolution Cryo-EM Density Maps , 2019, bioRxiv.

[14]  Tugrul U. Daim,et al.  Assessment and adoption of web-based health information systems , 2009 .

[15]  Jian Huang,et al.  A Brief Survey of Machine Learning Methods in Protein Sub-Golgi Localization , 2019, Current Bioinformatics.

[16]  Wei Chen,et al.  Predicting protein structural classes for low-similarity sequences by evaluating different features , 2019, Knowl. Based Syst..

[17]  Zhangxin Chen,et al.  ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network , 2017, Molecules.

[18]  T. Daim,et al.  Technology transfer in China: literature review and policy implications , 2011 .

[19]  Renzhi Cao,et al.  Deep convolutional neural networks for predicting the quality of single protein structural models , 2019, bioRxiv.

[20]  Haruna Chiroma,et al.  A Review of the Advances in Cyber Security Benchmark Datasets for Evaluating Data-Driven Based Intrusion Detection Systems , 2015, SCSE.

[21]  Al Naqvi,et al.  Business Strategy in the Artificial Intelligence Economy , 2018 .

[22]  Hongtao Zhang,et al.  Artificial Intelligence on Job-Hopping Forecasting: AI on Job-Hopping , 2018, 2018 Portland International Conference on Management of Engineering and Technology (PICMET).

[23]  Alberto Perez Veiga.  Applications of Artificial Intelligence to Network Security , 2018, ArXiv.

[24]  T. Daim,et al.  Sectoral innovation system and technology policy development in China , 2012 .

[25]  Gang Wang,et al.  LEMNA: Explaining Deep Learning based Security Applications , 2018, CCS.

[26]  Renzhi Cao,et al.  Survey of Machine Learning Techniques in Drug Discovery. , 2019, Current drug metabolism.

[27]  Yan Lin,et al.  iTerm-PseKNC: a sequence-based tool for predicting bacterial transcriptional terminators , 2018, Bioinform..

[28]  Carlos García Garino,et al.  Automatic network intrusion detection: Current techniques and open issues , 2012, Comput. Electr. Eng..

[29]  Jianlin Cheng.  Protein Tertiary Structure Modeling Driven by Deep Learning and Contact Distance Prediction in CASP13 , 2019, BCB.

[30]  Naima Kaabouch,et al.  Cyber-security in smart grid: Survey and challenges , 2018, Comput. Electr. Eng..

[31]  Joseph M. Hatfield.  Social engineering in cybersecurity: The evolution of a concept , 2018, Comput. Secur..

[32]  T. Daim,et al.  Multi‐perspective analysis of the Chinese pharmaceutical sector: Policy, enterprise and technology , 2011 .