On the Recommendation of Protection Services

Cyberattacks are the cause of several damages on governments and companies in the last years. Such damage includes not only leaks of sensitive information, but also economic loss due to downtime of services. The security market size worth billions of dollars, which represents investments to acquire protection services and training response teams to operate such services, determines a considerable part of the investment in technologies around the world. Although a vast number of protection services are available, it is neither trivial for network operators nor endusers to choose one of them in order to prevent or mitigate an imminent attack. As the next-generation cybersecurity solutions are on the horizon, systems that simplify their adoption are still required in support of security management tasks. Thus, this paper introduces MENTOR, a support tool for cybersecurity, focusing on the recommendation of protection services. MENTOR is able to (a) to deal with different demands from the user and (b) to recommend the adequate protection service in order to provide a proper level of cybersecurity in different scenarios. Four similarity measurements are implemented in order to prove the feasibility of the MENTOR’s engine. An evaluation determines the performance and accuracy of each measurement used during the recommendation process.

[1]  David Hausheer,et al.  A Blockchain-Based Architecture for Collaborative DDoS Mitigation with Smart Contracts , 2017, AIMS.

[2]  Hui Xiong,et al.  Mobile app recommendations with security and privacy awareness , 2014, KDD.

[3]  V. G. Bhujade,et al.  A real time technique for targeted advertising using location-based services — For GPS enabled device: A review , 2017, 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA).

[4]  Kunal Shah,et al.  Recommender systems: An overview of different approaches to recommendations , 2017, 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS).

[5]  Liran Ma,et al.  A Context-Aware Budget-Constrained Targeted Advertising System for Vehicular Networks , 2018, IEEE Access.

[6]  Frederick T. Sheldon,et al.  CSSR: Cloud Services Security Recommender , 2016, 2016 IEEE World Congress on Services (SERVICES).

[7]  Chunhua Wang,et al.  Machine Learning and Deep Learning Methods for Cybersecurity , 2018, IEEE Access.

[8]  Georgios Kambourakis,et al.  DDoS in the IoT: Mirai and Other Botnets , 2017, Computer.

[9]  Elias Bou-Harb,et al.  Survey of Attack Projection, Prediction, and Forecasting in Cyber Security , 2019, IEEE Communications Surveys & Tutorials.

[10]  Lisandro Zambenedetti Granville,et al.  FENDE: Marketplace-Based Distribution, Execution, and Life Cycle Management of VNFs , 2019, IEEE Communications Magazine.

[11]  Tranos Zuva,et al.  A survey of recommender system feedback techniques, comparison and evaluation metrics , 2015, 2015 International Conference on Computing, Communication and Security (ICCCS).

[12]  Yi Zhou,et al.  Understanding the Mirai Botnet , 2017, USENIX Security Symposium.

[13]  Carol J. Fung,et al.  Demo: DroidNet - An Android Permission Control Recommendation System Based on Crowdsourcing , 2019, 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM).

[14]  Georgios Xilouris,et al.  SHIELD: A novel NFV-based cybersecurity framework , 2017, 2017 IEEE Conference on Network Softwarization (NetSoft).

[15]  Haralambos Mouratidis,et al.  Recommender Systems Meeting Security: From Product Recommendation to Cyber-Attack Prediction , 2017, EANN.

[16]  Gregorio Convertino,et al.  What data should I protect?: recommender and planning support for data security analysts , 2019, IUI.

[17]  Yonggang Wen,et al.  Towards Virus Scanning as a Service in Mobile Cloud Computing: Energy-Efficient Dispatching Policy Under ${N}$ -Version Protection , 2018, IEEE Transactions on Emerging Topics in Computing.

[18]  Ari Visa,et al.  Comprehensive survey of similarity measures for ranked based location fingerprinting algorithm , 2017, 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[19]  Kim-Kwang Raymond Choo,et al.  Privacy-aware smart city: A case study in collaborative filtering recommender systems , 2018, J. Parallel Distributed Comput..

[20]  J. Bobadilla,et al.  Recommender systems survey , 2013, Knowl. Based Syst..