MENTOR: The Design and Evaluation of a Protection Services Recommender System

Franco, Muriel Figueredo; Rodrigues, Bruno; Stiller, Burkhard

doi:10.23919/cnsm46954.2019.9012686

Cited by 21 publications

(9 citation statements)

References 14 publications

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“…In their paper, Franco et al [ 92 ] (as well as [ 40 ]) have presented the MENTOR system, which they describe as a “support tool for cybersecurity”. It was designed to be able to recommend the most suitable protection measures (such as specific anti-malware tools, firewalls, etc.…”

Section: The Results Of the Study—the State Of The Art Of Recommender Systems For Cybersecuritymentioning

confidence: 99%

A Systematic Review of Recommender Systems and Their Applications in Cybersecurity

Pawlicka

Pawlicki

Kozik

et al. 2021

Sensors

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This paper discusses the valuable role recommender systems may play in cybersecurity. First, a comprehensive presentation of recommender system types is presented, as well as their advantages and disadvantages, possible applications and security concerns. Then, the paper collects and presents the state of the art concerning the use of recommender systems in cybersecurity; both the existing solutions and future ideas are presented. The contribution of this paper is two-fold: to date, to the best of our knowledge, there has been no work collecting the applications of recommenders for cybersecurity. Moreover, this paper attempts to complete a comprehensive survey of recommender types, after noticing that other works usually mention two–three types at once and neglect the others.

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Section: The Results Of the Study—the State Of The Art Of Recommender Systems For Cybersecuritymentioning

confidence: 99%

A Systematic Review of Recommender Systems and Their Applications in Cybersecurity

Pawlicka

Pawlicki

Kozik

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…In such a model it will be possible for members to create strategic regional alliances and use a portion of their infrastructure to perform mitigation services. Therefore, in combination with recommender services [57] as selection of a suitable protection provider based on specified requirements can optimize costs.…”

Section: Discussion Of Achievementsmentioning

confidence: 99%

Blockchain Signaling System (BloSS): Cooperative Signaling of Distributed Denial-of-Service Attacks

et al. 2020

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Distributed Denial-of-Service (DDoS) attacks are one of the major causes of concerns for communication service providers. When an attack is highly sophisticated and no countermeasures are available directly, sharing hardware and defense capabilities become a compelling alternative. Future network and service management can base its operations on equally distributed systems to neutralize highly distributed DDoS attacks. A cooperative defense allows for the combination of detection and mitigation capabilities, the reduction of overhead at a single point, and the blockage of malicious traffic near its source. Main challenges impairing the widespread deployment of existing cooperative defense are: (a) high complexity of operation and coordination, (b) need for trusted and secure communications, (c) lack of incentives for service providers to cooperate, and (d) determination on how operations of these systems are affected by different legislation, regions, and countries. The cooperative Blockchain Signaling System (BloSS) defines an effective and alternative solution for security management, especially cooperative defenses, by exploiting Blockchains (BC) and Software-Defined Networks (SDN) for sharing attack information, an exchange of incentives, and tracking of reputation in a fully distributed and automated fashion. Therefore, BloSS was prototyped and evaluated through a global experiment, without the burden to maintain, design, and develop special registries and gossip protocols.

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“…The Data Layer and the User Layer communicate through the Communication API. An Integration API allows for external solutions to request information and reuse available miners, which provides integration options, such as for systems to recommend or offer protections against cyberattacks [9], [10].…”

Section: The Secgrid Platformmentioning

confidence: 99%

SecGrid: a Visual System for the Analysis and ML-based Classification of Cyberattack Traffic

Franco¹,

Assen²,

Boillat³

et al. 2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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Due to the increasing number of cyberattacks and respective predictions for the upcoming years with even larger numbers of occurrences, companies are becoming aware not only that the digitization of their businesses is essential, but also that the adoption of efficient cybersecurity strategies is crucial. Therefore, approaches for a better understanding and analysis of cybersecurity are essential.Thus, SecGrid, a Machine Learning (ML) empowered platform for analyzing, classification, and visualization of cyberattacks is introduced. SecGrid implements an extensible set of miners to analyze information from network traces to provide insightful visualizations of malicious traffic given and to classify automatically different types of cyberattacks by using supervised ML. Experiments conducted show high overall usability, scalability in terms of the capacity of the platform to extract information from large files, and high performance and accuracy during the classification of cyberattacks.

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MENTOR: The Design and Evaluation of a Protection Services Recommender System

Cited by 21 publications

References 14 publications

A Systematic Review of Recommender Systems and Their Applications in Cybersecurity

A Systematic Review of Recommender Systems and Their Applications in Cybersecurity

Blockchain Signaling System (BloSS): Cooperative Signaling of Distributed Denial-of-Service Attacks

SecGrid: a Visual System for the Analysis and ML-based Classification of Cyberattack Traffic

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