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Title: Detecting Ransomware Attacks Via Intelligent Algorithms: Recent Development and Next Direction from Deep Learning and Big Data Perspectives
Authors: Ibrahim, Bello
Chiroma, Haruna
Abdullahi, Usman A.
Yau, Abdulsalam Gital
Jauro, Fatsuma
Khan, Abdullah
Okesola, Julius O.
Abdulhamid, Shafi’i Muhammad
Keywords: Big data analytics
Decision tree
Deep learning
Machine learning algorithms
Random forest
Issue Date: 12-May-2020
Publisher: Journal of Ambient Intelligence and Humanized Computing
Abstract: Recently, cybercriminals have infiltrated different sectors of the human venture to launch ransomware attacks against information technology infrastructure. They demand ransom from individuals and industries, thereby inflicting significant loss of data. The use of intelligent algorithms for ransomware attack detection began to gain popularity in recent times and proved feasible. However, no comprehensive dedicated literature review on the applications of intelligent machine learning algorithms to detect ransomware attacks on information technology infrastructure. Unlike the previous reviews on ransomware attacks, this paper aims to conduct a comprehensive survey on the detection of ransomware attacks using intelligent machine learning algorithms. The study analysed literature from different perspectives focusing on intelligent algorithms detection of ransomware. The survey shows that there is a growing interest in recent times (2016—date) on the application of intelligent algorithms for ransomware detection. Deep learning algorithms are gaining tremendous attention because of their ability to handle large scale datasets, prominence in the research community, and ability to solve problems better than the conventional intelligent algorithms. To date, the potentials of big data analytics are yet to be fully exploited for the smart detection of ransomware attacks. Future research opportunities from the perspective of deep learning and big data analytics to solve the challenges identified from the survey are outlined to give the research community a new direction in dealing with ransomware attacks.
ISSN: 1868-5137
Appears in Collections:Cyber Security Science

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