Detecting ransomware attacks using intelligent algorithms: recent development and next direction from deep learning and
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ORIGINAL RESEARCH
Detecting ransomware attacks using intelligent algorithms: recent development and next direction from deep learning and big data perspectives Ibrahim Bello1 · Haruna Chiroma2 · Usman A. Abdullahi2 · Abdulsalam Ya’u Gital1 · Fatsuma Jauro3 · Abdullah Khan4,5 · Julius O. Okesola6 · Shafi’i M. Abdulhamid7 Received: 20 March 2020 / Accepted: 24 October 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Recently, cybercriminals have infiltrated different sectors of the human venture to launch ransomware attacks against infor‑ mation 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 ransom‑ ware 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. Keywords Big data analytics · Decision tree · Deep learning · Machine learning algorithms · Random forest · Ransomware
* Haruna Chiroma [email protected]; [email protected]
1
Department of Mathematical Sciences, Abubakar Tafawa Balewa University, Bauchi, Nigeria
Ibrahim Bello [email protected]
2
Future Technlogy Research Center, National Yunlin University of Science and Technology, Yunlin, Taiwan
Usman A. Abdullahi [email protected]
3
Department of Computer Science, Ahmadu Bello University, Zaria, Nigeria
Abdulsalam Ya’u Gital [email protected]
4
Fatsuma Jauro [email protected]
Faculty of Computing and Information Technology, Information System Department, King AbdulAziz University, Jeddah, Saudi Arabia
5
Abdullah Khan [email protected]
Institute of Computer Sciences and Information Technology, University of Agriculture Peshawar, Peshawar, Pakistan
6
Julius O. Okesola olatunjiokesola@tech‑U.edu.ng
Department
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