DEEP LEARNING MODEL FOR PREDICTING MULTISTAGE CYBERATTACKS

Simple item page
dc.contributor.authorIbor, Ayei E.
dc.contributor.authorOladeji, Florence A.
dc.contributor.authorOkunoye, Olusoji B.
dc.contributor.authorUwadia, Charles O.
dc.date.accessioned2019-09-27T07:30:18Z
dc.date.available2019-09-27T07:30:18Z
dc.date.issued2019-06
dc.description.abstractThe prediction of cyberattacks has been a major concern in cybersecurity. This is due to the huge financial and resource losses incurred by organisations after a cyberattack. The emergence of new applications and disruptive technologies has come with new vulnerabilities, most of which are novel – with no immediate remediation available. Recent attacks signatures are becoming evasive, deploying very complex techniques and algorithms to infiltrate a network, leading to unauthorized access and modification of system parameters and classified data. Although there exists several approaches to mitigating attacks, challenges of using known attack signatures and modeled behavioural profiles of network environments still linger. Consequently, this paper discusses the use of unsupervised statistical and supervised deep learning techniques to predict attacks by mapping hyper-alerts to class labels of attacks. This enhances the processes of feature extraction and transformation, as a means of giving structured interpretation of the dynamic profiles of a network.en_US
dc.identifier.citationAyei E. Ibor, Florence A. Oladeji, Olusoji B. Okunoye, Charles O. Uwadia. (2019). DEEP LEARNING MODEL FOR PREDICTING MULTISTAGE CYBERATTACKS. THE JOURNAL OF COMPUTER SCIENCE AND ITS APPLICATIONS. 26(1): 50-64en_US
dc.identifier.urihttps://ir.unilag.edu.ng/handle/123456789/6177
dc.language.isoenen_US
dc.publisherTHE JOURNAL OF COMPUTER SCIENCE AND ITS APPLICATIONSen_US
dc.relation.ispartofseries26;1
dc.subjectAlert correlationen_US
dc.subjectCyberattack predictionen_US
dc.subjectCybersecurityen_US
dc.subjectDeep learningen_US
dc.subjectCyberattacksen_US
dc.subjectSupervised and Unsupervised Learningen_US
dc.titleDEEP LEARNING MODEL FOR PREDICTING MULTISTAGE CYBERATTACKSen_US
dc.typeArticleen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Journal 20- Ibor-Oladeji 2019 NCS galley proof 6 (1).pdf
Size:
481.75 KB
Format:
Adobe Portable Document Format
Description:
Main article
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Computer Science -Scholarly Publications