Using Multivariate Heuristic Analysis for Detecting Attacks in Website Log Files: A Formulaic Approach

Peter Smith*, John Robson, Nick Dalton

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


As cyberattacks on websites evolve and become more sophisticated, there is a pressing need for detection methodologies that can adapt to this ever-changing landscape. This pilot study evaluates current methodologies in order to identify gaps in current literature and assesses their ability to be deployed in a real-world scenario. In order to do this, we propose a shift towards a multivariate framework, which measures the influence of several key factors. It was hypothesised that historic data is useful in predicting attacks. The study was given access to real website data in order to verify the efficacy of a multivariate approach on finding a variety of attacks. Results indicated a significant improvement in accuracy, specificity and sensitivity in attack detection in comparison to previous methods. This empirical evidence highlights the importance of using real-world data in cyber security and takes an essential preliminary step to be expanded by future research.

Original languageEnglish
Title of host publicationProceedings of the Second International Conference on Advances in Computing Research (ACR’24)
EditorsKevin Daimi, Abeer Al Sadoon
Number of pages12
ISBN (Electronic)9783031569500
ISBN (Print)9783031569494
Publication statusPublished - 29 Mar 2024
Event2nd International Conference on Advances in Computing Research, ACR 2024 - Madrid, Spain
Duration: 3 Jun 20245 Jun 2024

Publication series

NameLecture Notes in Networks and Systems
Volume956 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


Conference2nd International Conference on Advances in Computing Research, ACR 2024

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