TY - GEN
T1 - Empirical Evaluations of Machine Learning Effectiveness in Detecting Web Application Attacks
AU - Ismail, Muhusina
AU - Alrabaee, Saed
AU - Harous, Saad
AU - Choo, Kim Kwang Raymond
N1 - Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.
PY - 2024
Y1 - 2024
N2 - Web applications remain a significant attack vector for cybercriminals seeking to exploit application vulnerabilities and gain unauthorized access to privileged data. In this research, we evaluate the efficacy of eight supervised machine learning algorithms - Naive Bayes, Decision Tree, AdaBoost, Random Forest, Logistic Regression, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Artificial Neural Network (ANN) - in detecting and countering web application attacks. Our results indicate that KNN and Random Forest classifiers achieve an accuracy rate of 89% and an area under the curve of 94% on the CSIC HTTP dataset, a commonly used benchmark in the field. Meanwhile, the Naive Bayes classifier proves the most efficient, taking the least computational time when differentiating between malicious and benign HTTP requests. These findings may help direct future efforts towards more efficient, machine learning-driven defenses against web application attacks.
AB - Web applications remain a significant attack vector for cybercriminals seeking to exploit application vulnerabilities and gain unauthorized access to privileged data. In this research, we evaluate the efficacy of eight supervised machine learning algorithms - Naive Bayes, Decision Tree, AdaBoost, Random Forest, Logistic Regression, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Artificial Neural Network (ANN) - in detecting and countering web application attacks. Our results indicate that KNN and Random Forest classifiers achieve an accuracy rate of 89% and an area under the curve of 94% on the CSIC HTTP dataset, a commonly used benchmark in the field. Meanwhile, the Naive Bayes classifier proves the most efficient, taking the least computational time when differentiating between malicious and benign HTTP requests. These findings may help direct future efforts towards more efficient, machine learning-driven defenses against web application attacks.
KW - Machine Learning
KW - Web Attacks
KW - Web Vulnerabilities
UR - http://www.scopus.com/inward/record.url?scp=85180625821&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85180625821&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-50051-0_8
DO - 10.1007/978-3-031-50051-0_8
M3 - Conference contribution
AN - SCOPUS:85180625821
SN - 9783031500503
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 99
EP - 116
BT - Future Access Enablers for Ubiquitous and Intelligent Infrastructures - 7th EAI International Conference, FABULOUS 2023, Proceedings
A2 - Perakovic, Dragan
A2 - Knapcikova, Lucia
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th EAI International Conference on Future Access Enablers of Ubiquitous and Intelligent Infrastructures, EAI FABULOUS 2023
Y2 - 24 October 2023 through 26 October 2023
ER -