![](bg8.png)
ering superior results across diverse datasets.
Table 4: Results of the Experiments (Top two results of each
metric are bolded, precedence of MASD is certain).
Method Metric Banking CSIC2010 WAF
MASD
with RF
(Proposed)
Accuracy 0.9583 0.9667 0.9917
Recall 0.9583 0.9667 0.9917
Precision 0.9594 0.9662 0.9917
F1 Score 0.9587 0.9660 0.9916
SVM
Accuracy 0.8333 0.8333 0.8333
Recall 0.8333 0.8333 0.8333
Precision 0.6944 0.6944 0.6944
F1 Score 0.7576 0.7576 0.7576
Na
¨
ıve
Bayes
Accuracy 0.8417 0.7833 0.6583
Recall 0.8417 0.7833 0.6583
Precision 0.9188 0.8933 0.8880
F1 Score 0.8589 0.8092 0.7008
CNN
Accuracy 0.8333 0.8333 0.8417
Recall 0.8333 0.8383 0.8417
Precision 0.6944 0.6944 0.8669
F1 Score 0.7576 0.7576 0.7769
Decision
Tree
Accuracy 0.9083 0.8917 0.9750
Recall 0.9083 0.8917 0.9750
Precision 0.9409 0.9343 0.9783
F1 Score 0.9156 0.9012 0.9757
DBSCAN
Accuracy 0.7000 0.7417 0.7500
Recall 0.7000 0.7417 0.7500
Precision 0.7901 0.8521 0.8241
F1 Score 0.7317 0.7720 0.7748
SOM
Accuracy 0.9167 0.9417 0.9833
Recall 0.9167 0.9417 0.9833
Precision 0.9133 0.9455 0.9837
F1 Score 0.9105 0.9365 0.9830
8 CONCLUSIONS
In the realm of web session security, this paper repre-
sents a significant stride forward by presenting a ma-
chine learning-grounded solution for the detection of
malicious web sessions. The study introduces a novel
classifier-driven methodology that capitalizes on the
potency of machine learning algorithms to discern
and classify malicious web sessions. This approach
is applied across three distinct datasets, underscoring
its versatility and potential effectiveness. Notably, the
approach stands out by achieving a remarkable ac-
curacy rate, dispelling the need for the often restric-
tive feature extraction phase that typically extracts a
limited array of features. The recorded results strik-
ingly surpass the 99% mark across all assessed met-
rics. These outcomes collectively highlight the poten-
tial efficacy of the machine learning-based classifier
approach in fortifying web session security. Further-
more, the groundwork laid by this research paves the
way for future extensions, possibly encompassing the
classification of diverse user sessions and a deeper ex-
ploration of user behavior patterns.
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