Detecting Malicious Domains Using Modified SVM Model

Zhu, Jiachen; Zou, Futai

doi:10.1109/hpcc/smartcity/dss.2019.00079

Cited by 5 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Across many instances, the study found that utilizing lexical features with the other features can increase the detection efficiency of fraudulent websites using Random Forest (RF) with the precision of 0.98. Furthermore, Zhu and Zou et al [15] discovered that, as the detection procedure progresses, the detection performance of a standard Support Vector Machine (SVM) model decreases; however, a revised SVM technique (F-SVM) proposed by the study is effective in maintaining a significant precision rate throughout the detection phase. Due to its high performance, it is well-suited for online detection.…”

Section: Related Studiesmentioning

confidence: 99%

Interpretable Machine Learning Models for Malicious Domains Detection Using Explainable Artificial Intelligence (XAI)

et al. 2022

View full text Add to dashboard Cite

With the expansion of the internet, a major threat has emerged involving the spread of malicious domains intended by attackers to perform illegal activities aiming to target governments, violating privacy of organizations, and even manipulating everyday users. Therefore, detecting these harmful domains is necessary to combat the growing network attacks. Machine Learning (ML) models have shown significant outcomes towards the detection of malicious domains. However, the “black box” nature of the complex ML models obstructs their wide-ranging acceptance in some of the fields. The emergence of Explainable Artificial Intelligence (XAI) has successfully incorporated the interpretability and explicability in the complex models. Furthermore, the post hoc XAI model has enabled the interpretability without affecting the performance of the models. This study aimed to propose an Explainable Artificial Intelligence (XAI) model to detect malicious domains on a recent dataset containing 45,000 samples of malicious and non-malicious domains. In the current study, initially several interpretable ML models, such as Decision Tree (DT) and Naïve Bayes (NB), and black box ensemble models, such as Random Forest (RF), Extreme Gradient Boosting (XGB), AdaBoost (AB), and Cat Boost (CB) algorithms, were implemented and found that XGB outperformed the other classifiers. Furthermore, the post hoc XAI global surrogate model (Shapley additive explanations) and local surrogate LIME were used to generate the explanation of the XGB prediction. Two sets of experiments were performed; initially the model was executed using a preprocessed dataset and later with selected features using the Sequential Forward Feature selection algorithm. The results demonstrate that ML algorithms were able to distinguish benign and malicious domains with overall accuracy ranging from 0.8479 to 0.9856. The ensemble classifier XGB achieved the highest result, with an AUC and accuracy of 0.9991 and 0.9856, respectively, before the feature selection algorithm, while there was an AUC of 0.999 and accuracy of 0.9818 after the feature selection algorithm. The proposed model outperformed the benchmark study.

show abstract

Section: Related Studiesmentioning

confidence: 99%