A Novel Meta-Heuristic-Based Sequential Forward Feature Selection Approach for Anomaly Detection Systems

2016 ◽  
Author(s):  
Yukang Liu ◽  
Zhen Xu ◽  
Jing Yang ◽  
Liming Wang ◽  
Chen Song ◽  
...  
Keyword(s):  
Feature Selection ◽  
Anomaly Detection ◽  
Detection Systems ◽  
Selection Approach ◽  
Feature Selection Approach

scholarly journals An Efficient Malware Detection System using Hybrid Feature Selection Methods

2019 ◽  
Vol 9 (1S3) ◽  
pp. 224-228
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Unique Feature ◽  
Detection System ◽  
Malware Detection ◽  
Selection Methods ◽  
Training Process ◽  
Detection Systems ◽  
Selection Approach ◽  
Feature Selection Approach

Malware is a serious threat to individuals and users. The security researchers present various solutions, striving to achieve efficient malware detection. Malware attackers devise detection avoidance techniques to escape from detection systems. The key challenge is that growth of malware increases every hour, leading to large damages to users’ privacy. The training process takes much longer time, mining the unnecessary features. Feature Selection is effective in achieving unique feature set in detecting malware. In this paper, we propose a malware detection system using hybrid feature selection approach to detect malware efficiently with a reduced feature set. Machine learning based classification is performed on eight classifiers with two malware datasets. The experiments were done without and with feature selection. The empirical results show that the classification using selected feature set and XGB classifier identifies malware efficiently with an accuracy of 98.9% and 99.26% for the two datasets.

scholarly journals Hybrid feature selection approach to identify optimal features of profile metadata to detect social bots in Twitter

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eiman Alothali ◽  
Kadhim Hayawi ◽  
Hany Alashwal
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Cross Validation ◽  
Performance Metrics ◽  
Area Under The Curve ◽  
Support Vector ◽  
Detection Systems ◽  
Vector Machines ◽  
Selection Approach ◽  
Feature Selection Approach

AbstractThe last few years have revealed that social bots in social networks have become more sophisticated in design as they adapt their features to avoid detection systems. The deceptive nature of bots to mimic human users is due to the advancement of artificial intelligence and chatbots, where these bots learn and adjust very quickly. Therefore, finding the optimal features needed to detect them is an area for further investigation. In this paper, we propose a hybrid feature selection (FS) method to evaluate profile metadata features to find these optimal features, which are evaluated using random forest, naïve Bayes, support vector machines, and neural networks. We found that the cross-validation attribute evaluation performance was the best when compared to other FS methods. Our results show that the random forest classifier with six optimal features achieved the best score of 94.3% for the area under the curve. The results maintained overall 89% accuracy, 83.8% precision, and 83.3% recall for the bot class. We found that using four features: favorites_count, verified, statuses_count, and average_tweets_per_day, achieves good performance metrics for bot detection (84.1% precision, 81.2% recall).

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