Affiliations: [a] Department of Computer Science & Engineering, Tagore Institute of Engineering and Technology, Attur, Tamil Nadu, India
| [b] Department of Electrical and Electronics Engineering, Tagore Institute of Engineering and Technology, Attur, Tamil Nadu, India
Correspondence:
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Corresponding author. S. Jayanthi, Professor, Department of Electrical and Electronics Engineering, Tagore Institute of Engineering and Technology, Attur, Tamil Nadu-636112, India. E-mail: Drjayanthiphd74@gmail.com.
Abstract: The proliferation of mobile technology has given rise to a multitude of applications, among them those designed with malicious intent, aimed at compromising the integrity of mobile devices (MDs). To combat this issue, this study introduces an innovative anomaly application detection system leveraging Federated Learning in conjunction with a Hyperbolic Tangent Radial-Deep Belief Network (FL-HTR-DBN). This system operates through two distinct phases: training and testing. During the training phase, the system first extracts log files and transforms them into a structured format, harnessing the power of the Hadoop System. Subsequently, these structured logs are converted into vector representations using the Updating Gate-BERT (UG-BERT) technique, thereby facilitating feature extraction. These features are then annotated utilizing the Symmetric Kullback Leibler Divergence squared Euclidean distance-based K Means (SKLD-SED K Means) algorithm. The FL-HTR-DBN model is subsequently trained using these labelled features. The detected anomalies are hashed and securely stored within an index tree, alongside their corresponding hashed Media Access Control (MAC) addresses. In the testing phase, log files are cross-referenced with the hashed index tree to identify potential anomalies. Notably, this novel approach outperforms many valuable outcomes in comparison with the existing approaches ConAnomaly, QLLog and LogCAD in terms of precision 97.5, recall 97.1, accuracy 95.9, F-measure 93.9, sensitivity 94.8 and specificity 95.9.
Keywords: Updating gate –BERT (UG-BERT), symmetric kullback leibler divergence squared euclidean distance-based K means (SKLD-SED K Means), federated learning based hyperbolic tangent radial - deep belief network (FL-HTR-DBN)
