An Integrative Computational Intelligence for Robust Anomaly Detection in Social Networks

Abstract

Anomaly detection is very important in social networks to keep the truth, security and believability of online communities. This paper presents Adapto Detect which uses a fresh anomaly detection scheme named Pufferfish Optimization Technique (POT) for selecting features and Graph Embedding Autoencoder (GEAE) as an anomaly identifier. POT can pick out vital features from social network data well, it concentrates on attributes necessary for spotting anomalies. At the same time, GEAE is also learning low-dimensional representations of graph nodes. This helps to capture more complicated patterns and relationships within the structure of the graph. These embeddings are used for efficient anomaly detection, which shows deviations from regular social network model behaviour. The performance of AdaptoDetect is superior as shown by its thorough evaluation and comparison with existing methods on different social network datasets and situations. The POT and GEAE interaction in AdaptoDetect allows it to adjust for network alterations and effectively handle anomalies. In general, this system offers a strong answer that can easily identify irregularities within social networks – boosting their security, toughness, and trustworthiness online.

Keywords

• Anomaly Detection
• Deep Learning
• Social Networks
• Optimization
• Computational Intelligence Models
• Classification