NHAD: Neuro-Fuzzy Based Horizontal Anomaly Detection In Online Social Networks

Anomaly detection in online social networks

Anomalies in online social networks can signify irregular, and often illegal behaviour. Detection of such anomalies has been used to identify malicious individuals, including spammers, sexual predators, and online fraudsters. In this paper we survey existing computational techniques for detecting anomalies in online social networks. We characterise anomalies as being either static or dynamic, a...

Anomaly Detection using Neuro Fuzzy system

As the network based technologies become omnipresent, demands to secure networks/systems against threat increase. One of the effective ways to achieve higher security is through the use of intrusion detection systems (IDS), which are a software tool to detect anomalous in the computer or network. In this paper, an IDS has been developed using an improved machine learning based algorithm, Locall...

Anomaly Detection in Online Social Networks: Using Data- Mining Techniques and Fuzzy Logic

Mote-Based Online Anomaly Detection Using Echo State Networks

Sensor networks deployed for scientific data acquisition must inspect measurements for faults and events of interest. Doing so is crucial to ensure the relevance and correctness of the collected data. In this work we unify fault and event detection under a general anomaly detection framework. We use machine learning techniques to classify measurements that resemble a training set as normal and ...

Overlapping Community Detection in Social Networks Based on Stochastic Simulation

Community detection is a task of fundamental importance in social network analysis. Community structures enable us to discover the hidden interactions among the network entities and summarize the network information that can be applied in many applied domains such as bioinformatics, finance, e-commerce and forensic science. There exist a variety of methods for community detection based on diffe...