Anomaly Detection Using Supervised learning Techniques in Social Networks

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

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...

A Study of Anomaly Intrusion Detection Using Machine Learning Techniques

In the era of information systems and internet there is more concern rising towards information security in daya to day life, along with the availability of the vulnerability assessment mechanisms to identifying the electronic attacks.Anomaly detection is the process of attempting to identify instances of attacks by comparing current activity against the expected actions of intruder. Machine le...

Toward Supervised Anomaly Detection

Anomaly detection is being regarded as an unsupervised learning task as anomalies stem from adversarial or unlikely events with unknown distributions. However, the predictive performance of purely unsupervised anomaly detection often fails to match the required detection rates in many tasks and there exists a need for labeled data to guide the model generation. Our first contribution shows that...

Detection of money laundering groups using supervised learning in networks

Money laundering is a major global problem, enabling criminal organisations to hide their ill-gotten gains and to finance further operations. Prevention of money laundering is seen as a high priority by many governments, however detection of money laundering without prior knowledge of predicate crimes remains a significant challenge. Previous detection systems have tended to focus on individual...