MACHINE LEARNING APPROACH TO ANOMALY DETECTION IN CYBER SECURITY WITH A CASE STUDY OF SPAMMING ATTACK

Now the standalone computer and information flow in the internet are sources continues to expose an increasing number of security threats and causes to create a nonrecoverable victims with new types of attacks continuously injecting into the network applications. For this to develop a robust, flexible and adaptive security solution is a severe challenge. In this context, anomaly detection technique is an advanced adornment technique to protect data stored in the systems and while flow in the networks against malicious actions. Anomaly detection is an area of information security that has received much attention in recent years applying to most emerging applications. So in this paper we are going to elaborate a latest technique available in machine learning applied to anomaly detection which is used to thwarts the latest attacks created by attackers and here the spam is also a type of anomaly and it is classified as legitimate (ham) or spam. Finally a case study is discussed on latest spamming attacks infected on top web domains and countries in the world motivated by a traditional security ethic are awareness.

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