How Machine Learning helps find exceptions to stop Cybercriminals?

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Look how Machine Learning helps experts to find anomalies to find Cybercriminals?

You must be aware of the scams and cybercriminals activities happening around you, through these activities millions of money has been stolen by the threat actors. It is really difficult for experts to detect these anomalies and stop the actors from cybercriminals activities. Yesterday we reported how cybercriminals are using the top online stores’ favicon for web skimming the credit card details of the users.

There are many privacy concerns surrounding cybercrime when confidential information is intercepted or disclosed, lawfully, or otherwise. Computer fraud is any dishonest misrepresentation of fact intended to let another to do or refrain from doing something which causes loss. These threat actors web skim you by creating fake domains of the popular website and ask you to log in. The moment you log in to your account the actor gets access to your password and the data present on your account.

You must be thinking in all this how Machine Learning helps security experts to find loopholes and catch the threat actors. According to thenextpost.com blog, Companies employ machine learning to monitor emails, login attempts, personal transactions, and business activities every day. Most financial institutions use a kind of AI called anomaly detection, a process through which computers can classify activity on a consumer’s account as either typical or suspicious.

What is Anomaly detection?

In data mining, anomaly detection is the identification of rare items, events, or observations that raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problems such as bank fraud, a structural defect, medical problems, or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations, and exceptions.

In particular, in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts inactivity. This pattern does not adhere to the common statistical definition of an outlier as a rare object, and many outlier detection methods (in particular unsupervised methods) will fail on such data unless it has been aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the microclusters formed by these patterns.

One of the challenges with anomaly detection, especially when using deep learning techniques, is that it’s sometimes difficult to understand why certain transactions or companies were singled out as suspicious. Strictly speaking, the machine simply yields groupings and anomalies, hence requiring a human specialist to interpret the results. But what if an AI could tell us not only what the anomalies are, but also why they were classified as such? This emerging discipline is called explainable AI (XAI).

Anomaly detection was proposed for intrusion detection systems (IDS) by Dorothy Denning in 1986. Anomaly detection for IDS is normally accomplished with thresholds and statistics, but can also be done with soft computing, and inductive learning. Types of statistics proposed by 1999 included profiles of users, workstations, networks, remote hosts, groups of users, and programs based on frequencies, means, variances, covariances, and standard deviations. The counterpart of anomaly detection in intrusion detection is misuse detection.

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