Applications of Machine Learning against Cyber Security threats


Machine learning (ML) is one of the most important elements of the artificial intelligence (AI) subspace.  It is defined as the science of making computers self-sufficientso they can initiate actions without being explicitly programmed.It works on algorithms and uses computational methods that allows the system to self-learn from past experiences (data) without relying on predetermined equations as a model.Lately, IT professionals have been incorporating machine learning solutions to cope up with one of the biggest global risks, cybercrimes.

According to statistics, India experiences 17% of global targeted attacks while incurring losses of almost $18.5 billion annually. These figures indicate a strong need of ML and strategically implemented artificial intelligence services in the cyber security domain in order to safeguard users against various types of cybercrimes.

Some cyber security threats that can be prevented with machine learning applications include:

1. Ransomware

Ransomware is a terminology derived from the words ransom and software. It is a malicious software(a kind of malware) that hacks the system and preventsthe user to access personal files until a ransom amount is paid. According to IT experts, there are two major types of ransomware; file coder which leads to file encryption and lock screen which locks the computer. However, IT professionals can use machine learning solutions and neural networks to detect unknown ransomewares. According to experts, data sets can also be trained to effectively analyze the micro-behavior of ransomware attacks.

2. Spear Phishing 

Spear phishing is a fraudulent practice which includes sending of ostensible mails for inducing people to reveal their personal information.However, enterprises can augment spear phishing prevention while averting loss situations with the help of machine learning services offered by globally recognized certified consultants. The predictive URL classification model based on ML algorithms can identify patterns to reveal the malicious sender’s mail. It evaluates email headers, punctuation patterns, etc. for detecting the malicious nature of the mail. 

3. Watering Hole 

Watering hole, or commonly known as watering hole attack, is a system attack strategy used by hackers. They track and observe the most common user visited websites and infect them with malware attacks. But since machine learning algorithms can analyze the path traversals of the website, they can ensure and benchmark the security standards ofweb applications. They can also detect malicious domains and monitor rare or extraordinary redirection patterns to and from the website’s host. 

The applications of machine learning can also be used in other cyber security domains like webshell prevention and remote exploitation. The respective applications effectively analyze system behavior and can identify abnormal instances against normal network stances.   
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