Guide To Machine Learning For Application and Cyber Security

These days, our lives are simply becoming digital. Whether it is a large business or a small startup; everyone relies on the various software, web applications, API’s, and serverless assets. These assets come with both, complex code and maintaining and establishing the security for all these applications come as a daunting task on its own.

It has also been said that 75% of the attacks occur at the application level.

You might have used the traditional security solutions, but it only offers the protection at a network layer. However, there are various technologies like Machine Learning that fills the gap and works on advanced cyber attacks like- spear phishing, watering hole, ransomware, remote exploitation and web shell.

What Is Machine Learning?

At the simplest level, Machine Learning can be defined as the ability of computers to learn new behaviours on the basis of empirical data.

It is a system that can take a decision on its own without being programmed to do so. In the case of cyber security, this technology takes a look at previous cyber attacks and enables an automated defense system with effective defense responses.

Machine Learning can be split into two primary disciplines-

Supervised Machine Learning

Supervised Machine Learning has made the most significant impact on the area of cyber security. It works for spam detection, malware identification, and also works for risk scoring and anomaly detection.

Anomaly detection detects the normal behavior and then identifies the other behavior as an anomaly, which is sent to you as a potential threat.

Risk Scoring is used to check the probability of the user’s behavior to be malicious or not.

Unsupervised Machine Learning

Unsupervised Learning does not bring any anomaly in data, but it helps you to cluster the various data records such as traffic, chances of attack, and reducing the dimensionalities.

The dimensionality reduction allows you to explore the data, classify the entities, and asses the anomalies with this data.

Google says that their 50%-70% of e-mails are spam. With the help of machine learning, Google blocks such kind of unwanted communication with 99% accuracy.

Amazon has also acquired Start-Up Harvest and launched Macie that uses the machine learning to classify and sort the data on their cloud service.

The Final Word

Although Machine Learning does not works like magic for cyber threats, but it is by far the most promising area in this realm. You can also use tools like Web Eagle that secure your online presence sends you reports to warn against a threat.