Machine Learning in Security Industry

Deloitte Global predicts the number of machine learning and implementations will double in 2018 compared to 2017,and double again by 2020.

– Deloitte Global Predictions

Today’s digital world is producing humongous amounts of data, but to make sense of this data one needs to mine it, and that’s where  Data Science and Machine Learning come into the picture. These two technologies can be applied in parallel to solve the increasingly complex cyber threat landscape where manual methods are falling short.

The process of ML starts with extracting features of underlying data and this is where Data Science is extensivelyused. Selecting good features is one of the most important steps in training any MLmodel.

In data, there may be groups of co-related features.  Removing orcombining such features saves space and time and improves the performance of machine learningmodels. This process of reducing the number of unwanted or redundant features is known asdimensionality reduction.

Machine learning can be used to cluster even huge samples and them, map them to existing clusters orgenerate new ones. Simply put, theaim is to segregate groups with similar traits and assign them into clusters. At regular intervals, these clusters arere-clustered to accommodate newer samples, which can be referred to as incremental clustering. Various ML clusteringalgorithms like Centroid models, Distribution models and Density Models can be used for this purpose.

A daily job of processing these generated clusters is huge, and Machine Learning is quite extensivelyused for it. ML algorithms are highly effective to aggregate, analyze large-scale data and to automate the processof classification. Once the clusters are prepared they are labelled and sent to an automated malware classification system. Very often contextualinformation is used for enabling insights for classifying sample as malicious. Data mining is extensively used totrace the anomalies among the processed samples to clearly distinguish malicious and benign samples. These classified samples are thenfurther processed to generate Machine Learning models which can be deployable to endpoints for security.

This process of Machine Learning model preparation includes various aspects like selecting right set & right ratio ofbenign and malware samples, dividing the selected set into training & test set and finally selecting right ML algorithms to generate the models.

The generated models can be scrutinized on numerous factors in order to get qualified for endpoint deployment.

So, Machine Learning, in combination with DataScience and human expertise, is a winning formula for delivering very smart security solutions.