Applying machine learning for advanced web threat
Technology moves swiftly. Today machine learning is not like machine learning in the past. Machine learning is a method of data analysis that learn from data iteratively by using algorithms and allows computers to find hidden sight Computers can learn without being executing program and they are able to adapt independently as models are exposed to new data.
The task of online shopping as a simple example, most large online shopping channel will recommend items that you may want to purchase. These recommendations come out based shopping history, recent searches and related people or friend. Machine learning analyzes these large amounts of data and allows customers to purchase items by recommending items that are likely to purchase. Shopping is not the only industry to leverage recent advances in machine learning and other industries is growing to the various applications of machine learning such as fraud detection, email spam filtering, video analysis and advanced threats.
When we look at the cyber security industry, machine learning approaches are good fit for this industry. Machine learning is able to analyze increasing advanced cyber threats by collecting and storing large amounts of data. It would be difficult to analyze the vast amount of raw data that is collected every day in mature environment. Also, machine learning allows defending vital infrastructure and system effectively. Machine learning algorithms have stepped in to fill in the gap between proactivity and detection and it is better at applying models on large amounts of data without tiring or complaining of repetitive task.
In the cyber security industry, it needs a shift in the way we think about technology and its capabilities needs to apply before we fully trust the machine learning system.