Using Machine Learning to Flag Online Threats and Drive Cyber Resilience

According to the recent Ponemon Institute global study, about 60% of the company’s respondents believe the inadequate investment in machine learning is the biggest obstacle to cyber resilience. The results from the same study also highlight the increase in the severity of cyber-attacks and the increase in the time taken to resolve a security incident both expose the meagerness in the present-day cyber resilience strategies. Among all security vulnerabilities, nearly 50% of the data breaches occur due to vulnerabilities in web application and that’s the reason why it becomes critical to augment web application security to improve the overall cyber resiliency.

Web Application Firewalls and Application Learning

Web Application Firewalls Solutions are most commonly used to flag online threats and detect malicious activities. Powered by Application Learning algorithms, WAF solutions rely heavily on user activities and system behavior to identify anomalies and trigger adequate responses. This often works satisfactorily for malicious threat detection yet also hinders the organization’s progress by restricting non-malicious traffic and identifying benign anomalies as threats.

These critical issues in no way highlight the inadequacy of Web Application Firewalls and only exemplify the scantiness of Application Learning (AL) Models. However, a simple change of approach and Machine Learning (ML) adoption can truly make these firewalls more effective and significantly less interruptive.    

Using Machine Learning to Drive Cyber Resilience

The innovative machine learning technologies and artificial intelligence brings about new trends and approaches to detect web security vulnerabilities & threats. The machine learning models capitalize on the power of probability to precisely identify anomalies and minimize false positive detections. In this way, these cybersecurity models augment cyber resilience without triggering the blockage of legitimate applications & users.

Both the Application Learning (AL) and Machine Learning (ML) Models accumulate data by observing user behavior and application interactions. However, machine learning runs these data and observations through statistical models to identify discrepancies in the HTTP Request. In this way, only real malicious anomalies are flagged and benign anomalies are left uninterrupted. That’s not all, these anomalies undergo the second layer of statistical analysis to ensure the accuracy of the detection and to separate detections of typos and legitimate application changes.

Importance of Application Security

Application security is unquestionably the leading concern of IT Executives and about 83% of business executives believe application security is an integral part of a robust IT strategy. This advocates the importance of protecting the application environment by leveraging innovative solutions presented by machine learning and artificial intelligence. These models not only effectively drive cyber resilience but also limits the consumption of wasteful IT resources by integrating robust statistical models.