Dedicated web application testing is fast becoming essential to any organisation’s cyber security approach.
The rapid proliferation of connected devices and the direct links among applications, http protocol, and web services create numerous points of vulnerability for hackers to exploit. Machine learning technology offers an innovative way to prevent these attacks and optimise web application security testing.
How Machine Learning will Strengthen the Web Application Security Testing Market, a recent whitepaper from Frost & Sullivan’s Digital Transformation Growth Partnership Service, details the evolution of web application attacks, showcases different machine learning algorithms, explains the best approach to select a machine learning partner for web application testing and provides an exciting innovation matrix regarding the web security testing ecosystem.
“Machine learning algorithms are already disrupting the traditional cybersecurity ecosystem,” said Frost & Sullivan Digital Transformation Global Program director, Jean-Noël Georges.
“The technology will allow testing companies to create an extra layer of expertise to better detect potential threats in real time. The first layer, based on the traditional linear software analysis, can quickly detect a long list of vulnerabilities that will include several false positives.
"The second layer of analysis based on machine learning will be deeper, enabling better detection of vulnerabilities and reduced false positives. This process optimises the list of vulnerabilities and threats, supporting the third and final human-augmented analysis and reporting.”
By 2020, Frost & Sullivan expects the number of connected devices to touch 22.6 billion units and the number of connected cars to reach 51 million units globally. Even with sophisticated cyber security systems, it will be hard to protect web platforms and cloud-based services and solutions.
Companies need a dedicated cybersecurity strategy based on application testing, monitoring and defense that incorporates robust web security testing. With machine learning, cyber security providers can better optimise counterattacks based on approved algorithms such as clustering and neural networks or artificial neural networks (ANNs).
“Having software perform the vulnerability analysis reporting will significantly reduce the cost and improve the scalability and quality of analysis,” said Georges.
“Machine learning, coupled with human augmentation, will offer a competitive mix of scalability, quality and cost. The machine learning approach will support robust vulnerabilities detection, where the entire flaw is tested with zero false-positives.”
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