FREMONT, CA: By 2018, nearly 70% of enterprises had experienced some form of a cybersecurity attack, and over half of them had experienced a data breach as per reports. This vast threat landscape makes cyber-security a critical area in which Machine Learning (ML) is increasingly becoming significant. But ML in cyber-security extends far beyond merely applying established algorithms to cyber entities. ML is not a cure-all for cybersecurity, but it does introduce intelligence to an organization’s first level of defense against cyber threats. Taking a look at how ML work and how they can be used for security purposes will be interesting to do.
The world today has robust machine learning-powered spam filters, which act based on different sets of rules to identify and filter spam in a cost-effective manner. These spam filters are highly flexible and efficient compared to mere knowledge-based methods for combating cybercrime in the present context. They work entirely based on dynamic algorithms, which are founded upon pre-classified datasets that classify emails as spam or not spam based on features including the hyperlinks, the attachments, then the IP address to name a few.
Risk detection and responding to potential threats promptly is one of the very foundations of cybersecurity. ML used for cybersecurity helps monitor, analyze, and respond to all kinds of threats and attacks that happen on the networks, the software, the applications, and the hardware. One should not forget that infiltration or infection of a system occurs much before detection and remain there inactive for months before launching an attack. Machine learning comes in handy here, playing a pivotal role in identifying and detecting cybercrime, thereby protecting networks and their components from all kinds of risks.
ML-powered systems are astonishingly transforming the safety of the whole internet. In reality, advances in the field of machine learning have opened a completely new era of cybersecurity.
We have worked out a big shift from the former rule-based malware-detection methods and focus more on detecting malware by analyzing files during the pre-execution phase itself using machine learning. Detecting advanced malware attacks, including ransomware attacks, have thus become easier and more effective, thanks to machine learning. We also use deep learning algorithms to detect rare, high-profile targeted attacks. Thus, machine learning is helping us detect all kinds of malware including trojans, ransomware, adware, spyware etc.
In adopting ML, organizations need to maintain their human capacity to oversee and manage AI and ML technology. They cannot abdicate responsibility for the outcomes produced by ML software to machines, so they need people to be aware of issues around ML transparency, trustworthiness and interoperability.