How Machine Learning Can Improve Threat Prediction Accuracy In Cyber Security
According to the Identity Theft Resource Center, there were a record 1,093 known security breaches in 2016, with phishing and skimming the two most common types of attacks. With so many organizations being hacked, applying innovative solutions like machine learning to cyber security is the most effective solution.
Machine learning works by using algorithms to repeatedly train and test data to either categorize it (supervised learning) or identify meaningful patterns (i.e. labels) where none had previously existed (unsupervised learning). Both of these machine learning techniques are strong tools for improving the threat predictive accuracy of cyber security systems. In the case of supervised learning, large data sets of behavioral data, consumer-based or network-based, train to distinguish 'normal' or non-threatening behavior from hacking attempts. The more data the algorithm trains on the better its predictive capabilities. Likewise, unsupervised learning is resourceful in helping IT managers determine what exactly constitutes 'normal' consumer or system behavior. Because the two methods allow for ongoing adjustments and fine-tuning, companies can adapt to ever-changing threats.
Deep Learning, a branch of machine learning that uses brain-like neural networks to detect patterns, also plays a role in improved cyber security. While traditional machine learning techniques are good at analyzing large amounts of data to sort and identify labels, deep learning works a lot like the human brain and is therefore ideal for handling messier, incomplete and or complex data. This will prove especially useful in detecting IoT security threats, as many devices currently have little or no security measures in places, making them likely targets for industrious hackers.
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