Cybersecurity threats are constantly evolving, making it more and more difficult for analysts to use traditional methods to effectively counteract them. According to the Malware Byte State Malware report, malware like ransomware, botnets and attack vectors notably increased in 2016. Likewise, reports of banks, major retailers and even tech companies being hacked are becoming increasingly frequent. While machine learning is the new standard of data analysis, it is only beginning to be used in cybersecurity. However, this powerful analytic tool has the ability to greatly improve threat prediction accuracy throughout the industry.
Machine learning is separated into two broad categories: supervised learning and unsupervised learning. In both cases, data is trained and tested against a known outcome until it is determined to have a statistically high degree of predictive accuracy. If the parameters of what constitutes a category (e.g., a square is something with four equilateral sides) is already known, then supervised learning is used. On the other hand, if a pattern needs to be identified and categorized, unsupervised learning methods are employed. Both of these machine learning techniques are applicable to the field of cybersecurity.
Encryption and cryptography are widely used in the cybersecurity industry and primarily work by keeping information secure as it travels from one destination to another. By applying machine learning techniques, cybersecurity analysts can become even more proactive and less reactive in the face of threats. Unsupervised learning can be used to help cybersecurity analysts figure out what constitutes normal consumer behavior. Machine learning consists of a lot of testing, trial and error, and fine tuning. As a result, algorithms, and ultimately cybersecurity detection, can easily be adapted to meet the ever-changing landscape of cyber threats.
Supervised learning can be applied to big data, generated from previous attacks, and then used to analyze that data to see if it fits into the defined category of malicious behavior or a cyber attack. If the data fits, it can then be used as a litmus test for differentiating normal behavior from abnormal. As cyber threats become increasingly sophisticated, machine learning will become one of the most effective strategies that analysts can employ to not only remedy them once they occur but also stop them from ever taking place. In addition, the more data an algorithm trains on, or learns from, the better it will be able to predict a threat. In the first half of 2017, a record number of large-scale cyber attacks occurred, including the infamous WannaCry, which crippled hospitals and health providers throughout the UK.
Data rarely arrives neatly ordered and ready for analysis. In fact, most data scientists spent the vast majority of their time preparing and cleaning data. When it comes to cybersecurity, an analyst may not have the time to clean petabytes of data, analyze it and then train on an algorithm as well. Enter deep learning. This branch of machine learning uses brain-like neural networks to assess even the messiest or incomplete of data sets. As IoT devices come online, many of which lack even the most basic of security defenses, neural nets will be able to make sense of the unordered data that they generate and use it to prevent potential attacks.
The world relies on cybersecurity software and technicians to keep information and data secure. As this becomes increasingly challenging with traditional methods, the industry must look to new and innovative strategies, like those used in machine learning, not only to keep up with more frequent and complex threats but also to prevent them from happening in the first place. Machine learning is the future of cybersecurity, and the future is here.
https://www.malwarebytes.com/pdf/white-papers/stateofmalware.pdf
https://www.wired.com/story/2017-biggest-hacks-so-far/
https://www.toptal.com/it/are-we-creating-an-insecure-internet-of-things
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