Cyber Security and Machine Learning
What is the potential and future of this technology in the world of hackers?
The landscape of adversaries and cybercriminals in computer security has derived over time, but the main attack vectors and threat types remained the same. Over years much research has been done to increase our knowledge about threat types and their complexities. With the guidance of those researchers, before taking precautionary actions, institutions can have a good understanding of the various attack types.
Nowadays any curious teenager who has an interest in hacking can acquire malicious software from the darknet. Darknet marketplace is not only an easy-access platform for hacking tools, malware, and hacking scripts, it has its own ecosystem for trading and purchasing.
Moreover, many hackers can trade/sell vulnerabilities, exploits, and personal information (credit card numbers, Social Security numbers, email accounts, phone numbers, addresses, and other private information) on the darknet and it makes the darknet a viable source of income for malignant users. Published 2021 cyber attacks statistics also reveal the expansion of malicious attacks. According to a recent CheckPoint Research (CPR) report, the number of cyberattacks per week on corporate networks increased by 50% in 2021 compared to 2020.
Growth of Cybercrime Costs
Not only are cyberattacks increasing, at the same time the damage that is caused to the economy is dementedly extending. That is too much volume for cyber security experts to handle without the help of Machine Learning.
Machine learning, a sub-member of the artificial intelligence group.
Benefiting from algorithms created from preceding datasets and statistical inquiries to make presumptions about computer behavior. This predictive ability of machine learning helps automatize many tasks in the field of cybersecurity.
We can allocate ML use cases in Cyber Security into two main categories: Pattern recognition and anomaly detection. With pattern recognition, security experts aim to discover explicit or inherent attributes in data. When those attributes sort out from the feature sets, it can be used to teach algorithms to detect the other patterns of the data that show the same set of attributes.
Anomaly detection does knowledge discovery from the other side of the same coin. The goal is not to learn a specific pattern that exists in a specific subset of the data but to establish a notion of normality that describes the majority of a given dataset. After that, all deviations from this normal value are identified as anomalous.
Lastly, spam detection is a classic example of pattern recognition. Since spam messages can contain many distinguishing characters, it is possible to reveal these pre-learned characters by the algorithm.
Written by Göktuğ Önyer
 Data Mining and Machine Learning in Cybersecurity – Sumeet Dua and Xian
 Check Point Research – 2021 Cyber Security Report
 Embroker – 2022 Must-Know Cyber Attack Statistics and Trends
 Machine Learning & Security – Clarence Chio & David Freeman