January 18, 2017

Utilizing Machine Learning In The Security Sector

By Jennifer Klostermann

Machine Learning Security

Machine learning for data security and protection is a popular topic, though expert opinion on the current probability of such uses is divided. And while computer algorithms have been making decisions for us for many years now, the future applications of such machine intelligence are far more complex than anything we’ve seen yet. For some, trusting a machine to manage weighty aspects of our lives is disagreeable; the EU’s General Data Protection Regulation penned for 2018 tackles such conflict through a clause which may give citizens the option of having machine-driven processes elucidated – a possibility causing some angst in many organizations that rely on sophisticated algorithms and predictive modeling. But what of trusting our machines, in collaboration with big data, to facilitate security and privacy?

Airport Security Systems Implementing Machine Learning

Already a highly security-conscious environment, we may start seeing Machine Learning implemented in airports for greater protection. Using collected data, it’s possible for behavior recognition programs to supplement scanners and baggage checks by recognizing dangerous behaviors. This blend of psychology and tech works according to the principle that those in the process of illicit exploits behave abnormally; stress can sometimes be perceived in criminals who act nervously or direct all their attention toward security procedures, and certain facial expressions, along with perspiration, lack of eye contact, and attempts to stay out of sight are common tip-offs to potential threats. Currently, such behavior is monitored by other humans; however, implementing a computer system that has no biases, is unbound by fear of giving offense, never feel tired or get distracted, and can measure the risk of every single person that passes through an environment promises both increased accuracy and a far broader reach.

Confronting Cybercrime with Machine Learning

Some believe cyber criminals will find ways of outwitting machine learning as a cyber security tool, but though threats will always evolve to match and one-up the security provisions we put in place, there’s no reason to give up on the new and more sophisticated devices available. Using machine learning it’s possible to find complex threat patterns and enhance the protection surrounding IT environments in a variety of ways.

Similarly to the behavioral monitoring which could have valuable applications in our physical world, the analysis of computer behaviors including browsing habits, keystrokes, mouse movements and PC usage times could offer some protection in the cyber realm. By utilizing machine learning algorithms able to recognize fraudulent and suspicious behavior it’s easier to prevent unauthorized access by both human and non-human attackers. A second valuable tool in the security and privacy of data is data cleaning through machine learning; implementing analytics tools to clean data helps enhance the value of data while limiting risks of exposure through precise anonymization and threat removal.

And machine learning additionally provides further data privacy and security through the structuring of policies built on data usage patterns. It’s able to measure the efficiency of rules applied, improve them through algorithmic calculation, and effectively supplement existing decision processes that determine who has access to what data and for what period of time.

Imbuing machines with the ability to learn without programming is an extraordinary notion, but it’s necessary also to recognize that it still holds many limitations. To begin with, machine learning depends on data, and the quality of that data, and though we live in a world overflowing with data there are still many areas in which it is scarce or of a poor standard. Furthermore, machine learning solutions are still in the early stages and as such typically inflexible and enigmatic. Nevertheless, the power machine learning provides over data along with the future possibilities we’re imagining are enough to keep us on an optimistic and resourceful path well into the future.

By Jennifer Klostermann

Jennifer Klostermann

Jennifer Klostermann is an experienced writer with a Bachelor of Arts degree majoring in writing and performance arts. She has studied further in both the design and mechanical engineering fields, and worked in a variety of areas including market research, business and IT management, and engineering. An avid technophile, Jen is intrigued by all the latest innovations and trending advances, and is happiest immersed in technology.
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