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Hacking has been a problem since connectivity came in form or maybe even before that. And since then experts are trying to figure out ways to stop it, block it, and to some extent end it. Over the years many anti-hacking ways evolved according to the hacking techniques, recent technologies and the need for hacking. The latest of it is using Machine Learning to prevent hacking.

What is hacking?

Hacking is gaining unauthorized access to data in a system or computer. Basically, it means gaining data access without the knowledge of the authorized owner. Basically a crime, it later evolved to be ethical too. Ethical hacking came into fashion when the system working on the security of data realized this can act as a boon too. Gaining access to illegal data helped the security systems in getting the data that caused the threat.

As the walls of security went high so did the ways to jump over it. The thief does not stop stealing because of the watchman, does he? With the advances in security, the hackers too found out more innovative ways to carry on with their tasks. Innovation is on the loose on both the sides. Now experts are looking forward to using AI to curb the hacking activity.

Rule-based security systems

Rule-based systems are basically old systems of security. These worked in a fixed pattern to detect breaches into the system. Any other way to enter into the system went undetected. This system did work effectively until the intruders found better ways.

The rule-based system worked according to fixed algorithms that blocked a login into a system if the site of login wasn’t familiar. Or the emails that contained the word ‘Viagra’ or the misspellings of it were blocked. But these systems also sometimes blocked the legitimate user like a card user on vacation to a new place got blocked. This was a drawback of the system.

The false positives rate was 2.8%. This initially does sound to be less but in greater numbers seemed to be a huge threat. All these things led to the invention of new ways and the discovery of using technology.

The new security systems

The newer technology now uses Machine Learning. It learns from the data of the company using it. The system studies the user’s online behavior and history. Using this technique the false positives rate has come down to 0.001% which is very less as compared to the previous system. The security engineers use the data to develop the machine learning algorithms and then make sure they are fast and smart. Smart enough to adapt to new ways of hacking and fast enough to act once a hacking activity is detected.

The new systems now also look for security breaches after a successful login. Initially, if a person logs in successfully he could still be an imposter. The later activities are studied and if the actions seem doubtful the user is blocked for threats. This can successfully help in stopping the thread and maintain the security without much damage.

Threats to the new systems.

The new systems study customer data to make algorithms. This training data is huge as security is an important aspect and cannot be played with. The attackers can find out this data and chalk out the security algorithm. They can also change the training data.

If the security systems can use Machine Learning to make security the attacker can use the same to break too. As in Harry Potter, solving the problem with magic definitely seemed to be the smart and easy solution to the muggle prime minister. But the problem was, both the sides knew magic!!!

Conclusion

The time is not far when these systems become more efficient. But there isn’t also a long time when there would be better ways to break in. AI Vs hacking isn’t a race to finish, it is a process that will keep on improving with advancements. All we can wish is that the balance always tilts in favor of the good.

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