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Why Artificial Intelligence will be the Future of Cybersecurity?

In cybersecurity, Artificial Intelligence (AI) is implemented through Machine Learning techniques. The Machine Learning algorithms allow computers to learn and make predictions based on data already known.

When we talk about Artificial Intelligence, people often refer to a world dominated by robots in a science fiction universe. But Artificial Intelligence is rooted in reality and is already used in many fields, such as online shopping, surveillance systems and many others.

Why use Artificial Intelligence for cybersecurity?

Artificial Intelligence has already proven effective in the treatment of millions of malware every day. Security analysts must examine well over 400,000 new malicious programs detected each day, according to statistics from the independent testing organization AV-Test.

Traditional detection methods (the signature-based antivirus solutions) unfortunately do not allow, in most cases, provides proactive protection. In addition, nowadays security vendors must also manage third-party specialized services that provide obfuscation mechanisms to help conceal malware from traditional antivirus solutions.

How to apply Artificial Intelligence for cybersecurity?

Security vendors regularly look for ways to implement Artificial Intelligence technology to their cybersecurity tools. For example, Bitdefender, an internet security software company, began to incorporate Machine Learning technologies in its detection systems seven years ago.

The three main pillars of Machine Learning are:

  • Data mining permitted and justified by the amount and variety of data produced today, potentially collected and available.
  • Pattern recognition allowing in particular to forge links between the data collected and highlight patterns.
  • Neural computer as additional analysis means, inspired by biological neural networks, such as the brain.

When using Machine Learning technologies, clustering and classification algorithms are used to respond correctly and quickly to the crucial question on whether the file is it healthy or malicious. For example, if a million files must be analyzed, the samples can be divided into small groups (called clusters) where each file is similar to the others, and then the security analysts only need to scan a file in each group and apply the results to others.

More importantly, Machine Learning provides particularly high detection rate for new malware in circulation.

The fundamental principle of Machine Learning is to recognize “patterns” that emerge from experience and make predictions accordingly. This means that security solutions can react to new cyber threats, even unknown, faster than automated cyber-attack detection systems used hitherto. This technology is also suited to fight against sophisticated attacks such as APT (Advanced Persistent Threat), which often orchestrated by a person or persons targeting a specific entity.

Should machine totally replace man?

Blurring the boundaries between man and machine, Artificial Intelligence is a particularly interesting cyber-weapon, but it cannot take sole charge of all of the fight against cyber threats. The Machine Learning systems can include generating false positives and human intervention is necessary to revise these algorithms with the corrected data.

Machine Learning algorithms are, overall, more accurate in assessing potential threats within large quantities of intelligence data as their human counterparts. They also know how to spot intruders faster.

A hybrid approach, where Machine Learning is supervised by human analysts offers the best results to date.

Conclusions

The applications of Machine Learning have been proven successfully in many areas. These applications will continue to make Machine Learning trendy and interesting for the coming years.

Regarding the future of AI, it is almost impossible to predict the future. However, in the years to come, Machine Learning will most likely focus on the creation of specific profiles for each user. If an action or a user’s behaviour does not match the predefined templates, the latter will be informed. For example, an unusual peak downloads achieved in a short period of time will be marked as suspect and analyzed more closely by a human expert.

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