Machine Learning for Cybersecurity 101

Cybersecurity

Future research has found that Cybersecurity is directed at human behavioral and emotional factors. Out of this discovery, questions arise.

Why do people really commit cybercrime? Is it out of greed, power, negligence, fear, pressure? What are the motives and intentions of these acts? Trends in global war are evolving from nukes and battleships to cyberattacks. No one is safe anymore! What drives people to carry out these acts?

It is important to note that most cyberattacks are often results of insider negligence, information or actions. After all, Bruce Schneier once quoted:

Security is only as good as the weakest link, and people are the weakest link in the chain.

It is almost impossible to create a perfect protective system. Still, the concept of Artificial Intelligence (AI) and Machine Learning (ML) is providing hope that cybercrime can be avoided and prevented. Machine Learning is the application of data (trained and untrained)in analyzing complex processes of pattern recognition for smart decision making. Pattern recognition employs supervised (trained data) and unsupervised (untrained data) approach in identifying patterns in data.

Artificial Intelligence

Applying AI, or Artificial Intelligence, to Cybersecurity provides security analysts with proactive steps in predicting, preventing and responding to intrusions and attacks. There is currently a section of AI that simulates human emotions and security behaviors majorly concerned with machines making decisions like humans.

Cognitive Computing

Cognitive Computing (CC) and Cognitive Security is the next big thing in AI. This simulates a human’s emotions and thought processes. This type of security involves simulating human emotion. It is particularly significant in predicting and preventing cybercrime. It provides security professionals with enough information required in analyzing security risk from the human behavioral context around security operations.

 AI uses cognitive methods (behavioral, psychological and emotional factors) such as security analytics, malware analysis, and correlation analysis. This allows the AI to gain insights on security anomalies and threats in addition to protecting these systems against attacks. Significant benefits of Cognitive Security involve improved threat detection, process automation, proactive prevention, improved incident response, and more accurate algorithms.

Conclusion

With the advent of AI and ML, one can say there is hope in combating cybercrime. The future of cybersecurity seems bright with AI. It has shown to have the ability to learn independently and devise the best strategies and responses to cyberattacks.

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