TECHNIQUES IN ARTIFICIAL INTELLIGENCE FOR IMPROVING CYBER SECURITY 

In this era of digitization with the rapid growth of IOT and linked devices, experts of cyber security face a lot of challenges. They need all the help to prevent attacks and respond to unauthorized access. The rapid increase in cyber security threats facing global businesses can be controlled by the integration of Artificial Intelligence into cyber security systems. Machine learning and Artificial Intelligence (AI) are being connected more extensively over industries and applications as the computing power, storage capacities and data collection increase. With machine learning and AI, that intensive amount of data could be broken down in fraction of time, helping the industry to identify and recover from the security threat. 

Security professionals tried to fix the flow of information. But the two fields have grown closer over the time, when the attacks have targeted to simulate the performance, not only at the human user level but also at lower system levels. CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is a very good example of connection of AI & Cyber Security. Improvements in automatic character recognition software, which can be considered to be a huge leap in AI technology, could take the field towards more refined pattern recognition. Artificial Intelligence systems that are intended to learn and adapt, and are capable of recognizing even the smallest changes in the settings, have the capability to act much earlier towards the cyber attacks. 

Techniques In Artificial Intelligence (AI) For Cyber Security 

1. Expert Systems  - An Expert System is a computer system that imitates the decision making trait of a human and can be considered as the best example of Knowledge based system. These systems are composed of two sub-systems: the Knowledge Base and the Inference Engine. 
2. Neural Nets - Neural Nets is also called deep learning. It is an advanced branch of AI and takes inspiration from the working of the human brain. When we implement the deep learning to cyber security, the system can identify whether a file is dangerous or legitimate without human interference. This technique generates strong results in detecting the malicious threats, compared with classical machine learning systems. The victory of neural nets in cyber security is their speed. Neural nets can define the exact detection of new suspicious threats and fill in the dangerous loop holes that leave industries wide-open to attacks. 
3. Intelligent Agents - Intelligent Agent (IA) is self-sufficient entity which may gradually learn or use their knowledge base to achieve their objectives. They can be either extremely simple or very complex. ALEXA and SIRI are the examples of Intelligent Agents. They can adapt to real time conditions, has capabilities to learn new things rapidly through communication with external environment, and also consist of memory base and recovery abilities. 

In the current scenario where threats and cyber-attacks increase with every minute, the need of intelligent security system is essential. Artificial Intelligence techniques are easily adaptable yet hard to secure than contemporary cyber security solutions. Therefore AI is needed to increase security implementations and defend system from a growing number of advanced and complex cyber threats in a more effective manner. For this, RBMI, Bareilly continuously put its efforts to educate for the same by conducting the workshops, taking the classes and seminars and also by implementing in the academic structure too. With this, we thus aim that in future we may have more intelligent systems than what we have currently. 

SIDDHARTH PANDEY (ASST. PROFESSOR, RBMI-BAREILLY) 

SWATI JHA (ASST. PROFESSOR, RBMI-BAREILLY)