M.C.T.I.S

Project Title

M.C.T.I.S

Project Area of Specialization Cyber SecurityProject Summary

Utilizing artificial intelligence and machine learning to apply threat and data intelligence strengthens an enterprise’s security by empowering stakeholders with evidential information on what and how cyber threats are relevant to their business. Adopting AI/ML to predict and stop data breaches requires a holistic, organization-wide threat intelligence strategy that is fully-integrated in the organizational security management framework. This makes it possible to find the needle in the haystack before it pricks you.

Project Objectives

To design a machine learning model for threat intelligence which can dynamically adopt environment to automate data collection and processing, integrate with your existing solutions, take in unstructured data from disparate sources, and then connect the dots by providing context on indicators of compromise (IoCs) and the tactics, techniques, and procedures (TTPs) of threat actors. Utilizing artificial intelligence and machine learning to apply threat and data intelligence strengthens an enterprise’s security by empowering stakeholders with evidential information on what and how cyber threats are relevant to their business. Adopting AI/ML to predict and stop data breaches requires a holistic, organization-wide threat intelligence strategy that is fully-integrated in the organizational security management framework. This makes it possible to find the needle in the haystack before it pricks you.

Project Implementation Method

Overview of Method The threat modelling method proposed in this guide comprises broadly the following 4 steps: i Step 1 – Scope Definition, which involves gathering information and demarcating perimeter boundary; ii Step 2 – System Decomposition, which involves identifying system components, drawing how data flows, and dividing out trust boundaries; iii Step 3 – Threat Identification, which involves identifying threat vectors and listing threat events; and iv Step 4 – Attack Modelling, which involves mapping sequence of attack, describing tactics, techniques and procedures

Benefits of the Project

Main motivation for adopting AI/ML for cybersecurity is an increasing complexity of IT threats. Before, businesses had to face “typical threats” like Zeus trojans, but as we learned to handle those, new species emerged. Recently, we faced Ryuk ransomware, smart botnets, and an evolved trojan, Trickbot. In medium and large organizations where hundreds of systems operating simultaneously which also generates catastrophic level of cyber threats, the problem(scale of data) of concept drift is exacerbated, and a static ML model might fail in a dynamic environment. The inability to account for a dynamic and adversarial nature creates a new class of ML risks.

Technical Details of Final Deliverable

We proposed model which can enhance the cyber security of organization so it is software based.

Final Deliverable of the Project Software SystemCore Industry ITOther Industries Finance , Media , Security , Telecommunication Core Technology Artificial Intelligence(AI)Other TechnologiesSustainable Development Goals Partnerships to achieve the GoalRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	80000
intel i7-7700k	Equipment	1	35000	35000
Nvidia GTX 1050 TI	Equipment	1	35000	35000
16 GB ram	Miscellaneous	1	10000	10000