Cloud Security Modeling: Using TCP Deltas with Data Analytics and Machine Learning Techniques

The 21st-century surge in computing infrastructure across mobile, centralized, and decentralized realms in the cloud has ushered in a new era of diverse applications, along with its Nemesis the Cybercrime. However, this progress comes hand in hand with amplified security risks, such as Ransomware Attacks. Security measures such as Multi-Factor Authentication, Strong Passwords, Network Segmentation, Endpoint Security, Robust Firewall and Intrusion Detection systems, and user awareness are on the Rise. Still, these efforts fall short when endpoint security fails, or a user is masqueraded to hack the system for ransomware. Essential security measures, including resource-intensive static and dynamic scans, strain these infrastructures, leading to a noticeable lag in user experience. This study delves into how to use Data Analytics/ Machine Learning (ML) techniques with TCP/IP 3-way handshake data to continuously monitor the TCP.Delta times at the server side of the load balancer to flag suspicious activities, along with measuring user lag in accessing these resources which are under security scans. The CI/CD pipeline will be used to provide Q's about abnormal load balancer traffic along with measuring lag in the response time using a generic methodology that revolves around TCP.Delta time and advanced Data Analytics/ML techniques.