Intrusion Detection with Machine Learning & Artificial Intelligence (ML & AI) Techniques to Reduce Cyberattacks (Network Traffic) (New Way to Improve Cybersecurity)

Cybersecurity plays an important role in the field of Information Technology (IT). Securing information becomes one of the biggest challenges in the present day. Whenever we think about cybersecurity the first thing that comes to our mind is cybercrime which is increasing immensely day by day. As we know that billions of people affected by breaches for many years, government agencies and businesses are spending more time and money defending against it. In the existing scenario, many cybersecurity systems use DIDS (Distributed Intrusion Detection Sensor/systems) that allows a limited trained analyst (i. e. , CSA/CTIA) to monitor several networks at the same time. However, this approach requires data to be transmitted from DIDS on the defended network to Central Analysis Server (CAS). Transmitting all the data captured by DID sensors and send summaries of activities to reportback to a security analyst (CSA/CTIA). With only summaries report, cyber-attacks can go undetected because the analyst (CSA/CTIA) did not have enough information to understand the network activity. In this proposed research we mainly focus on to identifying a new way to improve cybersecurity and toreduce cyber-attacks for which we proposed to design a Scalable Distributed Intrusion Detection System (DIDS) is in Artificial Intelligence& Machine Learning (AI& ML) techniques (i. e. Classifiers& Lossless compression) that gives the security analyst (CSA/CTIA) a quicker, easier, more efficient method to identify attacks across multiple network segments by compressing the network traffic, and also to trace back the activities of the attacker. The DIDS is in AI& ML techniques that provide better facilitation of advance network monitoring, incident analysis, and instant attacks data across multiple network segments and as a result, providesa real-time accurate analysis reportfor early detection of malicious activities and instant attacks. The DIDS system gives the analyst (CSA/CTIA) a complete real-time accurate analysis of activities reports, it allows the analyst much more flexibility in discovering attack patterns. And to capture all the transmitting data by sensors required too much bandwidth, keeping in view of this we propose to increase the bandwidth of network to improve the data rate flow of network traffic. For which it is easy to reduce the cyber-attacks on the network and save a lot of time and money.