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Stop Neglecting Start Monitoring the Network traffic.

We all know that the current IDPs drop down the malicious packets, blocks the offending IPs, and alert the admins about the potential threats. But are those systems are capable of recognizing suspicious activities, potential threats/attacks? If yes then why enterprises are getting victimized by these cyber attacks? All those systems are capable of recognizing the cyber-attacks and many of those systems use predefined databases for recognition and can be programmed to recognize attacks based on the traffic and behavioral anomalies. While being effective at blocking known attack vectors, some systems come with limitations. These are commonly caused by an overreliance on predefined rules, late responses, unbalanced datasets making them susceptible to false positive and false negative errors.


These systems can identify the existing attack patterns as per predefined datasets because current systems are designed as per heuristic rules, named signatures, to detect intrusions in a network environment that works only on the predefined database those systems can be bypassed using the modified and new attacks patterns by customizing the payloads. Because of these custom payloads which do not match any predefined datasets, it's near about got impossible to identify the new attack techniques. The current systems are designed to identify the existing attacks which are listed and known to everyone but how we are going to tackle those unidentified new and modified payloads. As per the research, we found that globally, 30,000 websites are hacked daily. There were 20M breached records in March 2021. In 2020, ransomware cases grew by 150%. Every 39 seconds, there is a new attack somewhere on the web. Email is responsible for around 94% of all malware.95% of attacks are carried out because of manual errors.


I observed that "many of the enterprise the system administrator deployed standard security products like firewalls, intrusion detection systems, or stronger authentication devices such as time-based tokens or biometric smart cards. But those things can be exploited in a much easier way by custom payload using the current weaknesses in unbalanced datasets, customized payloads, as well as late response time, which is the biggest problem in the current systems."


Now the question is how can we recognize those customized attack patterns? We found that the usage of big data, machine learning, and artificial intelligence used to identify investigate and neutralize the new modified attacks and also the existing ones by sophisticated data modeling online learning based Least squares support vector machines (LSSVM) can address those modeling problems of a time-consuming process. these can predict malicious activities through the collaboration of MBPSO and LSSVM by which the smart service can be used to create a balanced dataset that can compare the datasets using the execution of data present in the data packets so that it can differ all those suspicious as well as normal behaviors of the network traffic and secure the network more effectively.

Artificial intelligence and Machine learning algorithms are capable of identifying unknown threats and newly implemented cyber attacks in real-time also learn continuously according to the behaviors of users and data packets flowing on the network.

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