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7 AI solutions for Network security threats

Networking is an element of technology that will undoubtedly grow in importance throughout time, yet everything has advantages and disadvantages. As a result, numerous network security methods have been developed to address the drawbacks. These have aided us in maintaining network security and privacy to some extent. However, there are also concerns about future technological advancements.

7 AI Solutions for Network Security Threats
7 AI Solutions for Network Security Threats

The future scope is to combat these concerns by adding new methodologies researched and invented by some researchers and network security-based start-ups, including AI, which will be the future trend for tackling a lot of upcoming technology difficulties.

Some of the AI solutions for Network security threats are discussed below:

1) Malware Solution

Malware is harmful software that directly or indirectly impacts a computer system, with "94 % of all malicious executables being polymorphic," according to a Webroot 2018 study. When polymorphic malware spreads or is distributed, it automatically recodes itself. Signatures or heuristics are used in a lot of malware detection technology.

Cyber security in Artificial intelligence
Number of malware increasing over years

Regardless of what changes around it, signature detection systems locate exactly the same bit of malware. This method aids in the detection of a wide range of malware strains.

Furthermore, some of them are only detected using heuristics detection engines because they need a large number of resources that cannot be employed on a large scale.

2) Ransomware Solution

University of Kent research students published a paper about a predictive model called Randep, which is a machine learning-based model that provides information on finding and identifying behavioural patterns for improved ransomware detection and the response of 18 ransomware families.

Cyber security and AI
Cyber security and AI

3) DDoS Attack Solution

A variety of solutions for countering DDoS attacks are being developed, including signature or anomaly-based detection, network intrusion detection tool-SNORT, and many other approaches for distinguishing genuine from malicious traffic. If the amount of traffic is excessive, distributed computing can be used.

4) Solution for IoT - Related Treats

Many firms, like Cisco, Hitachi, Huawei, and others, are creating their solution products in different ways.

At the network and service provider level, securing IoT devices involves both protection and privacy. For IoT networks, a DDoS detection approach based on ANN is deployed. This method is based on the distinction between genuine and malicious traffic patterns.

The proposed system has been modelled and tested to achieve a detection accuracy of above 99%. Also, developing and executing regulations to address the IoT device's privacy and security problems throughout its lifecycle.

5) Phishing Solution

It's critical to set up adequate access management, which means that only staff who have a complete understanding of the system should have access to accounts. In addition, as AI and machine learning become more prevalent, businesses are developing new phishing detection algorithms to better their bottom lines.

6) Man-In-The-Middle Attack Solution

SSL/TLS encryption protocols are the most prevalent way for controlling MITM; this method uses a key that is encrypted and decrypted at the sender and receiver ends, respectively. However, because they use trusted third parties that may or may not be real, the hacker can still discover the communication between the two by knowing the sender and receiver.

As a result, several communication channels began to be employed instead of a single third party. A virtual private network is a novel method that has emerged (VPN). It encrypts the user's internet connection to hide it from hackers, making it impossible to decode even if it is intercepted. Furthermore, the internet speed remains unaffected.

7) SQL Injection Solution

Various solutions to solve SQL injection are now based on machine learning and AI, and one of the most efficient approaches to handle this danger was provided in a research paper by a San Jose University student.

In this case, the Gradient Boosting Classifier algorithm is utilised to classify incoming traffic based on a set of parameters. The accuracy of getting desired solutions improves to 97.4% using this technique. Despite this, SQL injection attacks can be mitigated using various network neural approaches.

Top technology investment in 2019

Because the world is getting digital at a rapid speed, invaders are becoming smarter by the day. Today, every field and organisation are trusting cybersecurity based on machine learning and artificial intelligence. To counter cyber-attacks, everyone requires a flexible platform that can be updated and adapted to meet current needs.

These AI-powered products and solutions will keep bad actors on their toes, allowing the IT industry to breathe easier.

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