How Artificial Intelligence and Machine Learning forbid cyber attacks? AI and ML might become new paradigms for automation in cybersecurity.

Machine Learning and Artificial Intelligence


How Artificial Intelligence and Machine Learning forbid cyber attacks?

AI and ML might become new paradigms for automation in cybersecurity. They allow predictive analytics to draw statistical inferences to reduce threats with fewer resources. Applications for automated network security include self-encrypting and self-healing drives to secure data and applications.

In the current world of data deluge, it is not possible for humans alone to analyze the billions of logs generated from the existing infrastructure components. Integrating AI into the existing systems including Security Monitoring Solutions, SIEM, Intrusion Detection Systems, Cryptographic technologies and Video vigilance systems may help in addressing many of these challenges to a great extent. Application of AI-based technologies into the prevailing systems will bring in much-enhanced systems that aid in better decision making.

Functionalities and types of AI Applications

The functionalities of AI make a considerable impact in key areas such as Data Mining, Pattern recognition, analytics, Fuzzy logic, development of expert systems, and fraud detection.

Some of the types of AI applications being used in cybersecurity solutions are Spam Filter Applications (spam assassin), Network Intrusion Detection and Prevention, Fraud detection, Credit scoring and next-best offers, Bonnet Detection, Secure User Authentication, Cybersecurity Ratings, Hacking Incident Forecasting

Working in Machine Learning?

Machine learning is an approach to the science of artificial intelligence. Some aspects that set Machine learning unique from other types of programming are the capabilities to learn from huge amounts of data using algorithms constructed by humans to accomplish tasks. The algorithms enable them to learn and adapt to new data so that the machine can think and act more like a human.

The algorithms that are used can be grouped under:

Supervised learning — the computer is provided with parameters or examples to compare with the data inputs.

Unsupervised learning — the computer is fed data and checks the relationships between the data by itself.

Limitations of Machine Learning

Once a Machine Learning solution is implemented, we need to ensure that we are detecting the exact thing. On top of that, testing and debugging is complicated, as we need to deal with a lot of uncertainties.

Costs of acquisition, operation, and maintenance are high. Also generally related to the highly specialized, scarce, and expensive expertise required.

The impact of regulatory Artificial Intelligence Cybersecurity frameworks might be varied, involving privacy, data protection and other regulations affects automated decision making.

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