Artificial Intelligence has transformed the way cybersecurity is approached, furthering threat detection and response times, including big data analysis.
Some of the main AI concepts reviewed in the paper include Expert Systems, Artificial Neural Networks, Bayesian Filtering, and Genetic Algorithms; real-life examples and scenarios of each are presented in some detail.
Expert Systems
Expert Systems: AI programs, which emulate the decision-making capability of a human expert in solving specific problems of a domain by using a knowledge base and inference rules.
Example: Intrusion detection in cybersecurity is one of the applications of an expert system. It works on the principle of analyzing patterns of normal behaviour and matching them against real-time data to decide upon a potential breach in security. Suppose the system notices that there are several login attempts originating from different geographical locations within a short duration; the system automatically trips alerts for investigation into the same.
Artificial Neural Networks (ANNs)
Artificial Neural Networks are computational models inspired by the structure of the human brain. They consist of interconnected nodes or neurons arranged in layers that process data through weighted connections. Some of the key characteristics of ANNs include the following:
- Input Layer: The layer that receives the raw input.
- Hidden Layers: These are the intermediate layers performing computations and feature extraction.
- Output Layer: It forms the last layer, which produces the final result or prediction.
Training an ANN involves adjusting weights in view of input data to minimize errors in the prediction, usually using techniques such as backpropagation.
Example: ANN applications are very common in anomaly detection in cybersecurity. A financial institution may implement an ANN to examine transaction patterns. When the network picks up transactions that are far different from established patterns, such as unusually large withdrawals or transfers, it may flag these transactions as possible fraudulent attempts.
Bayesian Filtering
Bayesian Filtering is a statistical technique for classifying data based on the probabilities. Bayes’s theorem is one that updates the probability of a hypothesis given more evidence. In cybersecurity, Bayesian filtering can be found being used actively in spam detection systems. A filter would classify incoming emails against the previously learned characteristic features of spam versus legitimate messages.
Example: The application of Bayesian filtering in an email service provider would be to block spam emails. The system works by one analyzing the contents of an incoming message and running them against a database of known spam characteristics to then assign a probability score to each email. If the score is above a set threshold, then the message is labeled as spam and delivered appropriately.
Genetic Algorithms and Programming
Generally speaking, GAs are the methods of optimization impelled by natural selection. They utilize mechanisms such as selection, crossover, mutation, and inheritance to evolve solutions to complex problems over successive generations. In cybersecurity, GAs may be used to find an optimal set of security protocols or configurations by evaluating a number of alternative solutions and selecting the best among them.
For instance, genetic algorithms can be applied in firewall rules optimization by a cybersecurity team. Using GAs to simulate multiple configurations and test for performance against different attack vectors can provide the most robust set of rules that minimize vulnerabilities while maximizing performance.
Conclusion
Artificial Intelligence, therefore, greatly enhances the capabilities of cybersecurity-related issues such as quicker threat detection, auto-responses, and decision-making processes. Expert systems, artificial neural networks, Bayesian filtering, and genetic algorithms are some topics that would enormously equip cybersecurity people with diverse tools against fighting evolving threats.