AI Tools for Fighting Cybercrime

By: Dhanush Reddy Chinthaparthy Reddy, Department of Computer Science and Artificial Intelligence, Madanapalle Institute of Technology and Science, Angallu(517325), Andhra Pradesh

Abstract

With more and various types of cyber threats becoming more complex and frequent in nature, the application of counter tactics is highly necessary. Artificial Intelligence (AI) solution has become more influential in the cybersecurity areas and provided the new approaches for early threats perception as well as threat control means. From the following paper, the different AI tools used in cybersecurity and their functions in improving security measures and avoiding threats are discussed. The paper examines the main areas in protecting security by AI, including threat identification, the analysis of behavioral patterns, and the capability of automatic actions against threats which are typically performed by the use of machine learning algorithms and neural networks. Additionally, possibilities for the interaction of AI with the existing approaches and frameworks in security are considered, which can give an idea of how AI can support human actions and fill the gaps of the present protection measures. The issues arising from the use of AI in cybersecurity such as vulnerabilities of AI, the adversarial attacks and the learning patterns are also discussed. Finally, the paper assesses the opportunities that AI has for the future of cybersecurity with the propose several paradigms for further research to help create a defence mechanism that is, reliable, resilient, proactive, and intelligent enough to counter the dynamic and fluid nature of threats in the cyber domain.

Keywords: Cyber Crime, Artificial Intelligence, Cyber Attacks

Introduction

Cyber security has emerged as a major consideration for organizations globally given the increased rates and magnitude of cyber-attacks on entities’ data. In a Norton’s records, average cost of data breach globally is at $C. 86 million, it is understood that organizations need 196 days to fully recover the outlay. These longer periods of recovery and thus, the considerable losses are indicative of the necessity for better solutions. Artificial intelligence and Machine learning or AI & ML have come out as a powerful technology to counter cyber threats and are also used to identify the threats, prevent them and minimizes the risk of cyber threats.

AI benefits cybersecurity analysis by allowing such systems to improve from previous actions and data analysis in identifying future threats and ways to counteract them. For example, AI can improve the availability of timely incident response and assist organizations in baselining their security. Existing cybersecurity tools are rather efficient, but many of them only use keywords and require additional analysis by analysts, which often takes a lot of time and is prone to mistakes. However, with the help of AI integration, such processes can be improved as well as automated and enable the efficient handling of large data.

Also, AI has a significant impact on the increased activity of cybercriminals who are always coming up with new ways of getting round classical security methods. Security software and systems based on AI are capable of processing information about tens of millions of cyber events, including potentially dangerous actions like users’ abuses or appearance of new types of viruses. AI in turn may utilize great volumes of identified threats and immediately distinguish new attacks, even if they are modifications of previous ones. This is because while the attackers are always devising new ways to breach these security measures the defenders too are constantly working to strengthen and fortify their measures.

Thus, the purpose of this paper is to analyse the use of AI tools in cybersphere with a focus on its influence on public safety, nation security and the continual war against cyber threats.[1]

AI Tools in Cybersecurity

The modern society is characteristic of the digital revolution which has touched virtually every facet of life specially where it concerns the people, organizations and societies. However, it is equally important to state that with this phenomenon of digital shift, there are numerous perceived advantages but the constant threats of cybercrimes are not only frequent but also escalating. In this ever growing and developing digital world there are bound to be advancements in these adversaries’ strategies and tools as well. As a result, protection from cyber crimes over the virtual assets and infrastructure has become a necessity for the individuals, organizations, as well as for the governments. Responding to the versatility of threats that are associated with cyber threats requires more than the conventional strategies in combating risks because digital risks are fluid.

In this regard, different AI tools have come out as the critical technologies that present unprecedented enhancements in cybersecurity. These tools include all the state-of-art methodologies and the algorithms that help the machines to mimic human intelligence such as learning from data, making decision and inference, and learning from new knowledge. Application of AI in cybersecurity is considered to be a fascinating approach to improve threat identification, prevent and protect from a multitude of threats in the sphere of digital security.[2]

Machine Learning Algorithms

In the context of the development of ML models, a significant aspect is labels, defined as the target value of the prediction function on a sample, for instance, as distinguishing between benign or malicious. Based on the presence or absence of labels, the ML techniques used here are categorized into two, namely Supervised learning and unsupervised learning. Supervised method on the other hand can only be used when the training data is labeled although this may be done automatically or may go through a process to get the labels. On the other hand, being also classified as the learning with minimal supervision, unsupervised methods do not presuppose the usage of labels at all or presuppose the limitation of their usage. For instance, in reinforcement learning the data for the model is acquired through automated feedback.

There is also another way of categorizing the ML methods, based on the depths of learning, which brings us to the last two categories on the list[3]. , deep learning are the ML methods founded on neural networks and usually demand greater processing power and large training sets than shallow ML methods – prerequisites only feasible in the recent years. But, it is paradoxical to conclude that deep learning is always superior to shallow ML[4]. Thus, if the number of features in the analyzed data is relatively low, shallow ML can honestly compete with deep learning, although the latter requires significantly more resources and the results obtained using it are much more difficult to interpret. It can be noted that the primary benefit of deep learning is in the cases when a shallow ML model is insufficient due to complexity of the input data – the format that is mediating data—images, text (unstructured), or data with temporal interdependence. The deep learning methods can be of either the supervised type or the unsupervised type and can also integrate reinforcement learning , as is witnessed with generative adversarial networks[5].

Conclusion

In this context, the incorporation of AI tools into cybersecurity can be classified as a major progress in the pursued fight against cyber threats. These tools also employ the machine learning, deep learning, and other Artificial Intelligence based techniques providing the capabilities to detect, prevent and counteract the more sophisticated and emerging kinds of attacks. The capacity of AI in real-time processing and analyzing large data sets for generating patterns and outliers and, on the other hand, providing automated threat responses helps organizations to fight them.

However, the use of AI in cybersecurity comes with its own troubles. Issues like data privacy and social rights and fairness of the algorithm for recommending particular item have to be delicately handled so that the usage of such technologies has to be done in a sensible as well as efficient manner. Also, the AI systems are complex, which requires continuous research and work on them with the aim of overcoming of existing drawbacks and improving of their stability.

In conclusion, AI tools seem to be a powerful complex layer that can efficiently help to counter the existing threats and become the foundation for changing the effectiveness of cybersecurity. With emerging trends of the threats in cyberspace, the use of artificial intelligence in the protection of information assets and preserving the integrity of cyberspace will be essential. These technologies must be adapted and implemented in by organisations while at the same time the organisations have to be cautious about the ethics and realities of using these technologies. Thus, with equal emphasis on technology and people, AI can take a central position to protect the digital environment.

Reference

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5. M. Rahaman, L. Triyono, – Prayitno, – Sukamto, and A. Yobioktabera, “Smartphone-based Indoor Navigation for Guidance in Finding Location Buildings Using Measured WiFi-RSSI,” JOIV Int. J. Inform. Vis., vol. 6, no. 4, pp. 829–834, Dec. 2022, doi: 10.30630/joiv.6.4.1528.
6. Gaurav, A., Gupta, B. B., Chui, K. T., Arya, V., & Wu, J. (2024, June). Enhancing Intrusion Detection in Software Defined Networks with Optimized Feature Selection and Logistic Regression. In 2024 IEEE International Conference on Communications Workshops (ICC Workshops) (pp. 1809-1815). IEEE.
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Cite As

Reddy D.R.C. (2024) AI Tools for Fighting Cybercrime, Insights2Techinfo, pp.1

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