By: Dhanush Reddy Chinthaparthy Reddy, Department of Computer Science and Artificial Intelligence, Madanapalle Institute of Technology and Science, Angallu(517325), Andhra Pradesh
ABSTRACT
AI plays the role of defining several sophisticated tools and techniques to safeguard the information assets against new and strong forms of cyber threats. This article also offers a general background of Real AI in the context of cyber defence and pinpoint the primary machines learning/Natural Language Processing and anomalous detection in the cyber defence framework. Concerning these technologies, it explains how they can be employed for boosting identification of threats, handling of incidents and management of vulnerability. Furthermore, apart from the specific proposed measures for the AI use in cybersecurity, the article designs the topics that are associated with it, including data quality, adversarial attacks, as well as interpretability of the model. As pointed out during the study, it can be evidenced that an ideal balance can be achieved in the execution of the reinforcement of the already set defence structures through embedding of AI and continued reinforcement of the structures by human personnel. Such direction for the future developments is proposed as: the use of the modern AI techniques, the definition of all the ethical concerns, the determination of the right measures for the application of the AI in the sphere of the cyber security. This has in turn underlined aspect of the fourth industrial revolution using artificial intelligence in enhancing revolutions in cybersecurity and consolidating the defences against other higher classes of cyber threats.
INTRODUCTION
The emergence of Smart Industry 4. 0 has seen many improvements especially in the manufacturing sector using technology in automating, increasing, and improving productivity. However, with such advancements in technology, there arises new dilemmas especially concerning cyber security. The rising trend in dependency of Smart Industry 4 devices and systems interconnection is another factor. 0 environments make them prone to cyber threats and security incidences. As a result of these challenges, a focus on the connection between AI and cybersecurity in Smart Industry 4. 0 applications. AI can involve the use of sensors, and other devices within the industrial setting to capture massive data and perform analysis with a view of identifying cyber threats. It is possible to train the machine learning algorithms to look for the normal behavioural patterns suggesting that anything deviant from the norm is a security threat. Cybersecurity techniques are of various types based on AI, which may include anomaly detection, intrusion detection, and predictive analysis. These techniques can be integrated with other solutions of cybersecurity as firewall, IDS, and security information and event management (SIEM) to help protect Smart Industry 4. 0 applications. Security issues and AI in Smart Industry 4. 0 applications can play an important role in enhancing the level of security in the industrial contexts and decreasing the probability of cyber threats and data leakage. But it must be noted that these technologies have to be properly implemented and configured for them not to cause other unwanted issues and/ or open new threat exposures. Thus, analysing and creating proofs with AI applications to prevent cyber threats and ensuring the stability and security of industrial climates are possible.[1]
AI Techniques in Cybersecurity
Machine Learning
It is noteworthy to state that ML and DL are pivotal AI techniques that have revolutionized threat detection in cybersecurity. Below is a detailed discussion of these techniques and their applications in threat detection: Machine Learning (ML) There are different machine learning techniques, as described below
• Supervised Learning: In supervised learning, AI systems are trained on labelled datasets, where each data point is associated with a specific class or category (e.g., malicious or benign). Through iterative training iterations, the system learns to map input data to the correct output labels, enabling it to classify new, unseen data accurately .
• Unsupervised Learning: Unsupervised learning involves training AI systems on unlabelled datasets, where the system aims to identify patterns or structures within the data without explicit guidance . This technique is particularly useful for anomaly detection, as it can uncover deviations from normal behaviour without knowing what constitutes an anomaly.
• Reinforcement Learning: Reinforcement learning is a trial-and-error-based learning paradigm where AI systems learn to make decisions through interaction with an environment .
Deep Learning
DL is a subset of ML that employs artificial neural networks with multiple layers of interconnected nodes known as neurons. These deep neural networks (DNNs) can automatically learn hierarchical representations of data, enabling them to extract intricate patterns and features from raw input data. The different types of DL are described thus:
• Convolutional Neural Networks (CNNs): CNNs are a deep neural network specifically designed to process structured grid-like data, such as images or time-series data . In threat detection, CNNs can be applied to analyse network traffic patterns or identify malicious patterns in malware binaries.
• Recurrent Neural Networks (RNNs): RNNs are well-suited for processing sequential data, making them ideal for tasks such as natural language processing (NLP) and time-series analysis ,. In cybersecurity, RNNs can be used to analyse log data or detect anomalies in system behaviour over time.
• Generative Adversarial Networks (GANs): GANs consist of two neural networks – a generator and a discriminator – that are trained simultaneously in a competitive manner . GANs have applications in generating synthetic data for augmenting training datasets or generating adversarial examples for testing the robustness of threat detection systems.
• By leveraging these AI techniques, threat detection systems can continuously adapt to evolving threats and identify previously unseen attack vectors with high accuracy. Moreover, ML and DL algorithms’ inherent flexibility and adaptability enable threat detection systems to detect complex and sophisticated threats
Technique | Description | Application |
Supervised Learning (ML) | Transforms labelled data to give better accuracy | Finds the threats and malware |
Unsupervised Learning | Identifies the patterns involved in the data | Finds the unknown threats and new behaviour |
Reinforcement Learning | Learns by trial and error for better optimization | Learns new strategies and new techniques |
Convolutional Neural Network | Analyses structured data and images | Identifies the network traffics and malware detection |
Recurrent Neural Network | Process the sequential and log data | Finds the anomalies in the given system |
[2]
Key Applications of AI in Cybersecurity
ANNs are rather complex concepts for data processing in a computer architecture based on the structure of the human brain. Depositions in the mind are in the form of dates signal while the ANNs have linked Nodes or processing elements that work on numeric Values to convert inputs into the output.
If ANNs manages to penetrate in the IDPS for the purpose of recognizing the traffic of the network, then the conventional methods for intrusion detection will be solved. Since they are fast, flexible, and capable, they are least effective where there is more than one attacker, and also in learning previously identified attack signatures in a matter of minutes rather than in first training samples though not perfect. Remember that, ANN is based on information and prior attacks episodes; thus it is ready for uncovering the new structured attack pattern being exhibited without being produced false-negative and false-positive[3].
Thus, one can assume that as regards IDS designed in terms of ANNs, there is a certain degree of their effectiveness and discretion, as well as their possibilities to adapt[4]. This kind of method can estimate new attacks with or without clear and specific rules to distinguish them from normal methods or without employing people. Based on the outcomes of the tests that have been run on the neural network, it would be reasonable to assume that by degrees, the task of setting attacks on computer systems would be quite conceivable. The examples are as follows: Nevertheless, there are some criticalities that should be considered about the use of AI systems for foreseen cyber-defence missions: security and legal[5].
REFERENCES
- S. B. Goyal, A. S. Rajawat, R. K. Solanki, M. A. Majmi Zaaba, and Z. A. Long, “Integrating AI With Cyber Security for Smart Industry 4.0 Application,” in 2023 International Conference on Inventive Computation Technologies (ICICT), Apr. 2023, pp. 1223–1232. doi: 10.1109/ICICT57646.2023.10134374.
- A. D. Sontan, S. V. Samuel, A. D. Sontan, and S. V. Samuel, “The intersection of Artificial Intelligence and cybersecurity: Challenges and opportunities,” World J. Adv. Res. Rev., vol. 21, no. 2, Art. no. 2, 2024, doi: 10.30574/wjarr.2024.21.2.0607.
- P. Aurucci, “Applications and Security Risks of Artificial Intelligence for Cyber Security in Digital Environment,” in Intelligent Environments 2018, IOS Press, 2018, pp. 308–317. doi: 10.3233/978-1-61499-874-7-308.
- M. Rahaman, K. T. Putra, A. Z. Arrayyan, R. Z. Syahputra, and Y. A. Pamungkas, “Design a Two-Axis Sensorless Solar Tracker Based on Real Time Clock Using MicroPython,” Emerg. Inf. Sci. Technol., vol. 4, no. 1, Art. no. 1, May 2023, doi: 10.18196/eist.v4i1.18697.
- M. Rahaman et al., “Utilizing Random Forest Algorithm for Sentiment Prediction Based on Twitter Data,” 2022, pp. 446–456. doi: 10.2991/978-94-6463-084-8_37.
- Gupta, B. B., & Agrawal, D. P. (2021). Security, privacy and forensics in the enterprise information systems. Enterprise Information Systems, 15(4), 445-447.
- Al-Sharif, Z. A., Al-Saleh, M. I., Alawneh, L. M., Jararweh, Y. I., & Gupta, B. (2020). Live forensics of software attacks on cyber–physical systems. Future Generation Computer Systems, 108, 1217-1229.
Cite As
Reddy D.R.C. (2024) The Basics of AI in Cyber Defence, Insights2Techinfo, pp.1