By: Dhanush Reddy Chinthaparthy Reddy, Department of Computer Science and Artificial Intelligence, Madanapalle Institute of Technology and Science, Angallu(517325), Andhra Pradesh
Abstract
The extensive interdependence on interconnected systems in the hi-tech digital world has posed a range of cybersecurity risks to people, corporations, as well as governments. Although information system security methods are constantly being developed and implemented, native security protocols emerge as more and more complex and frequent cyber assaults are launched on important data and assets. AI has the ability to analyze large sets of information, identify patterns and automate thus providing effective solutions to further the cybersecurity measures. To sum up, this paper offers a basic but sundry explanation of AI and cybersecurity interrelation. It explain how AI is used across the cybersecurity spectrum, for example in threats identification, response and in vulnerabilities. The paper also describes various deployments of AI in cybersecurity and their corresponding limitations, including the issues with bias, privacy, and adversarial examples. Thus, by the end of this paper, readers will understand how AI can help improve the state of cybersecurity and what measures that have to be taken to use AI in this field correctly.
Keywords: Artificial Intelligence,Cybersecurity,Threats
Introduction
Cybersecurity is a compilation of measures used to safeguard electronic information computer-users, and programs, among other valuable commodities, from harm and invasion by unauthorized parties. Given the current trend of change as influenced by the introduction of digital technologies in almost every sector, the effectiveness and sophistication of cyberattacks are equally on the rise at the same pace. Just like Moore’s Law of the processing power of an integrated circuit, which has been doubling every two years, the cyber adversaries are quickly doubling the efficiency of cyber-attacks for a minuscule fraction of the costs in some cases, even within months.
With the increase of the dependence on digital structures worldwide, the demand for the protection of such structures from cyber threats also increases. Global expenditure on cyber security is anticipated to reach $1trillion from 2016 to 2021 showing how much the global community has been willing to budget to counter the rising threats. Such an increase in the expenditure has been seen fitting and necessary to counter with equal force the hackers whose approach is unceasingly fluid and innovative.
This fight is a never-ending one, and Artificial Intelligence (AI) has a lot to do with this. AI can be defined as the system that can emulate the human mind or think like human beings and carry out activities that can be solved through human intellect. These systems can identify voice, convert the information into various languages, as well duplicate cognitive tasks like thinking and learning. AI is a branch of study that has under its control various branches of math, computer science, and philosophy, mainly dealing with the invention of smart machines and sources constituting the human characteristics.
AI has been indispensable today and especially in the coming years, especially when it comes to cybersecurity. Today AI is leading in areas of threat detection and prevention, countermeasures, which will make a significant contribution to improve the existing cybersecurity. With this background, it becomes possible to focus on the topic of analysing the relations between artificial intelligence and cybersecurity and ponder how this approach is changing the methodologies of protection.[1]
The Role of AI in Cybersecurity
The incorporation of AI in cybersecurity has been observed to offer a lot of gains to organizations. For example, those organisations that have implemented AI based cybersecurity solutions are seeing improved ROI from cybersecurity tools. Siemens AG which is an international company that deals with electrification, automation, and digitalization can be associated with the application of AI. Through the implementation of AWS, the CDC at Siemens was able to create an effective AI system that could analyse 60,000 potential critical risks within a second. Notably, this high-speed, fully automated, and highly scalable platform is controlled by a team of employees below twelve in number while the system’s efficiency is not in any way impaired.
AI also reduces the cost of identifying and containing breaches since organizations reduce the time spent on identifying new threats, investigating and remediating them since similar threats are repeated hence acknowledge and acknowledged by the system, hence improving on their detection and response time. Survey reveals that majority of the executives believe that implementations of AI decrease these costs by 1-15 percent, with an average of 12 percent. There are even some organisations that have been able to get even more advanced improvements in cost reduction improving the depth of the organisations’ financiers. He, who is the CISO of MediaMarktSaturn Retail Group, Oliver Scherer, for instance, said, “AI gives a chance to enhance the cybersecurity as the process of detecting threats, reacting to them and fixing problems is shifted from manual to efficient, automated one.”
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Adversarial AI
Adversarial AI could be described as a form of deception strategies that attempt to manipulate a machine learning model with slight perturbations of the input data are called adversarial examples. Such attacks can greatly affect the model thereby arriving at wrong predictions or decisions. In cases where AI can work against the user, it becomes especially dangerous in life-critical applications for example, self-driving cars, [3] which if fails to identify a sign could have disastrous consequences. Thus deep learning models are also prone to such attacks, which shows the need to have secure and reliable artificial intelligent systems. These attacks can be made either during the training of a model known as a poisoning attack [4]or during the prediction phase as an evasion attack which significantly degrades the model’s efficacy. These threats have been noticed and tried to be dealt and as such, as the recognition of these threats increases, so has the attempts to standardize AI systems and increase their security. However, the defence mechanisms that are currently available for protection against adversarial AI are by no means very advanced[5], and more sophisticated defence strategies are badly missing when it comes to defending AI applications in several diverse fields. Adversarial AI is an ideal that emphasizes the need to assess AI on a constant basis, to check on whether it has been infiltrated by malicious users and whether it can hold up to current challenges.
Conclusion
AI is a promising tool that can improve the potential of identifying, mitigating, and dealing with cyber threats in the field of cybersecurity. Due to its capability to process immense amounts of input data, identify patterns, and perform routine work, it is an indispensable means for cybersecurity specialists. However, there are various issues that need to be addressed before organizations start applying AI technologies in cybersecurity, including the problem of developing robust models for every type of AI tool used in the cybersecurity sphere and the problem of maintaining people’s trust in the technologies and their effectiveness due to the increasing risk of false-positive results and the possibility of diminishing human decision-making authority. Should these concerns be well addressed, it will be possible to find efficient solutions based on AI technology that will considerably advance the possession of secure cyber-defence systems to protect organizational data.
Reference
- F. Tao, M. S. Akhtar, and Z. Jiayuan, “The future of Artificial Intelligence in Cybersecurity: A Comprehensive Survey,” EAI Endorsed Trans. Creat. Technol., vol. 8, no. 28, Art. no. 28, Jul. 2021, doi: 10.4108/eai.7-7-2021.170285.
- L. Lazic, BENEFIT FROM AI IN CYBERSECURITY. 2019.
- S. Zhou, C. Liu, D. Ye, T. Zhu, W. Zhou, and P. S. Yu, “Adversarial Attacks and Defenses in Deep Learning: From a Perspective of Cybersecurity,” ACM Comput Surv, vol. 55, no. 8, p. 163:1-163:39, Dec. 2022, doi: 10.1145/3547330.
- 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.
- 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.
- Band, S. S., Qasem, S. N., Ameri, R., Pai, H. T., Gupta, B. B., Mehdizadeh, S., & Mosavi, A. (2024). Deep learning hybrid models with multivariate variational mode decomposition for estimating daily solar radiation. Alexandria Engineering Journal, 105, 613-625.
- Mishra, P., Jain, T., Aggarwal, P., Paul, G., Gupta, B. B., Attar, R. W., & Gaurav, A. (2024). CloudIntellMal: An advanced cloud based intelligent malware detection framework to analyze android applications. Computers and Electrical Engineering, 119, 109483.
Cite As
Reddy D.R.C. (2024) AI and Cybersecurity: A Simple Introduction, Insights2Techinfo, pp.1