By: KV Sai Mounish, Department of computer science and technology, Student of computer science and technology, Madanapalle Institute Of Technology and Science, 517325, Angallu, Andhra Pradesh.
ABSTRACT –
The rapid increase in AI technology has shown tremendous growth in automation. With its benefits of using to stop most of cyber attacks such as data manipulation, data breaches, phishing, ransomware, etc. avoiding most of the harmful usage of AI. The process of how attacker plans to intrude with AI driven techniques is called AI based cyber attacks which can also be used in combination with convention attack techniques. But there is a claim in which researchers didn’t study a lot about AI based cyber attacks which is yet to prolong providing insights into new type of attacks. So this article provides you classification of different applications of AI during cyber attacks and also provide underlying base to predict new threats in future and explain how to analyze AI based cyber attacks with help of hypothesis in various scenarios. This will help to improve security in future when attacker comes with various types of intrusion techniques.
KEYWORDS – AI, automation, phishing, cyber attacks, intrusion, ransomware, data breach, data manipulation.
INTRODUCTION –
Currently AI technology is progressing through the roof revolutionizing the automation landscape and is positively impacting a few sectors among them being cyber security. Cyber security solutions backed by Artificial Intelligence displays substantial efficiency in the protection from several cyber threats such as ransomware attacks, phishing, data theft and data tampering. These advanced solutions enhance the security against threats by approximating patterns that depict efficiencies in the systems and are capable of recognizing disturbances using artificial intelligence.
But the development of AI has also brought up cyber threats: IT based cyber attacks which are mainly influenced by artificial intelligence. Such breaches make use of AI technology to either develop entirely new techniques of hacking into security systems, or enhance the current tried-and-true strategies. Stating that AI-based cyber attacks are on the rise, it is still evident that there is a huge gap in the knowledge in this regard.
Failure to analyze the various facets concerning Artificial Intelligence Cyber attacks results to the inability to develop comprehensive defenses[1]. In the current body of work, the emphasis is made more on the strategies and tactics that can be protected from AI and less on what AI can be used to protect from. This lack of concentration means there is a genuine unexplored hole in our understanding of modern threats.
Rather than leaving the reader lacking any means of envisioning the scope and the nature of threats stemming from AI use in cyber attacks, this article aims to categorize all possible uses of AI for such purposes and provide a simple basic framework for threat assessment . It will look at how hackers employ these technologies in order to perform operations such as, phishing, getting round security and procreating.
Cyber security practitioners who understand A I-based cyber threats may develop stronger defenses against such elaborate threats and shield electrical networks from prospective breaches in the future[2]. Therefore, in a context in which AI freezes the capabilities of perpetrators and protectors continues to evolve, this study will provide a useful input to the ongoing process of protection of cyberspace.
EMERGING OF AI-BASED CYBER ATTACKS –
First of all AI–based cyber attacks are the biggest ongoing threat that will be extracting new methods of hacking or develop new methods to hack itself for different type of data breaches.
The definition and scope of AI-based cyber attacks involves the cyber threats used to improve attacks which can automate different process making it very easy for attackers to crack and proceed in weak vulnerabilities and help large scale attacks with improved accuracy and exploiting weak vulnerable areas.
Automated Campaigns of Phishing
The use of AI can make phony emails that mimic the target so closely because they get to study the target’s communication patterns, their social media accounts, and so on. Given that the purposes of these emails are to copy the genuine ones, it would be easy for the recipient to be enthralled by misleading[3].
Avoidance of Detection Mechanisms:
The security system patterns can be learned by AI algorithms, and they can adapt in order not to trigger the security system. For instance, understanding the workings of the antivirus softwares that have patterns that detect malware, AI can trick the malware to escape detection.
Intelligent Malware:
A versatile kind of malware can also be developed using artificial intelligence (AI) where the malware adapts to the environment it is placed in[4]. Evidently, this malware is equipped with the capacity to decide when to act, which files to infect, and how to embark on the stealing process without raising alarms.
Synthetic and Deep fake Media:
Attackers can also leverage AI to make deep fake movies or fake voices and thus perform a manipulative campaign or distribute fake news[5]. In such circumstances, such as when the character is pretending to be a business executive signing fake transactions, this can be helpful.
These are the hacking techniques involved in AI as shown in Figure 1.
DEVELOPMENT OF ROBUST DEFENSES TO AI-BASED ATTACKS
So for the ongoing threats to AI it must be important to implement robust security measures to avoid attacks and there are some key strategies and methods to enhance that as follows:
Optimization of Threat Evaluation with AI
Machine Learning for Anomaly Detection: Build an ML model that will analyze the traffic on complex networks to detect unusual signs of cyber threats. They can learn the normal behavior patterns and identify those that are not normal and depict an attack[6].
Behavioral Analysis:
Employ artificial intelligence to supervise user and entity activism (UEBA). Thus, AI can identify those activities that do not confirm to the pattern, for instance, working hours or usage of resources that are not typical.
Implement Advanced Threat Intelligence
AI-Driven Threat Intelligence Platforms:
Integrate AI to collect, process, as well as correlate threats from different sources. AI is better in perceiving the innovated threats compared to humans and supplies timely information for prevention.
Predictive Analytics:
Take huge amount of data and analyze it with a predictive approach so as to identify the possible ways through which the organization might be attacked in future. Machine learning can determine the most probable future attacks and suggest how to prevent them basing on historical attack data.
Automate Incident Response
Automated Playbooks: Develop basic procedures which act like scripts in the case of a threat towards the reach of previous procedures. This will significantly reduce sustaining impacts on cyber attacks.
AI-Powered Security: acquire tools with AI integration when focusing on the question of how group of members will cooperate with each other.
Enhance Endpoint Protection
AI Enabled Detection and Response – EDR: Take advantage of advanced tools that are AI integrated in such a way that they are on the lookout for any activities from those endpoints. It should be noted that all the above solutions can describe behaviors in real-time and can also minimize threats at the same time.
Adaptive Defense Mechanisms: Should be incorporated Artificial intelligence into the work in creating variants of the strategies pertaining to the variation of the protection systems. This includes dynamic firewalls, adaptive and real time access policies and adjustment of policies.
We should implement different defensive strategies to protect against AI-based cyber attacks as shown in Table 1.
Defensive Strategy | Description |
Enhancing threat detection | It will utilize AI to detect anomalies by analyzing patterns. |
Advanced threat intelligence | It will gather the data and predict future threats. |
Automate response | It will detect threats by using predefined actions. |
Strengthen Phishing defense | Train users to recognize and respond to different attacks. |
Promote adaptation | By continuous improvement of response capabilities by training. |
Table 1 : Defensive strategies
CONCLUSION –
So the cyber security has seen an important shift by the birth of AI based cyber attacks but also it consequently improved its defense mechanisms and plans for the new complex attacks generated. The researchers must be ahead of attackers by using AI for being active as threats related to AI changes. It will be one sort of defensive strategy so that it will tend to increase effectiveness in threats detection, automation, avoiding endpoint of security.
This is the reason this article explores about emergence of AI based attacks and specific defensive strategy against particular attack type and also tells about negative impact of AI based cyber attacks. The attackers will be evolving by improving their strategy through analyzing the security mechanisms and by this we should be ready by reforming strategy and defend our digital assets included and gain advantage.
So it is important to recommend more countermeasures against AI based cyber attacks it will require protective framework which is trustworthy. It is found that AI based cyber attacks has effect on economy, leading to unethical practices. For example if we consider AICD it is used to show way to develop services in such a way there should be trust worthy framework to avoid these type of problems.
REFERENCES –
- J. Owen, “Fortifying Cybersecurity: Exploring Defensive Strategies Against Adversarial Attacks on AI-Driven Systems,” Aug. 2023.
- N. Kaloudi and J. Li, “The AI-Based Cyber Threat Landscape: A Survey,” ACM Comput Surv, vol. 53, no. 1, p. 20:1-20:34, Feb. 2020, doi: 10.1145/3372823.
- M. Junger, L. Koning, P. Hartel, and B. Veldkamp, “In their own words: deception detection by victims and near victims of fraud,” Front. Psychol., vol. 14, May 2023, doi: 10.3389/fpsyg.2023.1135369.
- M. I. Malik, A. Ibrahim, P. Hannay, and L. F. Sikos, “Developing Resilient Cyber-Physical Systems: A Review of State-of-the-Art Malware Detection Approaches, Gaps, and Future Directions,” Computers, vol. 12, no. 4, Art. no. 4, Apr. 2023, doi: 10.3390/computers12040079.
- T. Haksoro, A. S. Aisjah, Sreerakuvandana, M. Rahaman, and T. R. Biyanto, “Enhancing Techno Economic Efficiency of FTC Distillation Using Cloud-Based Stochastic Algorithm,” Int. J. Cloud Appl. Comput. IJCAC, vol. 13, no. 1, pp. 1–16, Jan. 2023, doi: 10.4018/IJCAC.332408.
- L. Triyono, R. Gernowo, P. Prayitno, M. Rahaman, and T. R. Yudantoro, “Fake News Detection in Indonesian Popular News Portal Using Machine Learning For Visual Impairment,” JOIV Int. J. Inform. Vis., vol. 7, no. 3, pp. 726–732, Sep. 2023, doi: 10.30630/joiv.7.3.1243
- Gupta, B. B., & Panigrahi, P. K. (2022). Analysis of the Role of Global Information Management in Advanced Decision Support Systems (DSS) for Sustainable Development. Journal of Global Information Management (JGIM), 31(2), 1-13.
- Gupta, B. B., & Narayan, S. (2021). A key-based mutual authentication framework for mobile contactless payment system using authentication server. Journal of Organizational and End User Computing (JOEUC), 33(2), 1-16.
- Gupta, B. B., & Narayan, S. (2021). A key-based mutual authentication framework for mobile contactless payment system using authentication server. Journal of Organizational and End User Computing (JOEUC), 33(2), 1-16.
Cite As
Mounish K.V.S (2024) AI-Driven Biometric Security for Cyber Defense, Insights2Techinfo, pp.1