By: Dadapeer Agraharam Shaik, Department of Computer Science and Technology, Student of Computer Science and technology, Madanapalle Institute of Technology and Science, Angallu,517325, Andhra Pradesh.
Abstract:
Due to the fact that AI’s are now present in almost facet of life, the safety of these units assumes importance. AI systems have brought in these possibilities due to its strength but at the same time cyber threats can put these systems at risk in terms of its functionality and reliability. In this paper, the author raises awareness of existing cyber threats to AI systems such as data poisoning, adversarial attacks, model inversion, as well as issues connected with insufficient security. It also covers the approaches to protect AI systems, ways on how AI systems can be safeguarded from outside threats, constant vigilance and compliance to best practices that have been adopted in the development and deployment of the said technology. Such problems can be solved and analysed to improve the reliability of AI solutions for practical applications that require careful use.
Keyword’s: AI Security, Cyber Threats, Adversarial Attacks, Data Poisoning, Model Inversion.
1.Introduction
The term “Artificial Intelligence” first appeared in 1956 and went on to grow into practical applications in various fields. Machine Learning has been contributing to the fight against cyber-crime since the 1990s, with the development of Intrusion Detection Systems (IDS) and Anomaly Detection Systems (ADS), but this was limited by data and computer capacity. Today, AI is synonymous with cybersecurity that goes beyond corporate speak. It can emulate human intelligence and behaviors making it possible to automate cybersecurity beyond human capacity and can detect a breach in a network within seconds. The advent of COVID-19 hastened digital transformation causing businesses to rely on technologies such as Artificial intelligence (AI), machine learning (ML), big data among others. However, this emergence led to an upswing on cybercrime targeting individuals and established organizations at large. By 2025, estimates put the cost of cybercrimes at $10.5 trillion. Businesses face operational and continuity risks due to reliance on these technologies. With regard to operations for their benefits organisations should consider looking into usage of AI in cybersecurity.
2.AI Techniques for Cyber Security
Therefore, information technology is relatively an easy target for crime but also it still becomes the means through which a number of crimes have been committed. Increasing availability of devices and sophisticated products has simplified the process of carrying out attacks from any point without trace. Cybercrime refers to digital/cyber/computer/network crime in which these offenders primarily aim at stealing useful information such as hacking bank servers for money, taking away personal records etc. Among such crimes are online extortion, copyright infringements, international money laundering and economic espionage. These crimes have not only become increasingly commonplace but their nature has also become more dangerous.
Thus, artificial intelligence integration in cyber security has revolutionized the game. Also in review section above, there exist organizations as well as researchers who are putting much effort into coming up with new ways that will help them blend AI and safety which have proven helpful to many other organizations that will be mentioned later in this part.
Having discussed how AI is increasingly making efforts to become smarter n we can now see how AI works. There are three ways in which AI functions:
- Assisted intelligence: This helps people to improve what they have already been doing.
- Augmented intelligence: Provides help with things that cannot be easily done by people.
- Autonomous intelligence: Machine learning features that act as a separate entity and function on their own.
With this outline, it is possible to say that AI tackles tasks from merely supplementing current systems to handling the toughest ones alone like cybersecurity, since cyberattacks have shown themselves capable of being potentially ruinous.[1]
3. Defending Against AI-Driven Cyber Threats: Current Strategies.
The idea of protecting organizations and businesses against AI aided cyber threats is no doubt a complicated and even dynamic task for organizations and cyber security specialists. With the emergent usages of artificial intelligence AI and machine learning ML technologies in optimization and optimization of cyberattacks contemporary security measures are struggling to keep up. As a result, innovative sophisticated ways and means of combating the advanced AI-based cyber threat need to be developed and established by organizations.
An important methodology for preventing AI threats in cyber security is the integration of AI and ML methods for threat identification and combating. AI helps organizations to collect large amounts of data and through pattern analysis to identify new threats, which can then be effectively defended against. Modern machine learning approaches including anomaly detection, and behavioural analysis allow an organization to identify slight changes in the normal operation of a network and possibly a sign of an ongoing security breach.
In addition, there is an ability of AI to deliver security analytics for organizations that correlate and enrich security data from various sources, including network traffic logs, endpoints and threat feeds. Terrorists use the internet for propaganda, recruitment, and radicalization, with approximately 95% of websites and 45 % of social media accounts the major sources of terrorists’ communication. Also, the security orchestration and automation platforms powered by AI facilitate organizations to automate the processes and address security events in a short amount of time.
Fig.1 Securing AI Against Cyber Threats.
Another approach to protecting organizations’ systems against AI-DCT is the use of AI-security measures and AI-security principles. This is true considering the need for secure development practices which entail things like input validation, parameter sanitization in a bid to reduce instances of vulnerabilities around AI. Also, stronger authentication and access control measures should be put in place to curb affairs to any AI systems and information and knowledge definite to the organization. Moreover, organizations must regularly ensure the protection of AI training data and models; it is needed to apply encryption and access controls, as well as audit trails to avoid data breaches and manipulation [2].
4.AI based security solutions
Significant studies have been performed at the time of developing the improvements for the IoT network security against various types of attacks. This section describes the characteristics of the existing ML and DL methods that can enhance the efficiency of security solutions for smart IoT networks[3].
Traditional ML approaches can be categorized into two types: To be specific, rule-based ML and shallow ML. A characteristic feature of rule-based ML is that to train the model and automatically process the trained material based on the given actions, it is necessary to adhere to a set of preliminary protocols of the model’s construction. However, the particular kind of rule-based ML solutions can be Fuzzy Logic (FL), Fuzzy Neural Network (FNN), and Neuro-Fuzzy Inference System (NFIS). On the other hand, in the shallow machine learning, the process of feature extraction involves prior knowledge on the information being learned by the model. Shallow Machine Learning algorithms’ dependence is based on the area of implementation and the pattern of prediction framework which includes – regression, classification, clustering and reinforcement learning. Applications of Shallow Machine Learning methods which is used to classify or cluster the trained data to detect malicious activities in the network includes Decision Trees (DT), Support Vector Machines (SVM), Naive Bayes (NB), K-Nearest Neighbors (KNN), Random Forest (RF), and Ensemble Learning[4].
But, now a day, researchers focus on DL instead of traditional ML techniques. DL employs a multi-layered ANN structure and employs more complex mathematical formulae as per algorithmic computation and in many occasions surpass traditional (single layer) ML tools. DL is also capable of learning features by applying several layers of processing on the raw data which may not have much or any pre-processing done on it. Thus, the use of DL models for different applications in IoT networks is more distinct with ANN, CNN, RNN, and autoencoders to address different threats [5].
Reference:
- A. Anandita Iyer and K. S. Umadevi, “Role of AI and Its Impact on the Development of Cyber Security Applications,” in Artificial Intelligence and Cyber Security in Industry 4.0, V. Sarveshwaran, J. I.-Z. Chen, and D. Pelusi, Eds., Singapore: Springer Nature, 2023, pp. 23–46. doi: 10.1007/978-981-99-2115-7_2.
- B. T. Familoni, “CYBERSECURITY CHALLENGES IN THE AGE OF AI: THEORETICAL APPROACHES AND PRACTICAL SOLUTIONS,” Comput. Sci. IT Res. J., vol. 5, no. 3, Art. no. 3, Mar. 2024, doi: 10.51594/csitrj.v5i3.930.
- B. D. Alfia, A. Asroni, S. Riyadi, and M. Rahaman, “Development of Desktop-Based Employee Payroll: A Case Study on PT. Bio Pilar Utama,” Emerg. Inf. Sci. Technol., vol. 4, no. 2, Art. no. 2, Dec. 2023, doi: 10.18196/eist.v4i2.20732.
- M. Rahaman, C.-Y. Lin, and M. Moslehpour, “SAPD: Secure Authentication Protocol Development for Smart Healthcare Management Using IoT,” in 2023 IEEE 12th Global Conference on Consumer Electronics (GCCE), Oct. 2023, pp. 1014–1018. doi: 10.1109/GCCE59613.2023.10315475.
- S. Zaman et al., “Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey,” IEEE Access, vol. 9, pp. 94668–94690, 2021, doi: 10.1109/ACCESS.2021.3089681.
- 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
Shaik D.A. (2024) Securing AI Systems Against Cyber Threats, Insights2Techinfo, pp.1