By: Dhanush Reddy Chinthaparthy Reddy, Department of Computer Science and Artificial Intelligence, Madanapalle Institute of Technology and Science, Angallu(517325), Andhra Pradesh
Abstract
Nowadays, it is impossible to imagine development without the support of digital infrastructure, thus, network protection from cyber threats is imperative. The fourth wave in IT security is Artificial Intelligence (AI) that initiated great changes while developing and applying the protective measures. Therefore, this paper focuses on investigating the practices and opportunities of AI in protecting the network while discussing the problem that it solves in cybersecurity. We first look at today’s networks threats and the drawbacks that the conventional security solutions have. At the centre of this research subject is the implementation of AI technologies, including the machine learning and deep learning AI models, in the identification, counteraction, and management of cyber threats. Some of the AI systems we talk about include anomaly detection systems, predictive systems, and response systems. Moreover, we emphasize on the fact that it is evident in scenarios and cases where AI has prevented cyber terrorism effective and efficient. Lastly, the paper discusses the ethical and technical implications of putting to use AI in networks’ security with all the pros and cons of AI presented to the reader. Overall, it is evident that the AI is a major step in fortifying the measures used to protect the networks, especially because constant creation and strict adherence to the use of AI are essential to counter the constantly transforming cyber threats.
Keyword: Network, Protection, Cybersecurity.
Introduction
The amount of information collected with the help of personal devices cannot be deemed as an inconsiderable amount, using distributed and connected computation devices. This data is expected to be employed by the AI algorithms to perform functions that had well been performed by intelligent entities dependably and autonomously. These endurances in today’s world include robotics in industries, speech in the retail, knowledge reasoning in health sciences, control in self-driving vehicles, and many more. This progress is also supported by the fact that both, the usage of specific software frameworks and specially designed hardware accelerators, are on a rising trend. However, depending on its weakness, this kind of AI system is prone to cyber risks such as piracy and threats unique to the AI system.
As of now, increasing numbers of organizations experience cyber threats and the calculated cost for cybercrime in the year 2021 is 6 USD trillion. These attacks are on not only classical ICT technology but also on the AI systems. The subject is proved by the easiness of faking an AI; for example, Goodfellow et al., who ‘tempted’ an AI and ‘fooled’ it into believing that there is a panda in a picture as well as that the panda is a gibbon with almost absolute certainty using such a simple trick as adding noise; or Sharif et al., who developed adversarial glasses that can fool facial recognition systems. The real – world consequences involve threats to AI’S utility for example the misidentification of traffic signals in automated vehicles.
As for the functionality we are focusing on the interactions, the training data of AI, and the set of parameters of AI that was created as the attacker’s aim. Since the generation of the trained AI algorithm requires several inputs, it is considered as a valuable IP and within the scope of the adversary. New threats can be summarized by IP piracy, overproduction, and unlawful utilization of the AI models. Regarding this, the following protection mechanisms have been propagated by researchers. Juuti et al. suggested that there should be a detection mechanism for the model extraction attack Chakraborty et al. suggested that the AI services should be execute only on secure hardware and this is to be granted only to accredited users. Thus, most protection strategies mainly focus on protection that is watermarking and fingerprinting of artificial intelligence models. [1]
Deep Learning
Deep learning can be regarded as a subfield of machine learning that deals with employing artificial neural networks with multiple layers to obtain information from data. In contrast to other machine learning that could possibly involve less complex neural networks, deep learning makes use of deeper networks of neurons. These artificial neural networks are derived from biological systems; A node (or neuron) in this network sends signals to other nodes.
Common structures of a deep learning neural network consist of the input layer, the hidden layers, and the output layer. The term ‘‘Deep’’ indicates the number of stages that the input and output data go through in their transformation. The output from the previous layer is then modified and compounded in a higher level making it more abstract in the succeeding layer. This data is then forwarded to the next layer where the network is then enabled to perform progressively complex transformations.
Swarm Intelligence
Swarm intelligence can be described as the capacity of the large population of agents that are interacting with each other and forms a system. Self-organizing behaviour is borrowed from nature, that is, from the behaviour of ant swarms, birds, fish, microbial colonies, etc., which can effectively solve common problems.
An example of SI in nature can be synthesized from Deneubourg’s experiment in 1990 where ants the place offered white two routes: short and long to the food source. The ants, collectively, made choices that would always favour the shorter path showing us how collective behaviour could be convergence to efficient solution.
Swarm intelligence was first used in computing by Beni and Wang in the context of cellular robotic systems. Here, SI is seen in terms of multiple agents, like self-driving cars or nodes in a P2P network, that do not have a manager.
Expert System
An expert system (ES) is an information system that is developed and programmed to replicate the decision making of a human being in a particular context. Their main purpose is to solve such issues taking into consideration a rational approach based on the human experience. Instead of following procedural code, expert systems formulate this reasoning through a series of, ‘if something then something else’.
At the heart of an expert system are two key components: The two components within the expert system are the knowledge base and the inference engine. A knowledge base consists of a number of IF-THEN rules extracted from human experts’ experience in a specific field. With these rules of inference the inference engine then applies what is known to deduce what is not known.
Inference can be done in two main ways: two major types of chaining which are the forward chaining and the backward chaining. Forward chaining is the working in the opposite fashion starting from the knowledge contained in the data base and operating the rules to derive the new data. While backward chaining starts with an intended conclusion and through the rules of the system it searches for the data or circumstances that will lead to that intended result.
In addition, other advanced expert systems can also possess the ability to justify their decisions, how the arrived at a certain decision.
Challenges with IOT Protection
The problem area of IoT becomes especially critical given the fact that heterogenous and inter-connected networks must be secured [2]. A vast majority of IoT devices are ill-suited in terms of processing capabilities to address cyber threats and privacy issues exposing various openings all over the network. The specialists in the sphere of IT-security underline that weak protection concepts in many IoT devices make the gadgets potential objects of attacks. Also, controlling the data that IOT devices will generate – data regarding size, speed, variety, and importance of the data – is another problem.[3] Another issue is the identification and verification of every device within the network, for example, most devices do not have a unique number like the IMEI number encountered in phones. Finally, the possibility to physically protect these devices or geo-location of the devices is another factor of safe and sound protection[4].
Conclusion
AI offers a great chance to improve threat detection, prevent action instead of reacting to it and respond on the fly in the context of network security. Of course, there are some difficulties which must be solved, but the paybacks of AI application in the sphere of cybersecurity are great. With the emergence of new cyber threats strengthening, the balance in networks protection and data integrity will rely on AI-based security solutions. Businesses need to adopt AI as an important element of their protection from threats since the threats are constantly evolving.
References
- F. Regazzoni, P. Palmieri, F. Smailbegovic, R. Cammarota, and I. Polian, “Protecting artificial intelligence IPs: a survey of watermarking and fingerprinting for machine learning,” CAAI Trans. Intell. Technol., vol. 6, no. 2, pp. 180–191, 2021, doi: 10.1049/cit2.12029.
- Y. Alkali, I. Routray, and P. Whig, “Study of various methods for reliable, efficient and Secured IoT using Artificial Intelligence,” Jan. 28, 2022, Rochester, NY: 4020364. doi: 10.2139/ssrn.4020364.
- 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, C.-Y. Lin, P. Pappachan, B. B. Gupta, and C.-H. Hsu, “Privacy-Centric AI and IoT Solutions for Smart Rural Farm Monitoring and Control,” Sensors, vol. 24, no. 13, p. 4157, Jun. 2024, doi: 10.3390/s24134157.
- Sharma, A., Gupta, B. B., Singh, A. K., & Saraswat, V. K. (2023). Advanced persistent threats (apt): evolution, anatomy, attribution and countermeasures. Journal of Ambient Intelligence and Humanized Computing, 14(7), 9355-9381.
- Sharma, A., Gupta, B. B., Singh, A. K., & Saraswat, V. K. (2023). A novel approach for detection of APT malware using multi-dimensional hybrid Bayesian belief network. International Journal of Information Security, 22(1), 119-135.
Cite As
Reddy D.R.C. (2024) Protecting Your Network with AI Technology, Insights2Techinfo, pp.1