Role of Artificial Intelligence in enhancing IOT Security

By: Rishitha Chokkappagari, Department of Computer Science &Engineering, student of Computer Science & Engineering, Madanapalle Institute of Technology & Science, Angallu (517325), Andhra Pradesh. chokkappagaririshitha@gmail.com

Abstract

Internet of Things (IoT) devices have intensified in the recent past and have impacted many industries to facilitate interconnectivity and data sharing. At the same time, this advancement implies severe security issues. Indeed, IoT networks are becoming a favourite of cyber threats. IoT security is on the receiving end hence, Artificial Intelligence (AI) presents itself as a favourable solution in enhancing security through analysis of data, patterns, and real-time threats detection. This article seeks to establish the place of AI in the improvement of IoT security. The study looks at how machine learning, predictive analysis, and response can check for security issues and act on them accordingly. In our next level of research, we investigate individual AI approaches that can establish strong prevention strategies against complex attacks and protect IoT environments’ integrity and confidentiality. Moreover, the paper analyses the possibility of using AI frameworks in dynamic creation of protective measures based on threats observed in the networks hence enhancing the resilience of the IoT networks. Here, by combining AI with IoT secure measures we can attain a smarter environment for IoT along with safety from future advancements thus enhancing the security of technology.

Keywords: Artificial Intelligence, IOT, Cybersecurity

Introduction

The Internet of Things (IoT) denotes a new dimension of co-joining trillions of gadgets and objects that form the fabric of society, adding a new dimension to the web where real objects and things exchange information and fuel concepts of the smart universe. While the above trend enhances communication, this connectivity level also implies significantly increased cyber threats. With more IoT devices being deployed these are some of the areas that hackers attempt to gain access to, and which have effects extending beyond individual users to the network and systems of large organizations. The current conventional methods of protection, which predominantly are the after-the-fact and the rules of thumb, fail to meet the modern IoT challenges. This is where Artificial Intelligence or AI as it is commonly referred to comes in, with an innovative solution for cybersecurity.

The relevance of AI in strengthening IoT security comes from the platform’s capacity to learn as well as adapt to provide a current response to security threats. Machine learning algorithms integrated with AI allow it to receive a huge amount of data produced by IoT devices and analyse the information to recognize irregularities that can be associated with a security threat. Such a process of detection is proactive, thus allowing the organization to respond earlier, and possibly prevent additional damage. Besides, using AI, the next attack is predicted based on analysis of past occurrences, and enhanced security measures are instituted.

In this context, again AI is not just strengthening the traditional security models, but it is transforming the way security is implemented and managed making it more efficient, smarter and stronger. consequently, as IoT becomes more intertwined with various aspects of the population’s everyday life from the home to industry control and automation, the nexus between AI and IoT security will become even more crucial in ensuring the protection of the connected community.

1. Artificial Intelligence in Cybersecurity

To ensure dynamic protection of systems from threats, nowadays, cybersecurity specialists rely on Artificial intelligence (AI), especially for identification of intrusion based on traffic analysis for activity that looks like an attack[1]. The fig.1 shows the AI with CS.

1. Machine Learning: Supervised learning focuses people to manually assign the training data either as a virus or non-virus and then the algorithm assigns them to certain classes. Unsupervised learning does not use training data, and clusters the data according to its coherency and modularity. One usual type of machine learning algorithms widely used in cybersecurity is naïve Bayes, which involves a supervised way to learn data and assign it into distinct classes using the Bayesian theorem, regarding the intent of the anomalous activity as coming from separate events[2].
2. Decision Trees: Decision tree formulates rules from the training data to group traffic for instance by recognizing DoS attacks by the flow rate, size and duration of traffic. command injection attacks while the former can categorize CPU consumption, network flow, and data volume. The second is the Rule-Learning which looks for the characteristics of an attack to build a set of rules for classification[1].
3. K-Nearest Neighbours (k-NN): It is therefore noticeable that k-NN classified data based on the evaluation of the distance of new and already analysed data within the Euclidean space. It is of value for IDSs as it enables the fast adaptation to the new traffic patterns thus successfully detecting unknown forms of attacks.
4. Support Vector Machines (SVMs): SVMs place the data in a plane which separates the data into classes, and they can be linear, non-linear, or polynomial. In the area of cybersecurity, SVM’s work through analysing the internet traffic pattern and categorise them to as HTTP, FTP, SMTP and so on. SVMs are usually utilized in applications whereby attack traffic can be emulated from penetration testing traffic as components of training data.
5. Artificial Neural Networks (ANNs): ANNs try to mirror the way neurons communicate in the brain where information is passed and interpreted with the help of neurons, and thus adapt their model with additional information. This versatility places ANNs in a position to detect zero-day attacks and do well in IDS particularly with the DoS attacks[1].

Cybersecurity using Artificial Intelligence is still emerging with great potential for use, but costly sometimes. Thus, it is seen that the AI cybersecurity measures are useful for large networks such as smart cities where they basically detect that a particular network is already under attack, but do not prevent those attacks from occurring in the first place, which emphasizes the importance of using other preventive measures[3].

1. Privacy and Security Issues

Several privacy and security issues arise in IoT technologies. These places being compromised are aimed at by intruders with the evil intention of wreaking havoc and stealing data for their gains as seen in the various invasion techniques, including unauthorized entry, simple deception, viruses, betrayers, and theft[4]. The fig.2 below shows the privacy and security issues in Cyber security with IOT.

1. Cyber-attacks: The hacking of IoT systems entails the usage of hacking tools and techniques that unveil specific information or for fun. These attacks may involve obtaining the authentication details of the network, monitoring communication that is being conducted in the network and interception of traffic that is being conducted in the network and this is because this traffic is not encrypted.

1. Software and Hardware Systems Issues: As for the weaknesses, IoT systems are proved to have both the software and the hardware ones. These are failed activities involving application software, operating systems, drivers, and communication protocols; most of which stem from human errors in the software. Hardware issues are much more challenging to solve, particularly when it comes to the integration of different products in the same operational environment, including the Internet. These vulnerabilities could occur from various issues in planning, management, and allocation of resources to them which in turn compromise user security and privacy.
2. Structured and Unstructured attacks: The human threats to the IoT systems can be categorized as either being an external threat or an internal threat. Several threats are categorized as external threats which are threats from people outside the system while others are internal, they are from people who have permission to be inside the system. In unstructured attacks hackers rely on numerous tools to obtain data, while in structured attack hackers employ different tendencies in order to stress system flaws. APTs are geared towards networks that contain valuable information such as those which belongs to the government and the financial fraternity[5].
3. Access and Scouting Efficiencies: Access attacks seek unauthorized information like physical access to the device or access over the Internet. Scouting attacks collect valuable information about an operating system; commonly it employs packet capturing and system port scanning, which may go unnoticed as simple network disturbances.
4. Denial-of-Service and Viruses: DoS, which floods the networks using the compromised computers, commonly known as zombies, are a threat to SCADA systems. These are also expressed as ransomware taking full control of systems. Virus are skewed programs that change program’s functions and for this can pass from one computer to another, creating problems of general low performance and leaking of data[6].
5. Ransomware: Ransomware could be described as a type of virus that locks up the victim’s data, and the only way to reverse it, is paying a given amount of money, often in bitcoin. These may be through phishing techniques that make the users click on destructive links and files. Often organisations may end up investing some huge amount of money and time to try recover the lost files and its systems than paying the ransom demanded[5].
6. Dictionary and Brute Force Attacks: IoT systems are a target of Password-Based attacks. Dictionary attack involves using letters and numbers of a word to guess passwords of the concerned account, while the brute force attack involves trying every single combination of letters and numbers. These methods are employed to intrude into the users’ accounts with the intention for gaining improper access.
7. Authentication and Authorization: Secure authentication and authorization mechanism is highly important when many connected devices are participating in IoT networks. These default credentials set by manufacturers of the devices are not secure. In research conducted by ESET in 2016, it was established that approximately 15 percent of the tested routers had poor security resulting from default credentials that were easily exploitable by hackers[5].

Conclusion

The use of AI that has been adopted regarding IoT security policies is a significant revolution in protecting the devices and networks. The utilization of the AI in the machine learning, threat detection, and the predictive analysis used in security gives a competitive advantage in the detection of any other risks that the normal security would not detect. Through the integration of AI, the security systems of an organization can be heightened to be more strategic and anticipating of threats hence can respond as required and adopt to newer modes of risks. Although AI improves IoT security it comes with its own limitations like possible privacy invasion, and the need to constantly update the algorithm. Thus, the positive aspects of AI should be outweighed by these challenges to keep the AI-driven security approach efficient and moral.

In the future, there will be more collaborations between AI and IoT that will enhance innovation in security measures and prevents. Nevertheless, as AI develops, the integration of this technology into IoT security will be even more crucial because of continuously emerging threats, which necessitates the searching and discovering process. In this constantly evolving environment, it is necessary to apply artificial intelligence as one of the means to create effective, reliable, and future-proof IoT security solutions.

References

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Cite As

Chokkappagari R. (2024) Role of Artificial Intelligence in enhancing IOT Security, Insights2Techinfo, pp.1

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