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
There is an exponential rise in the use of the internet of things, which poses yet another layer of potential cyber threats, which is not safe to the normal user and the large-scale organizations alike. Problems that legacy security mechanisms present when applied in IoT networks include: The key constraint of IoT networks comprises of the considerable disparity of the environment among the interconnected devices, and there exists limited onto-resource which must be addressed appropriately. The article also aims at attempting to redefine the approach to enhance the security of IoT networks from cyber-attacks through AI methods. Threat prevention, threat detection, threat response are some of the ways AI through machine learning algorithm, Anomaly detection, and predictive analytics can help in protecting a system or network and act on it in real time without having to spend much time on it. Several AI based approaches are described in the process of the study, including behavioural anomaly detection, traffic analysis and self-motivated reaction to threats. And it evaluates the relevance of such approaches for raising the threat detection ratio, lowering the false-positive rate, and realizing high IOT compatibility and versatility in different situations. The presented study shows that it is possible to enhance the security approaches for IoT systems using AI, suggesting that it is possible to build upon it to tackle the modern complex cyber threats and safeguard the interconnected networks.
Keywords: Cyberattacks, IOT network, Artificial Intelligence
Introduction
The usage of IoT concerns the placing of Internet in objects or things is a revolution in the innovation as it aids the association of objects and systems to enhance efficacy, comfort, and invention in various sectors. But it has also given a new loophole which is none in IoT networks, i.e., mean, they are the easiest to attack by the hackers. The attacks are increasing day by day and are very sophisticated which poses danger to the privacy of the people and securities of the organizations and even the essential framework of a specific country. Therefore, Artificial Intelligence (AI) is presented as a significant antagonist to cyber threats[1]. Stemming from the manageable data with the help of machine leaned algorithms, pattern recognition, and anomaly detection, AI forms the reactive and proactive defence that can suitably react or prevent a threat. IoT security is explained more in detail thus understanding how intelligent systems may reduce the risks of connected gadgets[2].
To begin with, one must define what the peculiarities of IoT networks’ security threats are derived from such as their heterogeneity, relatively small computational power of individual items, and difficulties connected with the management of large amounts of data. Now, finally, it is worth switching to the discussion of numerous possibilities of using AI for protection of these networks. This encompasses the use of supervised and unsupervised learning for conjunction anomaly, reinforcement learning for dynamic security control, and deep learning for the threat’s prediction. Furthermore, we briefly discuss some of its applications and real-life case studies encompassing AI protecting the IoT from cyber threats. From these successes, one can gather different tips and advice regarding the usage of the AI in security application cases.
Thus, it can be stated that despite the fact, there are some challenges that can be associated with the use of AI in IoT security, the former has more opportunities than the latter can possibly offer challenges. Therefore, adopting AI increases the development of structures that are ready to defend against new dangers, hence preserving the connected world.
- The challenge of Securing IOT Network
The most significant issue which Concern for IoT networks encounter is that it is penetrated where devices are to be connected in IoT they may require to share computation and memory that is to be supplied as well as power is to be supplied. This of course makes it difficult to come up with good procedures that should be observed that in turn enhance security and avert loss of important information. Further, IoT devices are connected by insecure links and may be placed in households and in plants the regulate public utilities[3]. This is aggravated more by the fact that most of the devices in the IoT domain are of a longer lifespan and are less likely to be recalled incorporating security update when available.
Security of the internet of things through the provision of Artificial intelligence: A paper topic is quite an instrumental construction that needs to be studied in its actual usage in various contexts, as a textual practice and a discursive event. AI can enhance IoT security in several key areas.Fig.1 shows how to secure the IOT Network. Therefore, the following marked strategies appear to be useful for applying AI in IoT security enhancement:
Anomaly Detection: As for the IoT devices predicting the possible cyberattacks using collected data, the success is achieved by the help of the modern artificial intelligence techniques, namely, machine learning methods which can inform about the suspicious activity related to the possible cyberattack. They can be trained in such a way that they will be able to distinguish between what can be regarded as normal and what can be regarded as suspicious. Thus, in addition to the prescribed threats listed above, other threats can also be trained onto the model[4].
Intrusion Detection and Prevention Systems (IDPS): Secondly, to provide distinction from the terms ‘intrusion’ widely used in Intrusion Detection and Prevention Systems commonly referred to as IDPS. Thus, the IDPS which integrates the described AI one could systematically monitor the data flow in the network as well as the information exchanged by its devices. If such systems expound the use of deep learning, they have the capacity of learning about emergent patterns of attacks like the complex ones; and they autonomous ability to manage such attacks on their own for instance, by indulging the affected gadgets or filtering the traffic[5].
Behavioural Analysis: AI can establish the standard of how each of the IoT devices is expected to act. Getting back to the configuration, any deviation from the above-described initial state would mean that the alarms are ringing for security threats. In this form of analysis, the emphasis is put more on the actions performed by the user and as such it is very ideal in identifying insiders and the compromised machines that mostly carry out the malicious deeds.
Automated Threat Response: AI systems can also acquire set of response actions in case of an attack so that the impacts of the attack can be reduced to the barest minimal. This is Wall’s exclusive activities like using patches, interrupting the network and even pulling the gadgets offline to reduce further possibility of damaging the gadgets. These heats respond with speed that is vital in emending consequences of cyberattacks, a possibility made achievable by Automation.
Predictive Analytics: There is powerful capability of using past experiences combined with the trends to foresee some other gaps and threats in the system. Such a strategy assists an organization to be ready and address any loophole that an enemy can exploit to launch an attack.
- AI in the present security system of IoT devices
Several AI techniques are particularly effective in enhancing IoT security. Fig.2 below shows the process of security in IOT devices using AI. The following are some methods in AI that play a major role in IoT security:
- Machine Learning (ML): Machine learning which is one of the predictive analytics techniques can be trained from the collected data sets in the network traffic and device interactions to identify and predict threats. Classification learner models for supervised settings are used to categorize well known threats while for unknown threats, there are learner models in unsupervised settings[6].
- Deep Learning: Deep learning models which are specialized ANN can process lots of information and discover alternatives that imply the existence of cyber threats. As such it can also be used to specify models especially for the unstructured data arriving from different IoT devices.
- Reinforcement Learning: This technique enables the AI systems to master the mode that could be employed in the usage of how through practice. Hence, it can find its application in creation of reaction strategies to various attack scenarios in reinforcement learning.
- Future Directions
In summary, the current condition of IoT security using Artificial Intelligence is still at the experimenting level, and academics are trying their best to improve the AI model of classifiers. Future developments may include:
- Federated Learning: This approach offers a method of training as many of the AI models as is on numerous DA devices, employing the raw data without introducing the data to any over eager third party.
- Explainable AI (XAI): In the past, AI systems were developed and designed to be subservient to human beings while making decisions; however, using AI systems today means that the underlying concepts are that the systems are learning and are now capable of decision making on their own, and hence there is a need for justification. XAI is an approach that helps in the communication of results of AI decisions to reduce any form of suspicion and ensure adherence to the law.
- Edge AI: Realising AI at the edge of IoT devices also contributes to reducing latencies and allows implementing real-time threat detection and prevention, thus, three IoT networks’ security is improved.
Conclusion
The combination of IoT and AI constitutes a rather significant milestone in the field of cybersecurity. Since the IoT network is progressively being applied around the world, the vulnerability of these systems to cyberattacks enhances proportionally. Thus, Artificial Intelligence is the most potent ally and applicable solution, which can easily detect and eliminate threats without any delay. In this case, during this discussion, we have pointed out that IoT networks present special difficulties which include, The inputs as well as the processing and analysis of this data in real-time by AI, become a very strong defence mechanism. A variety of methods like anomaly detection, predictive analysis, and other self-learning security solutions prove that AI can be applied widely and is beneficial in IoT security.
Taking examples and use cases of real-life IoT systems, the importance of using AI for IoT security is established. All these examples can demonstrate the successful counteraction of threats and include information regarding the application of AI in the security sphere. The advancements of AI technologies are not only progressing constantly, but they also adapt to provide suitable approaches that match the new and more advanced methods of executing cybercriminal activities. But winning the journey is not that easy like having a beauty queen title. AI is critique for its specialties like algorithm bias, data privacy and strong infrastructure requirement that must be dealt to get the full advantages of AI in Securing the cyber space. It is incumbent upon the research community working in or across the SETs, industry, and policymakers to work in synergy and effectively address these challenges towards the creation of a secure IoT.
Therefore, it can be concluded that the use of AI is pertinent to protect IoT networks from cyber-attacks. Thus, the active implementation of AI-based security systems will help create invulnerable protection that is capable of further development and enhancing the security of the connected environment today and in the future. With the continuing advances seen in the field of Information technology and Artificial intelligence, internet of things security will be a future proof construct.
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Cite As
Chokkappagari R. (2024) Combating Cyberattacks on IOT networks with Artificial Intelligence, Insights2Techinfo, pp.1