By: Dhanush Reddy Chinthaparthy reddy, Department of Computer Science and Artificial Intelligence, student of Computer Science and technology, Madanapalle Institute of Technology and Science, Angallu, 517325, Andhra Pradesh
Abstract:
Network security is being boosted by Artificial Intelligence (AI) through the use of complex strategies aimed at early identification of threats and their counteraction. Due to the capability of applying machine learning & data analytics, AI can discover any anomalous patterns and security threats in the initial stage. These systems can prevent actions being taken in response to received incidents and prevent overreactions which diminish response time and resulting damage. Also, AI improves prospects for the prediction of security risks and recommending preventive actions. The use of AI is critically important in modern network security to build better and stronger protection in the provided digital world.
Keywords: Artificial Intelligence, Networking, Network Security.
1.Introduction:
In the twenty-first century, networks represent the lifeline through which people transmit messages and interact, share, and transfer data; at the same time, networks can be securely regarded as prospective cybersecurity threats. As these threats become more advanced, conventional security solutions are usually inadequate. This is the place where the concept of Artificial Intelligence (AI) comes to aid changing the way networks are secured. The rate detecting and responding to change patterns from big data is defined as AI, and it provides network security systems with robust capabilities to protect networks. Thus, the leveraging of AI in threat detection and response, in predicting possible threats, and in the search for the best approach to handling them is not just improving networks’ safety – it is revolutionizing it. This introduction aims at explaining how AI is changing the world, and specifically, how it contributes to the protection of networks and their overall performance in the face of current threats.
2.AI In Network Security
Industrial Automation and the Concept of Smart Factories
Industrial automation has paved the way for the development of smart factories, leveraging artificial intelligence (AI) and the Internet of Things (IoT). Key components of this transformation include adaptive supply chain management, human–robot integration, production quality enhancement, and predictive maintenance. These advancements are integral to the broader framework of industrial automation and cyber-physical systems (CPSs).
A vast array of sensors is deployed in industrial settings to facilitate effective and efficient automation. However, this extensive deployment brings about challenges such as interoperability, device heterogeneity, big data processing, data storage, energy management, and security.
Recent advancements in internet technologies, including IoT, software-defined networking (SDN), and cloud computing, have made it possible to realize smart homes, smart healthcare systems, smart security, industry 4.0, and CPSs. The compact size, low cost, and integrated features of IoT devices have enabled their widespread deployment across various sectors, from industry and commerce to home appliances[1].
According to industry reports, there will be a significant increase in the number of connected devices, with projections suggesting almost 30 billion devices will be operational in various domains. This large-scale deployment of IoT brings forth serious concerns. A major issue is the lack of industry-oriented standards, leading to problems with interoperability, compatibility, and heterogeneity among devices.
Security and privacy remain top priorities, especially for confidential data belonging to private individuals and corporate users. As the digital landscape expands, protecting digital assets and content is crucial. Organizations require actionable insights and scalable solutions to secure employee devices, IoT connections, infrastructure, and proprietary data.
The research community has been actively working to address these security challenges. Efforts have been made to enhance security aspects such as mutual authentication, integrity and confidentiality, and privacy protection. These advancements are critical in ensuring the safe and secure operation of smart factories and other IoT-enabled environments.[1]
3.How AI Enhances Network Security
• In 2005, the term “Internet of Things” was officially proposed by the International Telecommunication Union during the World Summit on the Information Society. IoT refers to a distributed network that, through wired and wireless communication technologies, interconnects the Internet with multi-sensory devices and systems including, but not limited to, sensor networks, RFID devices, barcode and QR code devices, and global positioning systems. This integration can let embedded systems communicate with and interconnect each other, facilitating a smooth flow of information.
Technical Paths in the Development of IoT
The development of IoT has been following three major technical paths:
Sensing, Identification, and Authentication Technologies: These technologies are at the heart of IoT. As the nerve endings of IoT, sensors are crucial in detecting and measuring physical phenomena. While many general-purpose sensors have been popularized, on the high-end, there have been large improvements for dedicated sensors. Identification and authentication technologies ensure that devices and data sources are recognized and verified.
Transmission and Communications Technologies: These technologies ensure the reliability and efficiency of IoT. The huge amounts of data that are collected by IoT devices need to be transmitted and aggregated to central nodes or processing units[2]. Improvements in wired and wireless networks, including cellular networks, have facilitated large-scale IoT data transmission, making it more convenient, reliable, and secure.
Data Computing and Processing Technologies: These are key technologies that provide applications and services for IoT data. The IoT application requires many information nodes to perceive in real-time and feedback intelligently. Artificial intelligence, cloud computing, and other data computing and processing technologies have strongly pushed forward the development of IoT in intelligence and effectiveness, thus resulting in the IoT capacity of offering advanced application services.
IoT as a Solution for Specific Applications:
Along with the technological development and growth in application areas, IoT has grown to become a specialized set of solutions for the corresponding application. It aims at integration and innovation of solutions where the Internet is combined with the real world by providing intelligent interaction. Applications of IoT range from manufacturing to energy management, for example, smart grids, urban life, for example, smart cities, personal healthcare. IoT Architecture
IoT typically follows an entity-based architecture, divisible into three layers:
Terminal Perception Layer: This is basically the layer where devices/sensors collect data from the physical environment. It is the lowest layer of the IoT architecture, which provides a base for the detection and measurement of various parameters.
Network Transport Layer: This layer is responsible for sending the collected data into central nodes or processing units. It includes both wired[3] and wireless technologies in communication that assure effective and reliable data transmission.
Application Service Layer: This layer processes the transmitted data and allows users intelligent applications and services. Advanced computing technologies are used in analyzing and interpreting data to give real-time feedback and solutions.
This can be seen in how such integration of layers provides a smooth flow of information, hence facilitating intelligent interaction between the physical and the digital worlds. This architecture underlines the interactive relationship among various entities involved in the IoT, showing their interconnection and interdependence.[4-8] [2]
Conclusion
AI can help in the early detection and combating of threats, which is very important for network security especially in the current complex interconnecting networks. With the help of machine learning and big data analysis and other AI tools, networks can detect and eliminate threats to security in advance and respond faster in case of an attack. AI integration with the network increases the ability to predict and prevent cyber threats and provides powerful protection of industrial systems and IoT space. The continually developing world of AI means it will be critical in the formation of secure and reliable networks, to guarantee the safety of the transmission of data relatively to the proactive measures of cyber threats.
Reference:
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Cite As
Reddy D.R.C. (2024) Safer Networks with AI: The Next Generation of Security, Insights2Techinfo, pp.1