By: Dhanush Reddy Chinthaparthy Reddy; Department of Computer Science and Artificial Intelligence, Madanapalle Institute of Technology and Science, Angallu (517325), Andhra Pradesh
Abstract
In the modern world of rapid digitalization, the use of artificial intelligence (AI) in the processes of network safety has become one of the key developments. Being a beginner’s guide, this paper focuses on the central importance of AI in enhancing the safety of the networks. The first part gives a brief overview of core subjects about AI and how it is used to boost security in the network, such as machine learning, deep learning, or natural language processing. Analysing the relations, the paper reveals how Ai’s solutions recognize, expose, and counteract cyber risks.
The use cases of AI in network safety, details aspects and presents implementational plans of how and why AI is effective in combating advanced cyber threats, protecting against data leaks, and maintaining the safety of the networks. AI increases the effectiveness of the threat detection and response creating more strength to the network protection through decreasing the time and efforts spent on security issues.
Nevertheless, there are still difficulties associated with the use of AI in networks safety. The paper shares key concerns like data protection, machine learning algorithms manipulated and the requirement for accountability and explain ability of the applied Artificial Intelligence. This is because the ethical issues concerning AI implementation regarding network safety are discussed comprehensively in order to avoid any questionable practices.
In sum, this guide will help acquaint novices with the basic facts they need to know to exist in this new world of AI-assisted network protection. Consequently, through analysing the strengths and weaknesses of applying AI in this regard, the audience is more likely to grasp the developments in the sphere of cybersecurity and further attempts at building a safe virtual world. Considering this, this paper seeks to reiterate the need to constantly advance and employ ethics in the advancement of AI in the enhancement of network security.
Keyword: Network, Artificial Intelligence , Safety
Introduction
Recent years saw high demand for the deep neural networks in life critical applications including self-driving car, virus/malware detection, and aircraft collision avoidance system. This can be attributed to the fact that these models offer results that are almost real You have seen that the models are applied giving very close to real results. Despite their success, a fundamental challenge remains: to argue that the learning algorithms particularly deep neural networks do what is expected of them. This has proved to be a big issue especially in the recent past after it was ascertained that even the best of the neural networks has massive flaws in adversarial examples. The basic idea behind formation of adversarial examples is that small levels of disturbance are added to the correct entry level which the network is able to recognize, with another entry level that is different from the original one. Several kinds of disturbances as is shown above can then be employed to generate adversarial examples as required. Such typical attacks as FGSM and the so-called brightening can cause a considerably negative impact on the used neural network. For example, the FGSM attack moves an image by adding the noise vector combined with ‘step size’ to the original image; the brightening attack move an image following the instruction to change all pixels, which are bigger than certain value, to the maximum possible value. Physical attacks are always a problem, and it becomes even worse when safety-critical systems use neural networks; attacks can be physically performed, and toward the neural network which is only available as a black box. To solve these issues, the modern research focuses neural network’s robustness enhancement with the emphasis on the local robustness, meaning that all points, belonging to the epsilon-neighbourhood of the certain input vector should be classified as the same class. Some of the previous studies have attempted to solve the problem by modifying some of the processes of training the specific kind of the network in question while the other studies have devised techniques to show the non-robustness or to arrive at the robustness of small fully connected feedforward networks. However, to the present, there is no essentially well-performed sound analyser compatible with convolutional networks, one of the most employed kinds of architecture.
Overview of AI in Network Safety
Cybersecurity with the help of artificial intelligence is the application of AI-derived technologies to guard against cybercrimes with an aim of safeguarding Internet-connected systems and information. Machine learning in AI discovers patterns, outliers, and weaknesses that signify the existence of cybersecurity threats or threats of any type. AI can develop solutions based on the newer and emerging threats thus learning from previously collected data and occurrences. It aggregates data from different sources like; threat intelligence feeds, security logs, and network traffic to create a holistic overview of the security posture. AI helps to avoid overload of information and corresponds important data, which helps to security analysts to concentrate on important problems. Machine learning techniques like behaviour analysis, natural language processing, and image processing help in the prevention of a thousand cyber events organisations face daily. Also, it separates the actions of legitimate users starting with using authorized accounts and good bots from the actions of unauthorized users and malicious bots. Data, analytical and learning tools, and continuous simulation, AI evaluates the probability and consequences of security incidences, causes and risks, and recommended control measures. AI performs routine functions such as system monitoring, malware scanning, and incident report that drain time and focus away from people. Moreover, AI prescribes and executes the handling of security-related occurrences through the utilization of predefined guidelines, procedures, and operational processes. Cybersecurity with the help of artificial intelligence is the application of AI-derived technologies to guard against cybercrimes with an aim of safeguarding Internet-connected systems and information. Machine learning in AI discovers patterns, outliers, and weaknesses that signify the existence of cybersecurity threats or threats of any type. AI can develop solutions based on the newer and emerging threats thus learning from previously collected data and occurrences. It aggregates data from different sources like; threat intelligence feeds, security logs, and network traffic to create a holistic overview of the security posture. AI helps to avoid overload of information and corresponds important data, which helps to security analysts to concentrate on important problems. Machine learning techniques like behaviour analysis, natural language processing, and image processing help in the prevention of a thousand cyber events organisations face daily. Also, it separates the actions of legitimate users starting with using authorized accounts and good bots from the actions of unauthorized users and malicious bots. Data, analytical and learning tools, and continuous simulation, AI evaluates the probability and consequences of security incidences, causes and risks, and recommended control measures. AI performs routine functions such as system monitoring, malware scanning, and incident report that drain time and focus away from people. Moreover, AI prescribes and executes the handling of security-related occurrences through the utilization of predefined guidelines, procedures, and operational processes.
Applications of AI in Network Security
An initial reference model for AI-SEC, whose mission is to act as an information infrastructure and proposal source for specialists in the field and cybersecurity researchers, primarily regarding AI-related technologies. These include K-Nearest Neighbour, Naïve Bayes, Random Forests, Adaptive Boosting, RNN, LSTM, CNN & Hidden Markov Model that assist a lot in work like intrusion detection analysis, attack classification, DDoS detection & analysis & to curb cyber terrorism. Possibilities that Analytics-driven structuring can be utilized in several areas of applications have been described, starting from the risk assessment to Abnormality detection may lead to a phishing risk. Analogues of AI in cybersecurity have been discussed; the analysis of the role of MI in the cybersecurity of IT systems for decision-making in it; the use of AI for the determination of risks in the network system and the making of intricate decisions. Since AI can have a database consisting of malware and threats, the files or behaviours can be classified by AI concerning supervised and unsupervised machine learning algorithms. The technique of neural networking which depicts each neuron as an entity in N – dimensional space with neighbour neurons is helpful in detection of IP traffic when integrated with clustering methods. Deep Learning can diagnose issues before they occur by utilizing mathematical intelligence to calculate behaviours. Therefore, the application of AI technology contributes to multi-level risks’ identification and solutions focusing on different forms of intrusion and detection abilities, fewer false alarms, and an integration of predictive analysis to the improvement of Internet Information Security. Expert systems and a range of neural networks including ANN and DNN continues to be developed. Cyber Security and Artificial Intelligence has also been explained in relation to social media and specifically in relation to the machine learning techniques applicable in those platforms. Thus, sentiment analysis can detect views of users and find the spread of dangerous illnesses or human trafficking. Such security and privacy issues include access control approaches as well as privacy-conscious systems and machine learning makes it possible to identify fake news and the presence of malwares in the social platforms.[1]
The machine learning-based intrusion detection system operates through a structured framework that includes four main modules: Machine learning, network packet capture, misuse rule processing and data pre-processing is also identified as an important area[2]. In its core, the described system is based on the machine learning module that subjects a learning machine to detect intrusions. The network packet capture module is intermediate in capturing data packets in various protocol levels; it employed a tool known as packet sniffer. This module sees to it that the efficiency of the system is achieved. The misuse rule processing module addresses rule-based detection through comparison of collected information with the model database containing information of network intrusions to guarantee accuracy and efficiency. In cases of the network capture module, the data pre-processing module is responsible for processing the huge volumes of raw data packets into formats that can be checked for further analysis.
Training of the Elman neural network learning module applies the back propagation through time (BPTT) algorithm. This process is similar to Back Propagation (BP) algorithm in which the network’s weight layers change to match to normal mode of network packets. Further, there is the neighbouring classification module developed based on the effective robust support vector machine with involved weight gradient. This module gets and averages the k biggest similarities based on the traditional neighbour classification algorithm and then compare it with the threshold to know if the state of the host system is abnormal[3].
Conclusion
The integration of AI into network security represents a paradigm shift, offering unprecedented capabilities in threat detection, response, and prevention. While challenges remain, the continued advancement of AI technologies and their thoughtful application promise to create more secure and resilient networks. As AI evolves, it will undoubtedly play an increasingly pivotal role in safeguarding our digital infrastructure, shaping the future of cybersecurity for years to come.
References
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- Rahaman, M., Lin, C. Y., Pappachan, P., Gupta, B. B., & Hsu, C. H. (2024). Privacy-Centric AI and IoT Solutions for Smart Rural Farm Monitoring and Control. Sensors, 24(13), 4157.
Cite As
Reddy D.R.C. (2024) AI and Network Safety: A Beginner’s Guide, Insights2Techinfo, pp.1