By: Dadapeer Agraharam Shaik , Department of Computer Science and Technology, Student of Computer Science and technology, Madanapalle Institute of Technology and Science, Angallu,517325, Andhra Pradesh.
Abstract:
Machine learning is completely revolutionizing the network security domain as it provides enhanced features for the identification and prevention of threats. When it comes to the protection of systems and data, traditional methods of security are no longer reliable due to more developed threats. AI here uses machine learning and deep learning for data calculations where AI can search large data quantities and detect vulnerabilities to automate and enhance security solutions. In this article, the writer discusses how AI affects network security covering the advantages, disadvantages, and trends in the future.
Keywords: Impact of AI, Impact on Network Security, Security Threats, Networking.
1.Introduction
Thus, Artificial Intelligence has influenced numerous fields, such as health care, finance, and entertainment. The last but not the least; the field of network security is another important area, where AI has found greater application in the recent time. With the evolution of threats in the cyber world and the constant increase of the threats themselves society is struggling with classic approaches of security. Since computers and their capabilities are advanced, AI brings the best and efficient and unique solution regarding the improvement of the network and its security. In this context, this article discusses how AI has turned out to alter the networks security, the advantages, the downside, and the prospective of AI in network security. Today’s world can be rightfully described as digital or, more specifically, IT-oriented, and this very reality is characterized by the continually growing pace and the growing risks of cyber threats. Cybercriminals and other parties with ill intent are using techniques that are much more advanced to attain unauthorized access to the networks and steal valuable information together with precipitating malice to companies’ operations. Several recent threats that are constantly changing in their patterns and tactics are no longer easily detected by specialized traditional network security planes, which include rules and signature-based systems. This is where AI comes into play putting everyone on the same level. Through the implementation of methods related to machine learning, behavioral analytics, and other forms of artificial intelligence, threats can be identified and mitigated as they are happening and sometimes even before.
Cyber security solutions that are backed up by Artificial Intelligence can process huge quantities of information within a very short time and detect patterns, which might suggest that a network is under attack. Unlike other systems, AI can learn from each failed attempt and enhance the structure’s protection with each try.
2.SVM based Machine Learning
Separating the constraints of the packet encapsulation and dynamic port split, another method of the internet traffic classification of the class of the machine learning is the Support Vector Machine (SVM). This technique is used frequently in classification procedures and it is one of the most effective in respect of accuracy in identification of Internet traffic. Thus, it is also widely referred to as the maximum margin classifier. Statistical learning theory is the foundation on which SVM is based.
The working model of the support vector machine (SVM) based algorithm primarily encompasses the segmentation of the entire internet traffic into small application categories presuming the facets of the network flow derived from the packet headers. Therefore, the objective of this process is somewhat different from the objectives of other previously used machine learning approaches. The assessment of functional surveys performed relative to the samples of traffic, collected for the SVM, proved that using packet headers from flow parameters can provide high accuracy in terms of classification. An accuracy figure of 99. When the examined training method was biased at 42%, the tested data samples were analyzed based on the traffic present in the real-time environment of the network. Thus, no matter even if the accuracy figure reaches about 97 per cent or slightly above that, the revenue will still remain abysmally low. 17 % was gotten from the unbiased method of sampling, whereby errors were evenly spread over the different classes of traffic to be categorized[1].
The applied uses of this procedure are in classification applications such as text classification, remote sensing, image categorization, and diagnosis. It can also apply to the process of encryption for network traffic systems as there is no idea of storing several packets or payloads in the application. As far as computational complexity is concerned, the selected SVM method based on the RBF kernel functions is thought of the most efficient one. The drawback of the SVM method and other types of supervised machine learning algorithms is that a rather extensive collection of labeled samples are required for training.[2]
3.Network Intrusion Traffic: Big Data Classification Problems
According to the machine learning paradigm, the accumulation of massive traffic datasets and training their characteristics is the primary prerequisite for the prediction of an unknown intrusion attack. Large network traffic data is collected continuously, which leads to Big Data issues; this contributes to major obstacles to applying machine learning frameworks. Some of the causes are the characteristics of big data some of which include volume velocity and variety. In executing the operations of information retrieval from the labeled datasets, different machine learning tools are applied when training and validation is done. Typically, the utilization of supervised algorithms is common in the classification of network traffic data mainly for he purpose of intrusion detection such as the use of support vector machines.[2]
The guiding ideology of active defence in cyber war embodies attack and defence, self-defence and confrontation, winning and containing cyber war, and reflects the peaceful development of China’s network technology in the future.[3]
The challenges associated with managing Big Data classification are as follows: The challenges associated with managing Big Data classification are as follows:
1. Hence, the ML algorithm, learned on a given particular data set may not be suitable for some other data set. This entails that it is not effective for other data domains within the system.
2. A certain number of classes are employed in the training of an ML technique. Thus, in the case of dynamic datasets when at the same time there is a various set of classes, inaccurate traffic classification is possible.
3. However, when there is a unique learning task that an ML technique is supposed to accomplish, it will not accommodate another learning task[4].
Although the indices provided by the Big Data traffic classification strategy are useful in the prediction of anomalies or network intrusion, researchers have proposed the use of the modern representation-learning algorithms along with the support vector machines combined with Cloud Technologies and Hadoop Distributed File Systems. The issues mentioned concerning the continuity parameters are solvable by incorporating machine learning into the lifelong-learning framework[2].
Conclusion:
The application of AI in network security is changing the ways organizations protect and secure their networks. Whenever the threats posed by cyberspace increases in terms of its complexity or frequency conventional security measures become utterly useless. AI provides a dynamic and strong application since it incorporates the additional analytical functions and machine learning mechanisms that can quickly identify and counter threats. It also overcomes conventional techniques, making AI based systems to learn in a relatively shorter time and perform high-speed and energy-efficient data processing and find out patterns and anomalies that indicate possible breaches. This means that while basic security operations may be handled by machines, human analysts work on other critical tasks, hence increasing security efficiency.
However, when it comes to application of AI in Network Security, few of the challenges include; Large Data set for Input for the Model AI, False Positive and False Negative, and adversarial attacks on AI Based systems. However, the AI technologies’ deployment demands expenses and specialized knowledge from the endorsing organization. Nevertheless, the principal trends of the network security development presuppose the combination of AI with conventional approaches. This can only get better with the footsteps of the new advanced AI technology that is poised to give better and efficient methods of securing networks. The continuous advancement of AI security will be paramount in defining the future of cybersecurity hence the need to adopt them in organizations amid new emergent threats.
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Cite As
Shaik D.A. (2024) The Impact of AI on Network Security, Insights2Techinfo, pp.1