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:
AI in cybersecurity has brought a change especially though the application of analytical prediction in cybersecurity. This approach is nearly ideal because AI is programmed to scan through huge data sets and determine various risks before they occur. Thus, the application of predictive analytics offers a step change in converting tactical cybersecurity measures into strategic ones. This paper discusses what predictive analytics entails, how it is carried out, as well as the importance of the application in cybersecurity.
Keyword’s: Artificial intelligence, Predictive analysis, Cyber Threat, Machine Learning.
1.Introduction
The emergence of artificial intelligence (AI) has influenced the numerous sections of the economy; however, cybersecurity is one of the most sensitive regions that have embraced the change. There are a number of general and traditional approaches that are commonly used in cybersecurity solutions, which are typically reactive, meaning that a set of actions is implemented only after the identification of a threat. Nevertheless, this method is relatively ineffective at the present given the complexity and future development of cyberspace threats. Predictive analytics, which uses AI, provides an opportunity to transition to preventive security measures rather than responding to the threats. With the help of AI, large sets of data are analysed with the help of which certain patterns as well as their deviations that can be relative to various threats are identified, which makes it possible to take actions in advance. That transition also improves threat identification efficiency and the ability to track new cyber threats without interruptions. Advanced by the incorporation of machine learning and other AI methods in the predictive analytics, its effectiveness has similarly increased offering conspicuous tools for threat anticipation and mitigation. This article discusses the strategies and advantages of predictive analytics in cyberspace and describes how AI techniques are changing the field of cybersecurity.
2.Predictive Modelling for Cyber-Attack Prediction
It is a part of artificial intelligence, which deals with patterns and anomalies of the data to probable cyber threats and attacks. It uses the analysis of input data and mathematical algorithms to predict future adverse events ahead of time as is done in case of weather forecasting. While distributed machine learning involves partitioning the data set to be used in training and applying computations over the separated chunks of data, federated learning enables several computing nodes within a network to work together by distributing training data and training the ML model. This approach minimizes the time that is taken to rebuild models when there is a need to update the nodes since the update can be made during the training process. A particular method called FedAvg has been compared to some of the centralised learning methods and it was observed that the former in general attains higher overall accuracy ; however , the latter have the advantage when data are not non-independent and identically distributed (non IID). Through federated learning, the predictive models would become more innovative and skilled in the recognition of the cyber threats’ presence while mitigating learning from more data sources impinging privacy.[1]
The process of predictive modelling typically involves several steps: The process of predictive modelling typically involves several steps:
1. Data Collection: Gathering associated data from different sources, which in this case are the sensors, databases and sites on the internet.
2. Data Pre-processing: Coalescing and pre-processing of the data for analysis. This includes data cleaning which involves treating of missing values, eliminating of outliers and restructuring of variables when need arises.
3. Feature Selection: Selecting a subset of the best variables/ features that are most likely to affect or be related to the outcome as was predicted.
4. Model Training: Adjusting the parameters of the chosen model through statistics as a means of enhancing the ability of the model in predicting errors when used data from the past.
5. Model Evaluation: Validating using methods and techniques the reliability and accuracy of the trained model developed.
6. Prediction: Utilizing the learnt model in making predictions as per new or unseen data[2].
Currently, business industries and organizations from different fields apply predictive modelling to solve problems, analyse data, and make decisions and improvements. AI techniques, based on the organization’s past experiences, can help organizations in finding the opportunities for improvement, risks, likely future scenarios, and performance improvement.
3.AI in reference to Cybersecurity
Given the geometrical growth of threats related to the daily emergence of new malware, it is impossible in practice to imagine that threats can be successfully counteracted using the efforts of operator’s analysts alone. There is a need to initiate algorithms that enable the initial assessment that often forms part of triage; consequently, deprive us of the capability to filter the threats to be presented to the cybersecurity personnel to respond adequately to ongoing attacks.
It means that it is crucial to be able to reply actively and flexibly to the changes characterizing the context connected with the existence of original risks. This also means that the analysts are not only the custodians of the tools and methods of cybersecurity but also the holders of a correct understanding and assessment of the results presented by AI and ML computations[3].
It is thereby imposed to the cybersecurity professional to explain the reasoning of the algorithms and move ahead to fine tune the learning phases dependent on the outcomes and the intended goals.
Some of the tasks related to the use of AI are as follows: Some of the tasks related to the use of AI are as follows:
- Classification: It is one of the key actions in the work of cybersecurity that forms a certain structure and aims at solving tasks. It is applied for correct classification of similar attack types – different program codes of the same malware type, that is, having the same features and behaviour, even if they have different signatures (speak about polymorphic viruses, for example). Similarly, it is necessary to distinguish between the relevant emails and the spam, unnecessary messages.
- Clustering: While classification focuses on the placement of objects into predetermined varieties, the identification of classes, which belong to the samples When, information about classes is not initially given (this is a common objective, as discussed earlier, of unsupervised learning)[4].
- Predictive analysis: Thus, through the utilization of NNs and DL, threats can be recognized during a process. For this purpose, the highly dynamic strategy has to be implemented to stabilize and let algorithms learn on their own[5].
Possible uses of AI in cybersecurity are as follows: In a way, this is as it should be, for Levinas’s thought is most immediately relevant to philosophy as a discipline in its original home.
- Network protection: The use of ML makes it possible to realize very developed IDSs, which are to be used in the area of the network perimeter protection.
- Endpoint protection: Ransomware can be sufficiently recognized if algorithms are used that familiarize themselves with the behaviors characteristic of such viruses, which excludes the drawbacks of conventional antiviruses.
- Application security: The some of the most prevalent and dangerous threats types for web applications are SSRF attacks, SQL injections, Cross-Site Scripting and Distributed Denial of Service. Such are all the types of threats that could be effectively addressed and managed through the help of AI as well as ML tools as well as algorithms.
- Suspect user behaviour : Detection of fraud or compromization of applications by the unscrupulous persons at the very time it is happening is one of the areas where the DL is already finding applications.[6]
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Cite As
Shaik D.A. (2024) Predictive Analytics in Cybersecurity Using AI, Insights2Techinfo, pp.1