RESEARCH

The Role of Artificial Intelligence in Cybersecurity: Analyzing the Application of Machine Learning for Anomaly and Attack Detection in Networks

The integration of artificial intelligence (AI) and machine learning (ML) has transformed the way of detecting and combating cyber risks. This paper provides an extensive discussion on the application of AI and ML in the identification of anomalies and attacks in network systems.

Abstract

The integration of artificial intelligence (AI) and machine learning (ML) has transformed the way of detecting and combating cyber risks. This paper provides an extensive discussion on the application of AI and ML in the identification of anomalies and attacks in network systems. When possessing more sophisticated algorithms, especially deep learning ones, AI-based models can analyze large amounts of data and detect patterns and specific trends in data, which makes it simpler to detect anomalies than in traditional approaches. The paper examines the Machine Learning algorithms' effectiveness of models based on ensemble and adaptive ones, emphasizing the application of ML technologies to strengthen cybersecurity. Nonetheless, there are drawbacks of the technology, including; false positives, adversarial attacks, and privacy issues. The

Results further suggest the need to enhance AI and ML in extolling to these issues and strengthening cybersecurity against emergent threats.

            Machine learning algorithms, primarily ensemble and adaptive models, have proven effective in enhancing cybersecurity. Ensemble models combine multiple learning algorithms to improve prediction accuracy, while adaptive models adjust their learning process based on new data. This adaptability is critical in cybersecurity, where threats are constantly evolving. ML models can predict and respond to emerging threats more effectively than static, rule-based systems by continuously learning from new attack patterns. Despite their advantages, AI and ML in cybersecurity have drawbacks. One significant challenge is the occurrence of false positives, where benign activities are mistakenly flagged as threats. This can lead to unnecessary alarms and potentially hinder legitimate operations. Adversarial attacks pose a critical risk, where attackers deliberately manipulate input data to deceive AI models, causing them to misclassify malicious activities as safe. Additionally, using AI and ML raises privacy concerns, as these technologies often require access to vast amounts of sensitive data to function effectively.

Keywords: Anomaly detection, Information security, Artificial Intelligence, Neural networks, Internet security.

Introduction

In the current technological advancement landscape, there has been a rise in network traffic and cybersecurity threats, hence the need for better security systems. Standard preventive approaches, which rely on some scan-and-identify approach or rule-based system, are becoming wholly insufficient when it comes to new and far more acute threats that are being seen more and more. One of the most interesting trends in the field of cybersecurity is the use of Artificial Intelligence and Machine Learning in this sphere. 

AI and ML have proven to be useful in the detection of anomalies in network systems where the technique involves analyzing a large amount of data in real-time so as to look for any signs or actions that appear peculiar and could signal a hack [3]. Of these, ML has been especially promising, especially deep learning, since it can identify very complex patterns within data. That has necessitated the evolution of smarter and more effective security mechanisms that can be able to counter threat scenarios as they evolve. Thus, this paper seeks to analyze the use of AI and ML, particularly for network anomaly detection in cybersecurity. The research aims to explore and review the literature available about AI in cybersecurity to understand the present scenario of AI in cybersecurity and highlight the areas where the researchers need to develop new technologies more urgently. The overall objective is to help devise the further evolution and improvement of the entirety of cybersecurity contours that could help withstand new and diverse threats.

Related Work

AI and, particularly, ML have been employed and analyzed in cybersecurity and cybersecurity-related tasks for years, with a specific focus on anomaly detection. Salih et al. [1] conducted a survey in which they discussed the use of AI, ML, and deep learning in the context of the detection of cyber-security threats; they underlined that these technologies are very significant to current security architectures. The survey shows how intelligent algorithms, specifically machine learning methods, are used to identify and analyze threats in real time to outrun other conventional methods.

The effectiveness of ML in cybersecurity lies in its ability to process vast amounts of data and uncover hidden patterns that might elude human analysts or traditional software. Techniques such as supervised learning, where algorithms are trained on labelled datasets, and unsupervised learning, which identifies patterns without prior knowledge, are employed to detect known and unknown threats. Deep learning, a subset of ML, further enhances this capability by using neural networks to analyze complex data structures, improving threat detection accuracy. Applying AI and ML in cybersecurity is challenging. The reliance on large datasets for training models raises concerns about data privacy and the potential for adversarial attacks, where malicious actors manipulate inputs to deceive AI systems. Additionally, the dynamic nature of cyber threats requires continuous model updates and retraining to maintain effectiveness.

Yaseen [2] provides a more detailed description of network anomaly detection and ML and the importance of adaptive models to the process as well as the use of ensemble methods. Of crucial importance is that the proposed ML models are flexible so that they can be easily modified in response to new cyber threats. Ensemble techniques are accentuated by taking into consideration the Random Forests because of their great effectiveness as they incorporate characteristics of a number of models and thereby minimize the likelihood of false positives and minimum false negatives. Due to this integration of ML techniques, there has been an enhancement in the accuracy and reliability of Anomaly detection systems.

Li [3] addresses the role of Artificial Intelligence in detecting and combating cybersecurity threats but, at the same time, underlines the possibility of using AI as the target of such threats. The study calls for better security measures to be put in place to ensure that such attacks do not impede the functionality of AI models as they are trained. This duality of using AI underscores the problems of applying AI to cybersecurity and the need to create advanced AI systems that are resistant to sabotage.

       The reliance on AI also introduces significant risks. As AI systems become central to cybersecurity strategies, they become attractive targets for cyber attackers. Adversaries may attempt to manipulate AI models through techniques such as adversarial attacks, where subtle modifications to input data can lead to incorrect model predictions. These attacks can undermine the effectiveness of AI in identifying threats, thereby compromising the overall security framework. Additionally, if AI systems are sabotaged or corrupted, their functionality may be impaired, potentially leading to severe security breaches. Li's study calls attention to the need for developing advanced security measures specifically designed to protect AI systems. This involves implementing robust security protocols, such as encryption and access controls, to safeguard AI models from tampering. Moreover, continuous monitoring and validation of AI systems are essential to detect and address potential vulnerabilities before they can be exploited.

Recently, Abdullahi et al. [4] conducted a systematic review of AI techniques for cybersecurity attacks in the IoT. Getting down to the review of the AI techniques used in IoT security, the author mentions the large datasets that emanate from IoT devices. As described in the study, deep learning is highlighted as an especially useful method for analyzing such data and identifying outliers. Still, the study also discusses the issues regarding data privacy and directions for future research.

Shaukat et al. [5] have also carried out a survey on machine learning techniques in cybersecurity with emphasis on the findings in the last decade. The survey also divides the ML techniques based on the application mode in the field of cybersecurity, for instance; intrusion detection, malware classification, and threat intelligence. The authors elaborate on the latest developments in the field of ML technologies and their increasing roles in preserving network security. Further mention about the difficulties of incorporating these involving technologies into the confirmative security models.

Nassif et al. [6] provide a comprehensive review of the ML methods for anomaly detection, along with a description of the methods and their efficiency in different contexts. The strong points of the review are the focus on feature engineering tasks and on the use of deep learning to enhance the accuracy of an AS. But it also states the issues that involve these techniques in practical contexts, with respect to the feasibility and profitability of ML models in processing huge amounts of data.

One of the review’s notable strengths is its focus on feature engineering. Feature engineering involves creating relevant input variables that can significantly influence the performance of ML models. By carefully designing and selecting features, practitioners can improve the ability of anomaly detection systems to identify outliers with higher precision. Nassif et al. highlight that effective feature engineering is crucial for capturing the underlying patterns in data, thereby enhancing the overall accuracy of the anomaly detection process. This insight is valuable for both researchers and practitioners aiming to optimize their models. The review also underscores the role of deep learning in advancing anomaly detection capabilities. Deep learning techniques, particularly neural networks, have shown remarkable performance in identifying complex patterns and anomalies in diverse datasets. By leveraging hierarchical feature representations, deep learning models can achieve higher detection accuracy compared to traditional methods. Nassif et al. provide compelling evidence of how deep learning enhances anomaly detection systems, making them more robust and capable of handling intricate data structures.

Method

This research study examines the ability of machine learning (ML) techniques to identify anomalies in IoT networks with the IoT-23 dataset from the Avast AIC Lab. The study is systematically divided into three key stages: data pre-processing, the use of algorithms in processing data and in analyzing the result. In the course of data pre-processing, the dataset comprised of the malware captures as well as the benign anomalies passed through crucial stages. These comprised assembling of the required files, examination of the main variables which relate to anomaly detection, cleaning involving handling missing values, and reshaping of the structure to format the data for the ML algorithms. The dataset was then split into 80:20, the 80% of which formed the training data and the 20% of the testing data.

Three ML algorithms were chosen for implementation: SVM, Random Forest and Naive Bayes. The following algorithms have been used as they have many benefits when used in classification problems [3]. In SVM, points in n-dimensions having features were mapped then the hyperplane was determined optimal for differentiating between the classes; it was most useful for studies based on the problems that involve only two classes. Random Forest, being very stable, aggregated multiple decision trees, which were trained in parallel and applied different learning data sets to improve classification performance where the result was made via a voting system. The Naive Bayes was employed for its scalability, and based on Bayes' Theorem, it was effective in cases such as text classification.

The experimental setup was performed on an operating system, Windows 10, with Intel Core i3 as the central processing unit and NVidia as the graphics processing unit; the programming language used was Java 10. Evaluation metrics were Accuracy, F1-score, Recall, and Support which will allow characterizing each model according to the ability and frequency in which it can identify anomalies without going through false positives and false negatives too often[8]. To determine which of the models was the most efficient for anomaly detection in an IoT network, the paper ended with a comparison of the outcomes.

Results

During the experimental analysis of the IoT-23 dataset and using different ML algorithms, it was found that the proposed algorithms are efficient in recognizing anomalies in IoT networks. As shown in the previous performance metrics table, the Random Forest algorithm was remarkably accurate, having achieved 99%. 5 %, which exceeded the Support Vector Machine (SVM) classifier of 99 %. 4%, Decision Tree with 96. 3%. For the classification of a single document, the best designs are KNN with 84—8%. The True Positive rates were particularly impressive for both Random Forest and SVM, at 0. 995 and 0. At 994, respectively, this confirms their accuracy in detecting irregularities.

The precision values there finally emphasized the hegemony of Random Forest, which, with a value of 1, correctly observed all instances. This brings a confirmation of features of variance and invariance; hence the conclusion that the Random Forest model made correct positive predictions with no cases of false positives [8]. On the other hand, as concerns the precision, the Naive Bayes' performance proved utterly disappointing with a value of 0. 78 eight which is relatively lower than the other features in categorizing between benign and malicious activities.

F-Measure Analysis

F-Measure, which measures the precision as well as the recall, was also the highest, with Random Forest and SVM both having 0. 99, which shows that they have a balanced performance of identifying true positives and negatives with minimized false positives[8]. The Decision Tree was also efficient but a bit less stable and had an F-Measure equal to 0. 945. The outcomes of this study indicate that Random Forest might be an excellent algorithm for the IoT arena in learning anomaly instruction with higher accuracy and reliability.
                               

Discussion

 As demonstrated in the findings of this research study and many other related works, ensemble methods such as Random Forest perform very well in classification situations, especially in cybersecurity. The findings of the study demonstrate that Random Forest has a high accuracy and a very low standard deviation, and in IoT applications, there are always diverse and noisy signals [8]. Hence, the Random Forest model achieves simplicity by aggregating decisions from several trees, and this makes the model more adaptable to the complexities of detecting an anomaly than the other ML models [5]. The result of SVM, which was as close as to the Random Forest, has shown its qualification in binary classification. However, this slight gain in the performance of Random Forest is attributed to the fact that it is more efficient in handling models with non-linear relationships. Compared to other models, Naive Bayes is not very effective for large datasets; however, its accuracy could have improved here, probably due to the fact that Naive Bayes assumes features to be independent, whereas they are not in network data[5]. Hence, these results not only validate the literature but also state that for practical use in IoT security, ensemble models like the Random Forest should be used. As for future work, the deep learning method needs to be applied in combination with Random Forest to achieve higher levels of detection accuracy and solve the problem of adversarial attacks that will persist.

Conclusion

In conclusion, this study proves how AI and machine learning, used specifically with the Random Forest algorithm, can improve IoT anomaly detection. Random Forest algorithm has better accuracy and precision in detecting anomalies in network traffic. The F-Measure scores also testify to its effectiveness in the threat detection process for the cybersecurity domain. Future trends in this domain should, therefore, involve ways to enhance the robustness of these models, especially using deep learning techniques as well as addressing issues such as adversarial attacks and data leakage. Further, attempts should be made to enhance the computational efficiency of these models in order to construct relevant models for real-time practices of cybersecurity.

References

[1]Shaukat, K., Luo, S., Varadharajan, V., Hameed, I. A., & Xu, M. (2020). A survey on machine learning techniques for cyber security in the last decade. IEEE Access, 8, 222310-222354. https://ieeexplore.ieee.org/abstract/document/9277523/

[2] Salih, A., Zeebaree, S. T., Ameen, S., Alkhyyat, A., & Shukur, H. M. (2021, February). A survey on the role of artificial intelligence, machine learning and deep learning for cybersecurity attack detection. 7th International Engineering Conference “Research & Innovation amid Global Pandemic"(IEC) (pp. 61-66). IEEE. https://ieeexplore.ieee.org/abstract/document/9476132/

[3] Yaseen, A. (2023). The role of machine learning in network anomaly detection for cybersecurity. Sage Science Review of Applied Machine Learning, 6(8), 16-34.

 [4]  Li, J. H. (2018). Cyber security meets artificial intelligence: a survey. Frontiers of Information Technology & Electronic Engineering, 19(12), 1462-1474. https://ieeexplore.ieee /article/10.1631/FITEE.1800573

[5]  Abdullahi, M., Baashar, Y., Alhussian, H., Alwadain, A., Aziz, N., Capretz, L. F., & Abdulkadir, S. J. (2022). Detecting cybersecurity attacks in the internet of things using artificial intelligence methods: A systematic literature review. Electronics, 11(2), 198. https://www.mdpi.com/2079-9292/11/2/198

[6] Nassif, A. B., Talib, M. A., Nasir, Q., & Dakalbab, F. M. (2021). Machine learning for anomaly detection: A systematic review. Ieee Access, 9, 78658-78700. https://ieeexplore.ieee.org/abstract/document/9439459/

[8]Sahu, N. K., & Mukherjee, I. (2020, June). Machine learning-based anomaly detection for IoT network :( Anomaly detection in IoT network). In 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) (pp. 787-794). IEEE. https://ieeexplore.ieee.org/abstract/document/9142921/

RESEARCHER

Dr. Anar Israfilov

RESEARCH ABSTRACTS

Backed in
Research.

About the AI Research Centre

You can download our research article as a pdf below and examine it in detail.