Machine Learning-Based Ensemble Network Security System

Wagle, Prashanth P.; Rani, Shobha; Kowligi, Suhas B.; Suman, B. H.; B, Pramodh.; Kumar, P. Rajendra; Raghavan, Srinivasa; Shastry, K. Aditya; Sanjay, H. A.; Kumar, Manoj; Nagaraj, Kalyan; Subhash, Chavan Aniket

doi:10.1007/978-981-16-3342-3_1

Cited by 2 publications

(4 citation statements)

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“…However, the precision and recall values are not available. With an accuracy of 82.00%, an F1 score of 82.50%, a precision of 85.00%, and a recall of 85.00%, reference [35] introduced the DSSTE method. Reference [36] applied an NB approach and achieved a high accuracy of 97.14%, a stellar F1 score of 97.94%, and a precision of 96.72%.…”

Section: Comparative Analysismentioning

confidence: 99%

Intrusion Detection System with Customized Machine Learning Techniques for NSL-KDD Dataset

Zakariah,

AlQahtani,

Alawwad

et al. 2023

CMC

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Modern networks are at risk from a variety of threats as a result of the enormous growth in internet-based traffic. By consuming time and resources, intrusive traffic hampers the efficient operation of network infrastructure. An effective strategy for preventing, detecting, and mitigating intrusion incidents will increase productivity. A crucial element of secure network traffic is Intrusion Detection System (IDS). An IDS system may be host-based or network-based to monitor intrusive network activity. Finding unusual internet traffic has become a severe security risk for intelligent devices. These systems are negatively impacted by several attacks, which are slowing computation. In addition, networked communication anomalies and breaches must be detected using Machine Learning (ML). This paper uses the NSL-KDD data set to propose a novel IDS based on Artificial Neural Networks (ANNs). As a result, the ML model generalizes sufficiently to perform well on untried data. The NSL-KDD dataset shall be utilized for both training and testing. In this paper, we present a custom ANN model architecture using the Keras open-source software package. The specific arrangement of nodes and layers, along with the activation functions, enhances the model's ability to capture intricate patterns in network data. The performance of the ANN is carefully tested and evaluated, resulting in the identification of a maximum detection accuracy of 97.5%. We thoroughly compared our suggested model to industry-recognized benchmark methods, such as decision classifier combinations and ML classifiers like k-Nearest Neighbors (KNN), Deep Learning (DL), Support Vector Machine (SVM), Long Short-Term Memory (LSTM), Deep Neural Network (DNN), and ANN. It is encouraging to see that our model consistently outperformed each of these tried-and-true techniques in all evaluations. This result underlines the effectiveness of the suggested methodology by demonstrating the ANN's capacity to accurately assess the effectiveness of the developed strategy in identifying and categorizing instances of network intrusion.

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Section: Comparative Analysismentioning

confidence: 99%

Intrusion Detection System with Customized Machine Learning Techniques for NSL-KDD Dataset

Zakariah,

AlQahtani,

Alawwad

et al. 2023

CMC

View full text Add to dashboard Cite

show abstract

“…Boosting models use a sequential iterative technique to combine multiple weak individual learners into one strong learner with a better performance. To generate the output of this strong learner, a relationship based on a weighted average or voting is established between the basic learners with different distributions [73]. Finally, stacking, known as stack generalization, is another type of ensemble learning technique that includes heterogeneous weak learners.…”

Section: Supervised Learningmentioning

confidence: 99%

“…Table 2 provides the characteristics, advantages, and disadvantages of these supervised classification techniques. strong learner, a relationship based on a weighted average or voting is established between the basic learners with different distributions [73]. Finally, stacking, known as stack generalization, is another type of ensemble learning technique that includes heterogeneous weak learners.…”

Section: Supervised Learningmentioning

confidence: 99%

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Machine Learning: Models, Challenges, and Research Directions

Talaei Khoei,

Kaabouch

2023

Future Internet

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Machine learning techniques have emerged as a transformative force, revolutionizing various application domains, particularly cybersecurity. The development of optimal machine learning applications requires the integration of multiple processes, such as data pre-processing, model selection, and parameter optimization. While existing surveys have shed light on these techniques, they have mainly focused on specific application domains. A notable gap that exists in current studies is the lack of a comprehensive overview of machine learning architecture and its essential phases in the cybersecurity field. To address this gap, this survey provides a holistic review of current studies in machine learning, covering techniques applicable to any domain. Models are classified into four categories: supervised, semi-supervised, unsupervised, and reinforcement learning. Each of these categories and their models are described. In addition, the survey discusses the current progress related to data pre-processing and hyperparameter tuning techniques. Moreover, this survey identifies and reviews the research gaps and key challenges that the cybersecurity field faces. By analyzing these gaps, we propose some promising research directions for the future. Ultimately, this survey aims to serve as a valuable resource for researchers interested in learning about machine learning, providing them with insights to foster innovation and progress across diverse application domains.

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Machine Learning-Based Ensemble Network Security System

Cited by 2 publications

References 7 publications

Intrusion Detection System with Customized Machine Learning Techniques for NSL-KDD Dataset

Intrusion Detection System with Customized Machine Learning Techniques for NSL-KDD Dataset

Machine Learning: Models, Challenges, and Research Directions

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