Study and analysis of intrusion detection system using random forest and linear regression

Kumar, Sathish P.; Raaza, Arun

doi:10.21533/pen.v6i1.289

Cited by 8 publications

(4 citation statements)

References 2 publications

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“…It is also susceptible to outliers and multicollinearity. LR must be modified utilizing one-vs-all or one-vs-one techniques to be relevant in circumstances involving several classes [20].…”

Section: Backgrounds and Related Workmentioning

confidence: 99%

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Hyperparameters optimization XGBoost for network intrusion detection using CSE-CIC-IDS 2018 dataset

Chimphlee,

Chimphlee

2024

IJ-AI

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<p>With the introduction of high-speed internet access, the demand for security and dependable networks has grown. In recent years, network attacks have gotten more complex and intense, making security a vital component of organizational information systems. Network intrusion detection systems (NIDS) have become an essential detection technology to protect data integrity and system availability against such attacks. NIDS is one of the most well-known areas of machine learning software in the security field, with machine learni ng algorithms constantly being developed to improve performance. This research focuses on detecting abnormalities in societal infiltration using the hyperparameters optimization XGBoost (HO-XGB) algorithm with the Communications Security Establishment-The Canadian Institute for Cybersecurity-Intrusion Detection System2018 (CSE-CIC-IDS2018) dataset to get the best potential results. When compared to typical machine learning methods published in the literature, HO-XGB outperforms them. The study shows that XGBoost outperforms other detection algorithms. We refined the HO-XGB model's hyperparameters, which included learning_rate, subsample, max_leaves, max_depth, gamma, colsample_bytree, min_child_weight, n_estimators, max_depth, and reg_alpha. The experimental findings reveal that HO-XGB1 outperforms multiple parameter settings for intrusion detection, effectively optimizing XGBoost's hyperparameters.</p>

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“…It is also susceptible to outliers and multicollinearity. LR must be modified utilizing one-vs-all or one-vs-one techniques to be relevant in circumstances involving several classes [20].…”

Section: Backgrounds and Related Workmentioning

confidence: 99%

“…Random forest is opaque, making it difficult to understand the basis behind forecasts. Furthermore, its prediction time is longer than that of single decision trees, and characteristics such as the number of trees must be tuned for improved results [20], [22].…”

Section: Backgrounds and Related Workmentioning

confidence: 99%

Hyperparameters optimization XGBoost for network intrusion detection using CSE-CIC-IDS 2018 dataset

Chimphlee,

Chimphlee

2024

IJ-AI

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“…The correlation between the independent and dependent variables is the definition of this method. Kumar and Raaza [45] the independent variable is thought of as input values, and the dependent variable is thought of as output values.…”

Section: Linear Regression (Lr)mentioning

confidence: 99%

Machine learning to improve the performance of anomaly-based network intrusion detection in big data

Chimphlee

2023

IJEECS

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With the rapid growth of digital technology communications are overwhelmed by network data traffic. The demand for the internet is growing every day in today's cyber world, raising concerns about network security. Big Data are a term that describes a vast volume of complicated data that is critical for evaluating network patterns and determining what has occurred in the network. Therefore, detecting attacks in a large network is challenging. Intrusion detection system (IDS) is a promising cybersecurity research field. In this paper, we proposed an efficient classification scheme for IDS, which is divided into two procedures, on the CSE-CIC-IDS-2018 dataset, data pre-processing techniques including under-sampling, feature selection, and classifier algorithms were used to assess and decide the best performing model to classify invaders. We have implemented and compared seven classifier machine learning algorithms with various criteria. This work explored the application of the random forest (RF) for feature selection in conjunction with machine learning (ML) techniques including linear regression (LR), k-Nearest Neighbor (k-NN), classification and regression trees (CART), Bayes, RF, multi layer perceptron (MLP), and XGBoost in order to implement IDSS. The experimental results show that the MLP algorithm in the most successful with best performance with evaluation matrix.

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“…The targets that are tracked can be even directly such as tracking moving objects or based on video surveillance such as monitoring status at specific location. The tracking processes are illustrated briefly by mining data related from an interested image edge in order to discover the shape of the object, tracing the object among the frames taken from the image, and evaluating the target object to understand its behavior [4,5,6]. The hierarchical structure that integrates the robotic systems, the edge processing and the tracking methods in a uniformed robust module is called visual servoing.…”

Section: Introductionmentioning

confidence: 99%

Eye in hand robot arm based automated object grasping system

Aris¹,

Mahmood²

2019

PEN

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The modern robotic systems state that the tracking methodology and the visual servoing are imperative to discover the existence of an object and excite the robot in order to manipulate the target. This paper shows a new object tracking and grasping technique in real time based on Eye in Hand visual servoing structure via a camera mounted at the end of the robot arm. The working principle of the robotic system depends mainly on the prediction based on Kalman filter method that estimates the next location of a moving object in order to specify the path of the target under the scope of the camera. Hereby, the proposed system observes the object and studies its behavior based on the pervious state in order to grasp the target at the exact position. Furthermore, the vision system implements feedback control approach to keep the extracted information of the object updated to solve the stability and the reliability issues that might be encountered. It has to be mentioned that the proposed robotic system was tested by grasping moving objects in different speeds and directions. In addition, the grasping of a stationary object was tested to confirm the practical and the theoretical results. As a final result, it can be stated that the speed of the object is directly proportional with the grasping time and vice versa.

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Study and analysis of intrusion detection system using random forest and linear regression

Cited by 8 publications

References 2 publications

Hyperparameters optimization XGBoost for network intrusion detection using CSE-CIC-IDS 2018 dataset

Hyperparameters optimization XGBoost for network intrusion detection using CSE-CIC-IDS 2018 dataset

Machine learning to improve the performance of anomaly-based network intrusion detection in big data

Eye in hand robot arm based automated object grasping system

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