Comparative Analysis of Intrusion Detection Systems and Machine Learning-Based Model Analysis Through Decision Tree

Azam, Zahedi; Islam, Md. Motaharul; Huda, Mohammad Nurul

doi:10.1109/access.2023.3296444

Cited by 44 publications

(6 citation statements)

References 215 publications

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“…In other words, IDS is a security technology designed to analyze and monitor network behaviors, aiming to detect and react to security violations, unauthorized access, or potential risks. IDSs work by examining network traffic or system actions, matching them with predetermined signatures or behavioral patterns that could signify an intrusion or malicious behavior [14]. The primary objectives of an IDS include monitoring and analyzing host and network behaviors, providing alerts, and taking action in response to suspicious activities [7].…”

Section: B Intrusion Detection Systemsmentioning

confidence: 99%

A Comparative Analysis of Intrusion Detection Systems: Leveraging Classification Algorithms and Feature Selection Techniques

Shakir,

Mohsin

2024

JASTT

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With the increasing use of the Internet and its coverage of all areas of life and the increasing amount of sensitive and confidential information on the Internet, the number of malicious attacks on that information has increased with the aim of destroying, changing, or misusing it. Consequently, the need to discover and prevent these kinds of attacks has increased in order to maintain privacy, reliability, and even availability. For this purpose, intelligent systems have been developed to detect these attacks, which are called Intrusion Detection System (IDS). These systems were tested and applied to special benchmark datasets that contain a large number of features and a massive number of observations. However, not all the features are important, and some are not relevant. Therefore, applying feature selection techniques becomes crucial, which select the features with the most importance and relevance in order to enhance the performance of the classification model. The aim of this review paper is to conduct a comparative analysis of various state-of-the-art IDS that use algorithm classifications to detect network attacks with the cooperation of feature selection techniques that have been applied to various well-known IDS datasets, such as KDD cup99, NSL-KDD, etc. This comparison is based on several factors, including the utilized classification technique, feature selection used, employed evaluation metrics, datasets used, and finally the highest accuracy rate obtained by each study.

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Section: B Intrusion Detection Systemsmentioning

confidence: 99%

A Comparative Analysis of Intrusion Detection Systems: Leveraging Classification Algorithms and Feature Selection Techniques

Shakir,

Mohsin

2024

JASTT

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“…The Decision Tree Classifier (DTC) is a versatile tool in supervised learning, serving classification and regression tasks (Azam et al, 2023). It generates a tree-like framework in which internal nodes represent attribute tests, branches indicate test results, and leaf nodes hold class labels (Figure 4).…”

Section: Decision Tree Classifier (Dtc)mentioning

confidence: 99%

Comparison of machine and deep learning algorithms using Google Earth Engine and Python for land classifications

Nigar,

Li,

Jat Baloch

et al. 2024

Front. Environ. Sci.

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Classifying land use and land cover (LULC) is essential for various environmental monitoring and geospatial analysis applications. This research focuses on land classification in District Sukkur, Pakistan, employing the comparison between machine and deep learning models. Three satellite indices, namely, NDVI, MNDWI, and NDBI, were derived from Landsat-8 data and utilized to classify four primary categories: Built-up Area, Water Bodies, Barren Land, and Vegetation. The main objective of this study is to evaluate and compare the effectiveness of comparison of machine and deep learning models. The machine learning models including Random Forest achieved an overall accuracy of 91.3% and a Kappa coefficient of 0.90. It accurately classified 2.7% of the area as Built-up Area, 1.9% as Water Bodies, 54.8% as Barren Land, and 40.4% as Vegetation. While slightly less accurate, Decision Tree model provided reliable classifications. Deep learning models showed significant accuracy, of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The CNN model achieved an impressive overall accuracy of 97.3%, excelling in classifying Water Bodies with User and Producer Accuracy exceeding 99%. The RNN model, with an overall accuracy of 96.2%, demonstrated strong performance in categorizing Vegetation. These findings offer valuable insights into the potential applications of machine learning and deep learning models for perfect land classifications, with implications for environmental monitoring management and geospatial analysis. The rigorous validation and comparative analysis of these models contribute to advancing remote sensing techniques and their utilization in land classification tasks. This research presents a significant contribution to the field and underscores the importance of precise land classification in the context of sustainable land management and environmental conservation.

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“…The strength of Behavioral IDSs lies in their advanced analytical capabilities, driven by the integration of machine learning algorithms, statistical analysis, and data mining techniques [25][26]. These methodologies empower behavioral IDSs to learn from historical data and dynamically adjust their intrusion detection mechanisms.…”

Section: Behavioral Idssmentioning

confidence: 99%

Enhancing Intrusion Detection in Mobile Ad-Hoc Networks: Comparative Study of Behavioral IDSs

Jelleli,

Alimi

2024

Preprint

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With an emphasis on behavioral intrusion detection systems [BIDSs], this study investigates the field of intrusion detection in mobile ad hoc networks [MANETs]. Because they are dynamic and decentralized, MANETs are vulnerable to a range of security risks, such as infiltration attempts. In this situation, conventional intrusion detection techniques show their shortcomings, opening the door for BIDS research. We provide a thorough comparison study of several intrusion detection system [IDS] methods, such as behavioral analysis, rule-based detection, machine learning-based detection, statistical anomaly detection, and environmental-based detection. These techniques are assessed in a dynamic network setting that considers the increasing volume of data and sporadic changes in sensor characteristics. The simulation becomes more realistic with the addition of behavior scores, which complicate the intrusion detection procedure Among the evaluation criteria are detection rates, which provide information about how well each IDS strategy performs in dynamic MANETs. The results contribute to the ongoing effort to improve security in MANETs by highlighting the advantages and disadvantages of the various intrusion detection approaches.

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Comparative Analysis of Intrusion Detection Systems and Machine Learning-Based Model Analysis Through Decision Tree

Cited by 44 publications

References 215 publications

A Comparative Analysis of Intrusion Detection Systems: Leveraging Classification Algorithms and Feature Selection Techniques

A Comparative Analysis of Intrusion Detection Systems: Leveraging Classification Algorithms and Feature Selection Techniques

Comparison of machine and deep learning algorithms using Google Earth Engine and Python for land classifications

Enhancing Intrusion Detection in Mobile Ad-Hoc Networks: Comparative Study of Behavioral IDSs

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