IntruDTree: A Machine Learning-Based Cyber Security Intrusion Detection Model

Sarker, Iqbal H.; Abushark, Yoosef B.; Alsolami, Fawaz; Khan, Asif Irshad

doi:10.20944/preprints202004.0481.v1

Cited by 69 publications

(78 citation statements)

References 45 publications

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“…Signature-based IDS requires specific patterns of malicious samples to perform detection, which raises an issue against never seen threats. On the other hand, anomaly-based IDS profile the behaviour of network traffic in order to detect deviations when no traffic categorization can be performed [ 30 ]. Different anomaly-based IDS deployments leverage data-driven learning approaches in order to detect botnets accurately, both when dealing with existing specimens and with new variants as well.…”

Section: State Of the Artmentioning

confidence: 99%

Benchmark-Based Reference Model for Evaluating Botnet Detection Tools Driven by Traffic-Flow Analytics

Ramos

Monge

Vidal

2020

Sensors

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Botnets are some of the most recurrent cyber-threats, which take advantage of the wide heterogeneity of endpoint devices at the Edge of the emerging communication environments for enabling the malicious enforcement of fraud and other adversarial tactics, including malware, data leaks or denial of service. There have been significant research advances in the development of accurate botnet detection methods underpinned on supervised analysis but assessing the accuracy and performance of such detection methods requires a clear evaluation model in the pursuit of enforcing proper defensive strategies. In order to contribute to the mitigation of botnets, this paper introduces a novel evaluation scheme grounded on supervised machine learning algorithms that enable the detection and discrimination of different botnets families on real operational environments. The proposal relies on observing, understanding and inferring the behavior of each botnet family based on network indicators measured at flow-level. The assumed evaluation methodology contemplates six phases that allow building a detection model against botnet-related malware distributed through the network, for which five supervised classifiers were instantiated were instantiated for further comparisons—Decision Tree, Random Forest, Naive Bayes Gaussian, Support Vector Machine and K-Neighbors. The experimental validation was performed on two public datasets of real botnet traffic—CIC-AWS-2018 and ISOT HTTP Botnet. Bearing the heterogeneity of the datasets, optimizing the analysis with the Grid Search algorithm led to improve the classification results of the instantiated algorithms. An exhaustive evaluation was carried out demonstrating the adequateness of our proposal which prompted that Random Forest and Decision Tree models are the most suitable for detecting different botnet specimens among the chosen algorithms. They exhibited higher precision rates whilst analyzing a large number of samples with less processing time. The variety of testing scenarios were deeply assessed and reported to set baseline results for future benchmark analysis targeted on flow-based behavioral patterns.

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Section: State Of the Artmentioning

confidence: 99%

Benchmark-Based Reference Model for Evaluating Botnet Detection Tools Driven by Traffic-Flow Analytics

Ramos

Monge

Vidal

2020

Sensors

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“…Thus the challenge is to effectively select the relevant and important features or extracting new features that are known as feature optimization. In the area of AI, particularly data science and machine learning, feature optimization problem is considered as an important pre-processing step that helps to build an effective and simplified model and consequently improves the performance of the learning algorithms by removing the redundant and irrelevant features [111]. Therefore, feature optimization could be a significant research issue in the area of mobile data science and intelligent applications.…”

Section: Research Issues and Future Directionsmentioning

confidence: 99%

Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions

et al. 2020

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Artificial intelligence (AI) techniques have grown rapidly in recent years in the context of computing with smart mobile phones that typically allows the devices to function in an intelligent manner. Popular AI techniques include machine learning and deep learning methods, natural language processing, as well as knowledge representation and expert systems, can be used to make the target mobile applications intelligent and more effective. In this paper, we present a comprehensive view on "mobile data science and intelligent apps" in terms of concepts and AI-based modeling that can be used to design and develop intelligent mobile applications for the betterment of human life in their diverse day-today situation. This study also includes the concepts and insights of various AI-powered intelligent apps in several application domains, ranging from personalized recommendation to healthcare services, including COVID-19 pandemic management in recent days. Finally, we highlight several research issues and future directions relevant to our analysis in the area of mobile data science and intelligent apps. Overall, this paper aims to serve as a reference point and guidelines for the mobile application developers as well as the researchers in this domain, particularly from the technical point of view.

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“…Among the traditional machine learning classification approaches, a tree-based, particularly, a decision tree based context-aware model is more effective to analyze user behavior in the domain of smartphone data analytics [7]. A number of researchers use decision tree classification technique in their study for different purposes [19][20][21][22][23]. For instance, Hong et al [20], Lee et al [21] propose context-aware model for providing personalized services utilizing context history.…”

Section: Background and Related Workmentioning

confidence: 99%

“…A decision tree based robust user behavior model utilizing contextual smartphone data has been presented in [11]. In addition to smartphone usage, decision tree based model can also be used in the domain of IoT or cybersecurity analytics [23]. Several decision tree learning approaches such as ID3 decision tree [24], C4.5 decision tree [25], behavioral decision tree BehavDT [6] exist with the capability of constructing contextual decision trees and building context-aware predictive models.…”

Section: Background and Related Workmentioning

confidence: 99%

“…A contextual dataset may contain data with high dimensions, and some of them might not contain significant information for building a machine learning classification-based model. Moreover, further processing with all the given features or attributes using machine learning techniques might give poor prediction results because of the over-fitting problem [23,41]. Thus, an optimal number of features by selecting contexts, is needed not only to reduce the computational cost but also to build a more effective context-aware model with a higher accuracy rate.…”

Section: Contextual Feature Selectionmentioning

confidence: 99%

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Context pre-modeling: an empirical analysis for classification based user-centric context-aware predictive modeling

et al. 2020

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Nowadays, machine learning classification techniques have been successfully used while building data-driven intelligent predictive systems in various application areas including smartphone apps. For an effective context-aware system, context pre-modeling is considered as a key issue and task, as the representation of contextual data directly influences the predictive models. This paper mainly explores the role of major context pre-modeling tasks, such as context vectorization by defining a good numerical measure through transformation and normalization, context generation and extraction by creating new brand principal components, context selection by taking into account a subset of original contexts according to their correlations, and eventually context evaluation, to build effective context-aware predictive models utilizing multi-dimensional contextual data. For creating models, various popular machine learning classification techniques such as decision tree, random forest, k-nearest neighbor, support vector machines, naive Bayes classifier, and deep learning by constructing a neural network of multiple hidden layers, are used in our study. Based on the context pre-modeling tasks and classification methods, we experimentally analyze user-centric smartphone usage behavioral activities utilizing their contextual datasets. The effectiveness of these machine learning context-aware models is examined by considering prediction accuracy, in terms of precision, recall, f-score, and ROC values, and has been made an empirical discussion in various dimensions within the scope of our study.

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IntruDTree: A Machine Learning-Based Cyber Security Intrusion Detection Model

Cited by 69 publications

References 45 publications

Benchmark-Based Reference Model for Evaluating Botnet Detection Tools Driven by Traffic-Flow Analytics

Benchmark-Based Reference Model for Evaluating Botnet Detection Tools Driven by Traffic-Flow Analytics

Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions

Context pre-modeling: an empirical analysis for classification based user-centric context-aware predictive modeling

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