Application of XGBoost to the cyber-security problem of detecting suspicious network traffic events

Podlodowski, Łukasz; Kozłowski, Marek

doi:10.1109/bigdata47090.2019.9006586

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…Therefore, the outcome generally can be regarded as an either robust or reliable prediction. Among a series of gradient boost methods, XGBoost outshines in several AI applications [54] [55] [56], and the further discussion about the robustness of XGBoost can be realized in the work "Tree Boosting with XGBoost" [57]. Therefore, in this section, it will reveal the mechanism of XGBoost and explain why it is suitable to be applied in this work.…”

Section: Boosting Regression Based Methodsmentioning

confidence: 99%

A Boosting Regression-Based Method to Evaluate the Vital Essence in Semiconductor Industry Performance

et al. 2020

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In accordance with the statistical analysis, the industrial performance is usually related to research and development (R&D) intensity, and this factor indeed plausibly brings the biggest profit with patents and supporting products to the development of semiconductor industry. How to evaluate the completive performance of modern industries is an increasing issue, especially for the semiconductor industries in these decades. However, almost every traditional statistical model is deterred by the hypothesis of population and independent correlation among each feature, and this makes the result of typical regression model potentially lose reliability. To avoid this weakness, this paper therefore applies a gradient boosting based method-XGBoost to evaluate the feature importance of semiconductor industries. In the simulation experiments, different findings revel certain information, apart from R&D intensity, actually sway the gross net value in the annual financial announcement of semiconductor industries. Moreover, this paper proposes another concept to evaluate the essential factor contributing the development of semiconductor industries. Instead of only focusing on the effect of R&D intensity, this paper also predicts the future growth rate (GR) of net value by applying the greedy search of XGBoost Regression. INDEX TERMS XGBoost, Boosting Regression, Semiconductor Industry I. INOTRODUCTION Research and development (R&D) management is one of important issues in the research field of human resource management (HRM), and how to make proper investment in the expense of R&D management has become an increasing issue. The most works reveal a common conclusion in the literature that R&D has a positive effect on the performance of industries, so the term "R&D Intensity" is primarily applied in the analysis of relevant works and defined as the

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Section: Boosting Regression Based Methodsmentioning

confidence: 99%

A Boosting Regression-Based Method to Evaluate the Vital Essence in Semiconductor Industry Performance

et al. 2020

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“…Random Forest (RF): RF constructs numerous decision trees at training time and outputs the mode of the classes of the individual trees [43]. This malware detection approach is accurate even when a lot of data is absent and can handle a lot of data according to [44] and performance are XGBoost's major advantages according to [46]. Across 60,000, 100,000, and 160,000 cases, the models performed well.…”

Section: Classifiersmentioning

confidence: 99%

Toward Enhancing Malware Detection Using Practical Swarm Optimization in Honeypot

2024

IJIES

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Malware attacks have become a pressing concern in the domain of computer and communication systems, posing significant threats to data security and privacy. A practical approach is represented aiming to enhance the malware detection process with the help of Honeypot. A hybrid model of particle swarm optimization (PSO) integrated with Fuzzy_KNN algorithms is used in this research. Numerical simulations and mathematical analysis are conducted after developing numeric codes of this scheme. The performance and practicality are examined via these evaluation metrics including accuracy, precision, recall, and F1-score. Based on the numerical investigations, the findings confirmed satisfying performance measures of the hybrid model. The FuzzyKNN algorithm does attain the most remarkable effectiveness, achieving accuracy between 99.95% and 99.97%. This model employs a premium method of neighbourhood voting with an element of fuzziness and excels in large or complex datasets where patterns may emerge based on instance similarity.

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“…In the past couple of years, there is a surge in usage of XGboost Models due to their versatility and performance on complex data. [47,48,49,50]. The GPU Support and performance on XGBoost models is an added advantage for quick and reliable Pipeline design and Development.…”

Section: Tree Based Modelsmentioning

confidence: 99%

Anticipated Network Surveillance - An extrapolated study to predict cyber-attacks using Machine Learning and Data Analytics

Srivast¹,

Thakkar

Valiveti

et al. 2022

Preprint

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Machine learning and data mining techniques are utiized for enhancement of the security of any network. Researchers used machine learning for pattern detection, anomaly detection, dynamic policy setting, etc. The methods allow the program to learn from data and make decisions without human intervention, consuming a huge training period and computation power. This paper discusses a novel technique to predict an upcoming attack in a network based on several data parameters. The dataset is continuous in real-time implementation. The proposed model comprises dataset pre-processing, and training, followed by the testing phase. Based on the results of the testing phase, the best model is selected using which, event class which may lead to an attack is extracted. The event statistics are used for attack prediction. Implementation of the proposed architecture is evaluated against accuracy, precision, recall, and F1 score for processed data.

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Application of XGBoost to the cyber-security problem of detecting suspicious network traffic events

Cited by 6 publications

References 16 publications

A Boosting Regression-Based Method to Evaluate the Vital Essence in Semiconductor Industry Performance

A Boosting Regression-Based Method to Evaluate the Vital Essence in Semiconductor Industry Performance

Toward Enhancing Malware Detection Using Practical Swarm Optimization in Honeypot

Anticipated Network Surveillance - An extrapolated study to predict cyber-attacks using Machine Learning and Data Analytics

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