DDOS Attack Detection Using Lightweight Partial Decision Tree algorithm

Kareem, Mohammed Ibrahim; Jasim, Mahdi Nsaif

doi:10.1109/csase51777.2022.9759824

Cited by 14 publications

(8 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This process iterates recursively until no instances remain unaccounted. For the creation of an individual rule [21], PART constructs a partial decision tree based on the current set of instances. The leaf node with the most extensive coverage is then selected as new rule.…”

Section: Partial Decision Treesmentioning

confidence: 99%

Smart livestock management: integrating IoT for cattle health diagnosis and disease prediction through machine learning

Swain,

Pattnayak,

Mohanty

et al. 2024

IJEECS

View full text Add to dashboard Cite

Cattle diseases can significantly impact on livestock health and agricultural productivity is substantial. Timely detection and prognosis of these diseases are essential for prompt interventions and preventing their spread within the herd. This study delved into employing machine learning models to anticipate cattle diseases based on relevant parameters. These parameters encompass milk fever, milk clots, milk watery, milk flake, blisters, lameness, stomach pain, gaseous stomach, dehydration, diarrhea, vomiting, abdominal issues, and alkalosis. A dataset of 2,000 samples from diverse cattle populations was amassed, each tagged with the presence or absence of specific diseases. The primary goal was to compare the efficacy of five well-known machine learning models: Naïve Bayes multinomial (NBM), lazy-IBk, partial tree (PART), random forest (RF), and support vector machine (SVM). The findings underscored the consistent superiority of RF in comparison to the other models, boasting the highest accuracy in predicting cattle diseases. The RF model exhibited an accuracy rate of 88% on the test dataset. This achievement can be ascribed to its capacity to handle intricate interactions among input features and mitigate over fitting through ensemble learning. These insights can furnish valuable information about early indicators and risk factors associated with diverse cattle diseases.

show abstract

Section: Partial Decision Treesmentioning

confidence: 99%

Smart livestock management: integrating IoT for cattle health diagnosis and disease prediction through machine learning

Swain,

Pattnayak,

Mohanty

et al. 2024

IJEECS

View full text Add to dashboard Cite

show abstract

“…In [8], a support value-based graph is used to classify intrusions from CICDDoS2019 dataset. Kareem et al [9] used several ML techniques, trained with on CICIDS2017 and validated using the CICDDoS2019 dataset, showing a DDoS attacks detection accuracy of above 99.77%. In [10], DNN proposed the recognition of DoS/DDoS attacks, by using CICIDS2017 dataset, which reaches an accuracy of 98.72%.…”

Section: Related Workmentioning

confidence: 99%

New Denial of Service Attacks Detection Approach Using Hybridized Deep Neural Networks and Balanced Datasets

Mjahed¹,

Hadaj²,

Guarmah³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Denial of Service (DoS/DDoS) intrusions are damaging cyberattacks, and their identification is of great interest to the Intrusion Detection System (IDS). Existing IDS are mainly based on Machine Learning (ML) methods including Deep Neural Networks (DNN), but which are rarely hybridized with other techniques. The intrusion data used are generally imbalanced and contain multiple features. Thus, the proposed approach aims to use a DNN-based method to detect DoS/DDoS attacks using CICIDS2017, CSE-CICIDS2018 and CICDDoS 2019 datasets, according to the following key points. a) Three imbalanced CICIDS2017-2018-2019 datasets, including Benign and DoS/DDoS attack classes, are used. b) A new technique based on K-means is developed to obtain semi-balanced datasets. c) As a feature selection method, LDA (Linear Discriminant Analysis) performance measure is chosen. d) Four metaheuristic algorithms, counting Artificial Immune System (AIS), Firefly Algorithm (FA), Invasive Weeds Optimization (IWO) and Cuckoo Search (CS) are used, for the first time together, to increase the performance of the suggested DNN-based DoS attacks detection. The experimental results, based on semi-balanced training and test datasets, indicated that AIS, FA, IWO and CS-based DNNs can achieve promising results, even when cross-validated. AIS-DNN yields a tested accuracy of 99.97%, 99.98% and 99.99%, for the three considered datasets, respectively, outperforming performance established in several related works.

show abstract

“…Following the results and analyses of the experiments and a discussion of their significance, the paper concludes with recommendations for further research. The detection of DDoS attacks has been proposed using a variety of different methods such as [8]- [10]. Techniques based on machine learning are the ones that appear most frequently in published works of research.…”

Section: Introductionmentioning

confidence: 99%

K-Means clustering-based semi-supervised for DDoS attacks classification

Jasim

Gaata

2022

Bulletin EEI

View full text Add to dashboard Cite

Network attacks of the distributed denial of service (DDoS) form are used to disrupt server replies and services. It is popular because it is easy to set up and challenging to detect. We can identify DDoS attacks on network traffic in a variety of ways. However, the most effective methods for detecting and identifying a DDoS attack are machine learning approaches. This attack is considered to be among the most dangerous internet threats. In order for supervised machine learning algorithms to function, there needs to be tagged network traffic data sets. On the other hand, an unsupervised method uses network traffic analysis to find assaults. In this research, the K-Means clustering algorithm was developed as a semi-supervised approach for DDoS classification. The proposed algorithm is trained and tested with the CICIDS2017 dataset. After using the proposed hybrid feature selection methods and applying multiple training, testing, and carefully sorting DDoS traffic through a series of experiments, the optimum 2 centroids were found to be DDoS and normal. The generated centroids can be used to classify network traffic. So the proposed method succeeded to cluster the network traffic to safe and theat.

show abstract

DDOS Attack Detection Using Lightweight Partial Decision Tree algorithm

Cited by 14 publications

References 25 publications

Smart livestock management: integrating IoT for cattle health diagnosis and disease prediction through machine learning

Smart livestock management: integrating IoT for cattle health diagnosis and disease prediction through machine learning

New Denial of Service Attacks Detection Approach Using Hybridized Deep Neural Networks and Balanced Datasets

K-Means clustering-based semi-supervised for DDoS attacks classification

Contact Info

Product

Resources

About