A Genetic-Based Extreme Gradient Boosting Model for Detecting Intrusions in Wireless Sensor Networks

Alqahtani, Mnahi; Gumaei, Abdu; Mathkour, Hassan; Ismail, Mohamed Maher Ben

doi:10.3390/s19204383

Cited by 69 publications

(36 citation statements)

References 54 publications

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“…The extreme gradient boosting (XGBoost) method is a kind of gradient boosting decision tree (GBDT) [50] technique, which can be used for both classification and regression problems. As described in [51], gradient boosting is an ensemble learning method that combines a set of weak classifiers f i (x) to form a strong classifier F(x). Therefore, boosting methods have three elements [52]:…”

Section: Extreme Gradient Boosting (Xgboost)mentioning

confidence: 99%

“…The proposed approach is based on a Fisher-score-based feature selection method and a genetic-based extreme gradient boosting (GXGBoost) model. GXGBoost is an effective ensemble model that was proposed in [51] to detect intrusion attacks in wireless sensors networks in which the features of the data traffic are quite small. The GXGBoost model uses the genetic algorithm to select the optimal values of model parameters to improve the accuracy of minority classes without affecting the overall accuracy of other classes.…”

Section: Proposed Approachmentioning

confidence: 99%

“…The methods used to develop the GXGBoost model are described briefly in the previous subsections. More details and an explanation of the GXGBoost algorithm steps are given in [51]. Algorithm 2 describes the pseudocode of the GXGBoost model.…”

Section: Proposed Approachmentioning

confidence: 99%

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IoT Botnet Attack Detection Based on Optimized Extreme Gradient Boosting and Feature Selection

Alqahtani

Mathkour

Ismail

2020

Sensors

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Nowadays, Internet of Things (IoT) technology has various network applications and has attracted the interest of many research and industrial communities. Particularly, the number of vulnerable or unprotected IoT devices has drastically increased, along with the amount of suspicious activity, such as IoT botnet and large-scale cyber-attacks. In order to address this security issue, researchers have deployed machine and deep learning methods to detect attacks targeting compromised IoT devices. Despite these efforts, developing an efficient and effective attack detection approach for resource-constrained IoT devices remains a challenging task for the security research community. In this paper, we propose an efficient and effective IoT botnet attack detection approach. The proposed approach relies on a Fisher-score-based feature selection method along with a genetic-based extreme gradient boosting (GXGBoost) model in order to determine the most relevant features and to detect IoT botnet attacks. The Fisher score is a representative filter-based feature selection method used to determine significant features and discard irrelevant features through the minimization of intra-class distance and the maximization of inter-class distance. On the other hand, GXGBoost is an optimal and effective model, used to classify the IoT botnet attacks. Several experiments were conducted on a public botnet dataset of IoT devices. The evaluation results obtained using holdout and 10-fold cross-validation techniques showed that the proposed approach had a high detection rate using only three out of the 115 data traffic features and improved the overall performance of the IoT botnet attack detection process.

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Section: Extreme Gradient Boosting (Xgboost)mentioning

confidence: 99%

Section: Proposed Approachmentioning

confidence: 99%

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IoT Botnet Attack Detection Based on Optimized Extreme Gradient Boosting and Feature Selection

Alqahtani

Mathkour

Ismail

2020

Sensors

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“…Khan and Gumaei [2] compared the most popular machine learning methods for intrusion detection in terms of accuracy, precision, recall, and training time cost. Alqahtani et al [3] proposed GXGBoost model to detect intrusion attacks based on a genetic algorithm and an extreme gradient boosting (XGBoost) classifier. Derhab et al [4] proposed a security architecture that integrates the Blockchain and the software-defined network (SDN) technologies, which focuses on the security of commands in industrial IoT against forged commands and misrouting of commands.…”

Section: Introductionmentioning

confidence: 99%

DL-IDS: Extracting Features Using CNN-LSTM Hybrid Network for Intrusion Detection System

Sun

Liu

et al. 2020

Security and Communication Networks

139

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Many studies utilized machine learning schemes to improve network intrusion detection systems recently. Most of the research is based on manually extracted features, but this approach not only requires a lot of labor costs but also loses a lot of information in the original data, resulting in low judgment accuracy and cannot be deployed in actual situations. This paper develops a DL-IDS (deep learning-based intrusion detection system), which uses the hybrid network of Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) to extract the spatial and temporal features of network traffic data and to provide a better intrusion detection system. To reduce the influence of an unbalanced number of samples of different attack types in model training samples on model performance, DL-IDS used a category weight optimization method to improve the robustness. Finally, DL-IDS is tested on CICIDS2017, a reliable intrusion detection dataset that covers all the common, updated intrusions and cyberattacks. In the multiclassification test, DL-IDS reached 98.67% in overall accuracy, and the accuracy of each attack type was above 99.50%.

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“…At the same time, Al-Abassi proposed an ensemble method using Deep Neural Network (DNN) and Decision Tree (DT), and this method shows it can achieve high detection accuracy with low false-positive [16]. References [17,18] used other deep learning algorithms to detect network intrusion manners, which can effectively identify various attacks. Although the above methods achieved good detection performance, the detection processes of these methods are offline, as a result, it is difficult to give a warning timely and to minimize the risk of network abnormal.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Intrusion Detection in Wireless Network: A Deep Learning-Based Intelligent Mechanism

Yang

Yin

et al. 2020

IEEE Access

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With the development of the wireless network techniques, the number of cyber-attack increases significantly, which has seriously threat the security of Wireless Local Area Network (WLAN). The traditional intrusion detection technology is a prevalent area of study for numerous years, but it may not have a good detection performance in a real-time way. Therefore, it is urgent to design a detection mechanism to detect the attacks timely. In this paper, we exploit a CDBN (Conditional Deep Belief Network)-based intrusion detection mechanism to recognize the attack features and perform the wireless network intrusion detection in real time. To avoid the impact of the imbalanced dataset and the data redundancy on the detection accuracy, a windowbased instance selection algorithm-SamSelect‖ is adopted to undersample the majority class data samples, and a Stacked Contractive Auto-Encoder (SCAE) algorithm is proposed to reduce the dimension of the data samples. By doing so, our proposed mechanism can effectively detect the potential attack and achieve high accuracy. The experiment results show that CDBN can be effectively combined with-SamSelect‖ and SCAE, and the proposed mechanism has a high detection speed and accuracy, with the average detection time 1.14 ms and the detection accuracy 0.974.

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A Genetic-Based Extreme Gradient Boosting Model for Detecting Intrusions in Wireless Sensor Networks

Cited by 69 publications

References 54 publications

IoT Botnet Attack Detection Based on Optimized Extreme Gradient Boosting and Feature Selection

IoT Botnet Attack Detection Based on Optimized Extreme Gradient Boosting and Feature Selection

DL-IDS: Extracting Features Using CNN-LSTM Hybrid Network for Intrusion Detection System

Real-Time Intrusion Detection in Wireless Network: A Deep Learning-Based Intelligent Mechanism

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