Ensemble machine learning approaches for webshell detection in Internet of things environments

Yong, Binbin; Wei, Wei; Li, Kuan‐Ching; Shen, Jun; Zhou, Qing; Woźniak, Marcin; Połap, Dawid; Damaševičius, Robertas

doi:10.1002/ett.4085

Cited by 48 publications

(29 citation statements)

References 39 publications

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“…For this kind of image processing problems with complex interference information, a low percentage of positive sample pixels in the field of view, and inconspicuous target features, it is usually difficult for the generative model to fully learn the distribution of key data in the input samples, and the complex scene information has a large impact on the accuracy of the generative model sample authenticity determination [22]. e multitarget tracker may encounter the problem of drifting, misfollowing, or losing the identity due to the change of identity caused by the deformation of the target in the process of tracking specific targets [23][24][25]. In the online tracking process, the stability and accuracy of the tracker can be effectively improved if more diverse samples (e.g., samples of the same target in multiple views and multiple motion states) can be generated based on a limited number of samples of a specific target in the history frame.…”

Section: Multiobjective Scheme Design For Motion In Sports Videomentioning

confidence: 99%

Machine Learning‐Based Multitarget Tracking of Motion in Sports Video

Zhang¹,

Yang

2021

Complexity

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In this paper, we track the motion of multiple targets in sports videos by a machine learning algorithm and study its tracking technique in depth. In terms of moving target detection, the traditional detection algorithms are analysed theoretically as well as implemented algorithmically, based on which a fusion algorithm of four interframe difference method and background averaging method is proposed for the shortcomings of interframe difference method and background difference method. The fusion algorithm uses the learning rate to update the background in real time and combines morphological processing to correct the foreground, which can effectively cope with the slow change of the background. According to the requirements of real time, accuracy, and occupying less video memory space in intelligent video surveillance systems, this paper improves the streamlined version of the algorithm. The experimental results show that the improved multitarget tracking algorithm effectively improves the Kalman filter-based algorithm to meet the real-time and accuracy requirements in intelligent video surveillance scenarios.

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Section: Multiobjective Scheme Design For Motion In Sports Videomentioning

confidence: 99%

Machine Learning‐Based Multitarget Tracking of Motion in Sports Video

Zhang¹,

Yang

2021

Complexity

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“…Another interesting work investigates the webshell detection in IoT environment using the ensemble methods [34]. The authors applied three types of ensemble techniques to enhance the machine learning model's performance: voting, extremely randomized trees (ET), and random forest (RF).…”

Section: Introductionmentioning

confidence: 99%

A Multi-Stage Classification Approach for IoT Intrusion Detection Based on Clustering with Oversampling

et al. 2021

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Intrusion detection of IoT-based data is a hot topic and has received a lot of interests from researchers and practitioners since the security of IoT networks is crucial. Both supervised and unsupervised learning methods are used for intrusion detection of IoT networks. This paper proposes an approach of three stages considering a clustering with reduction stage, an oversampling stage, and a classification by a Single Hidden Layer Feed-Forward Neural Network (SLFN) stage. The novelty of the paper resides in the technique of data reduction and data oversampling for generating useful and balanced training data and the hybrid consideration of the unsupervised and supervised methods for detecting the intrusion activities. The experiments were evaluated in terms of accuracy, precision, recall, and G-mean and divided into four steps: measuring the effect of the data reduction with clustering, the evaluation of the framework with basic classifiers, the effect of the oversampling technique, and a comparison with basic classifiers. The results show that SLFN classification technique and the choice of Support Vector Machine and Synthetic Minority Oversampling Technique (SVM-SMOTE) with a ratio of 0.9 and the k value of 3 for k-means++ clustering technique give better results than other values and other classification techniques.

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“…Threats such as phishing [5], spyware and malware, trojans, worms, and even intrusions need sophisticated instrumentation of a multitude of hacked machines, also known as botnets. The landscape of internet crimes has become automated, which has made the use of nonhuman agents such as botnets, hijacked Internet of Things (IoT) devices [6], and compromised wireless sensor networks (WSNs) [7] more common.…”

Section: Introductionmentioning

confidence: 99%

“…To face these problems, it is important to propose novel deep-learning frameworks and validate them on new malware datasets. The use of ensemble methods such as random forests has previously facilitated the output of machine learning models to enhance malware detection in Internet of Things (IoT) environments [6]. The goal of this research was to deploy an ensemble of deep neural networks for malware detection and classification.…”

Section: Introductionmentioning

confidence: 99%

Windows PE Malware Detection Using Ensemble Learning

et al. 2021

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In this Internet age, there are increasingly many threats to the security and safety of users daily. One of such threats is malicious software otherwise known as malware (ransomware, Trojans, viruses, etc.). The effect of this threat can lead to loss or malicious replacement of important information (such as bank account details, etc.). Malware creators have been able to bypass traditional methods of malware detection, which can be time-consuming and unreliable for unknown malware. This motivates the need for intelligent ways to detect malware, especially new malware which have not been evaluated or studied before. Machine learning provides an intelligent way to detect malware and comprises two stages: feature extraction and classification. This study suggests an ensemble learning-based method for malware detection. The base stage classification is done by a stacked ensemble of fully-connected and one-dimensional convolutional neural networks (CNNs), whereas the end-stage classification is done by a machine learning algorithm. For a meta-learner, we analyzed and compared 15 machine learning classifiers. For comparison, five machine learning algorithms were used: naïve Bayes, decision tree, random forest, gradient boosting, and AdaBoosting. The results of experiments made on the Windows Portable Executable (PE) malware dataset are presented. The best results were obtained by an ensemble of seven neural networks and the ExtraTrees classifier as a final-stage classifier.

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Ensemble machine learning approaches for webshell detection in Internet of things environments

Cited by 48 publications

References 39 publications

Machine Learning‐Based Multitarget Tracking of Motion in Sports Video

Machine Learning‐Based Multitarget Tracking of Motion in Sports Video

A Multi-Stage Classification Approach for IoT Intrusion Detection Based on Clustering with Oversampling

Windows PE Malware Detection Using Ensemble Learning

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