An Overview of the State-of-the-Art of Cloud Computing Cyber-Security

Bennasar, Hanane; Bendahmane, Ahmed; Essaaidi, Mohamed

doi:10.1007/978-3-319-55589-8_4

Cited by 8 publications

(2 citation statements)

References 13 publications

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“…Vasan et al [19] proposed an MC method based on combining two models of deep CNN, GoogleNet, and ResNet models. Bennasar et al [20] proposed an MC method based on combining three models of deep CNN, AlexNet, VGG, GoogleNet, and ResNet. Jeyaprakash et al [2] proposed an MC method based on DenseNet model.…”

Section: Related Workmentioning

confidence: 99%

Robust Malware Family Classification Using Effective Features and Classifiers

et al. 2022

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Malware development has significantly increased recently, posing a serious security risk to both consumers and businesses. Malware developers continually find new ways to circumvent security research’s ongoing efforts to guard against malware attacks. Malware Classification (MC) entails labeling a class of malware to a specific sample, while malware detection merely entails finding malware without identifying which kind of malware it is. There are two main reasons why the most popular MC techniques have a low classification rate. First, Finding and developing accurate features requires highly specialized domain expertise. Second, a data imbalance that makes it challenging to classify and correctly identify malware. Furthermore, the proposed malware classification (MC) method consists of the following five steps: (i) Dataset preparation: 2D malware images are created from the malware binary files; (ii) Visualized Malware Pre-processing: the visual malware images need to be scaled to fit the CNN model’s input size; (iii) Feature extraction: both hand-engineering (Tamura) and deep learning (GoogLeNet) techniques are used to extract the features in this step; (iv) Classification: to perform malware classification, we employed k-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Extreme Learning Machine (ELM). The proposed method is tested on a standard Malimg unbalanced dataset. The accuracy rate of the proposed method was extremely high, making it the most efficient option available. The proposed method’s accuracy rate was outperformed both the Hand-crafted feature and Deep Feature techniques, at 95.42 and 96.84 percent.

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Section: Related Workmentioning

confidence: 99%

Robust Malware Family Classification Using Effective Features and Classifiers

et al. 2022

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“…For the Internet of Things (IoT) security, risks and threats may occur on devices connected to the Internet in uncertain situations [ 4 , 5 ]. Moreover, cloud computing systems can be threatened from intruders in terms of the vulnerability of data integrity, authentication, and denial of service [ 6 , 7 ]. Therefore, recent security issues of machine learning systems led to complications in several real-life applications such as network traffic analysis, fraud detection, spam filtering, intrusion detection, phishing detection, ransomware detection, and malware classification [ 8 – 13 ].…”

Section: Introductionmentioning

confidence: 99%

One-pass-throw-away learning for cybersecurity in streaming non-stationary environments by dynamic stratum network

et al. 2018

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Throughout recent times, cybersecurity problems have occurred in various business applications. Although previous researchers proposed to cope with the occurrence of cybersecurity issues, their methods repeatedly replicated the training processes for several times to classify datasets of these problems in streaming non-stationary environments. In dynamic environments, the conventional methods possibly deteriorate the adaptive solution to prevent these issues. This research proposes a one-pass-throw-away learning using the dynamical structure of the network to solve these problems in dynamic environments. Furthermore, to speed up the computational time and to maintain a minimum space complexity for streaming data, the new concepts of learning in forms of recursive functions were introduced. The information gain-based feature selection was also applied to reduce the learning time during the training process. The experimental results signified that the proposed algorithm outperformed the others in incremental-like and online ensemble learning algorithms in terms of classification accuracy, space complexity, and computational time.

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