@article {Zerouaoui2022,
	title = {Deep hybrid architectures for binary classification of medical breast cancer images},
	journal = {Biomedical Signal Processing and Control},
	volume = {71},
	year = {2022},
	note = {cited By 16},
	abstract = {The diagnosis of breast cancer in the early stages significantly decreases the mortality rate by allowing the choice of adequate treatment. This study developed and evaluated twenty-eight hybrid architectures combining seven recent deep learning techniques for feature extraction (DenseNet 201, Inception V3, Inception ReseNet V2, MobileNet V2, ResNet 50, VGG16, and VGG19), and four classifiers (MLP, SVM, DT, and KNN) for a binary classification of breast pathological images over the BreakHis and FNAC datasets. The designed architectures were evaluated using: (1) four classification performance criteria (accuracy, precision, recall, and F1-score), (2) Scott Knott (SK) statistical test to cluster the proposed architectures and identify the best cluster of the outperforming architectures, and (3) the Borda Count voting method to rank the best performing architectures. The results showed the potential of combining deep learning techniques for feature extraction and classical classifiers to classify breast cancer in malignant and benign tumors. The hybrid architecture using the MLP classifier and DenseNet 201 for feature extraction (MDEN) was the top performing architecture with higher accuracy values reaching 99\% over the FNAC dataset, 92.61\%, 92\%, 93.93\%, and 91.73\% over the four magnification factor values of the BreakHis dataset: 40X, 100X, 200X, and 400X, respectively. The results of this study recommend the use of hybrid architectures using DenseNet 201 for the feature extraction of the breast cancer histological images because it gave the best results for both datasets BreakHis and FNAC, especially when combined with the MLP classifier. {\textcopyright} 2021 Elsevier Ltd},
	keywords = {accuracy, algorithm, Article, augmentation index, Binary classification, biopsy technique, Breast Cancer, Breast Cancer Histopathological Image Classification, Classification (of information), Classification algorithm, classifier, Cluster computing, clustering algorithm, Clustering algorithms, colloid carcinoma, Computer aided diagnosis, Computer architecture, construct validity, contrast limited adaptive histogram equalization, Convolutional neural network, Convolutional neural networks, deep hybrid architecture, Deep learning, Deep neural networks, Diseases, ductal carcinoma, external validity, Extraction, F1 score, Feature extraction, Features extraction, feed forward neural network, fibroadenoma, fine needle aspiration biopsy, histogram, Histological images, histology, Hybrid architectures, Image classification, image processing, Images processing, internal validity, learning algorithm, Learning algorithms, Learning techniques, lobular carcinoma, Machine learning, measurement precision, Medical imaging, MLP classifiers, Mortality rate, Network architecture, papillary carcinoma, Pathological images, phyllodes tumor, recall, residual neural network, scoring system, Scott Knott, Support vector machines},
	doi = {10.1016/j.bspc.2021.103226},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125746862\&doi=10.1016\%2fj.bspc.2021.103226\&partnerID=40\&md5=b9b74d0dcb135861bc2e3d820f836efa},
	author = {Zerouaoui, H. and Idri, A.}
}
@article {Houmz2021,
	title = {Detecting the impact of software vulnerability on attacks: A case study of network telescope scans},
	journal = {Journal of Network and Computer Applications},
	volume = {195},
	year = {2021},
	note = {cited By 1},
	abstract = {Network scanning is one of the first steps in gathering information about a target before launching attacks. It is used to scan for vulnerable devices and exposed services in order to exploit them. Such exploits can result in data breaches or network disruption, which can be very costly for organizations. There are many factors, including technical and non-technical, affecting the volume of scanning activities. In this paper, we study the impact of vulnerability disclosure on the volume of scans over time and propose a machine learning-based approach to predict this impact. We conducted a comprehensive data collection of network scans from two network telescopes hosted in different countries, as well as the disclosed vulnerabilities from 2014 to 2019. We then designed a set of features to characterize the disclosed vulnerabilities and used several classifiers to predict whether a vulnerability will impact the volume of daily scans. The resulting classifier achieves over 85\% accuracy in predicting the impact. In addition, we performed an analysis of the key characteristics of vulnerabilities that directly affect scanning activities. Our findings show that this approach is able to classify vulnerabilities that have an impact on network scans. The implementation of our model and validation tests proved the efficiency of the selected features, as well as the robustness of our model to classify vulnerabilities{\textquoteright} impact on scans. {\textcopyright} 2021 Elsevier Ltd},
	keywords = {Case-studies, Classification algorithm, CVE, Forecasting, Machine learning, Network scan, Network scanning, Network security, Network telescopes, NVD, OR-networks, Scanning, Software vulnerabilities, Telescopes, Times series},
	doi = {10.1016/j.jnca.2021.103230},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116335984\&doi=10.1016\%2fj.jnca.2021.103230\&partnerID=40\&md5=1d7f8d5d4111761b27fc93badc70f925},
	author = {Houmz, A. and Mezzour, G. and Zkik, K. and Ghogho, M. and Benbrahim, H.}
}
