Android malware detection based on image-based features and machine learning techniques

Bakour, Khaled/0000-0003-3327-2822WOS:000545934700001In this paper, a malware classification model has been proposed for detecting malware samples in the Android environment. The proposed model is based on converting some files from the source of the Android applications into grayscale images. Some image-based local features and global features, including four different types of local features and three different types of global features, have been extracted from the constructed grayscale image datasets and used for training the proposed model. To the best of our knowledge, this type of features is used for the first time in the Android malware detection domain. Moreover, the bag of visual words algorithm has been used to construct one feature vector from the descriptors of the local feature extracted from each image. The extracted local and global features have been used for training multiple machine learning classifiers including Random forest, k-nearest neighbors, Decision Tree, Bagging, AdaBoost and Gradient Boost. The proposed method obtained a very high classification accuracy reached 98.75% with a typical computational time does not exceed 0.018 s for each sample. The results of the proposed model outperformed the results of all compared state-of-art models in term of both classification accuracy and computational time

Analysis of efficiency of tunnel type induction furnace inductors with respect to variation of diameter of heated workpiece

UNVER, Umit/0000-0002-6968-6181WOS: 000427552600013In this study, calculations of net efficiency of heating process of aluminum billets those diameters are different from the diameter of the inductor were aimed. The targeted aluminum extrusion facilities utilize induction furnaces. In the heating experiments, an induction coil that was designed to heat circle divide 178 mm aluminum billets was used. The discs diameters are in accordance with the standard billet diameters. Additionally, a computer simulation of induction heating of discs was performed and the results were evaluated. It was observed that concentric or eccentric location of discs in the inductor induces an increment in the disc temperature and electrical power consumption and contrarily does not affect the heating efficiency. Therefore, it was determined that there is no need to construct some additional mechanisms to supply concentric location of the billet in the inductor. Finally, a user friendly efficiency scale function was obtained for the circle divide 178 mm inductor which is used for heating different diameter billets than the inductors design diameter

A new induction water heating system design for domestic heating

WOS: 000304512900039In the Middle Eastern and North African countries (between N36-N26 latitudes) where temperatures do not drop heavily in winter times, the resistance-boilers connected to the solar panels in serial offer a practical and economical solution for domestic heating. In this way, while solar power is benefited at the highest level, the desired room temperature can be achieved easily through the boiler having low resistance power. However, due to failure of resistances at short intervals and the need of laborious effort during the changing process, reduce the appeal of resistance-boilers. At this point, the boiler having a low breakdown probability and heating the water with induction heating principle offer a quite attractive solution. The aim of this study is to research induction water heating technique for electrical boiler applications. To this end, a special induction water-heating system was designed and produced. The designed system was run by single and two-phase electrical connection and satisfactory results were obtained. It is expected that an efficient heating system having low-cost operation and maintenance can be developed by improving this technique applicable for market.Konya University Scientific Research Projects OfficeNecmettin Erbakan UniversityThis study is sponsored by Konya University Scientific Research Projects Office

Diagnosis of Pneumonia from Chest X-Ray Images using Deep Learning

International Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science (EBBT) -- APR 24-26, 2019 -- Istanbul Arel Univ, Kemal Gozukara Campus, Istanbul, TURKEYWOS: 000491430200005Pneumonia is a disease which occurs in the lungs caused by a bacterial infection. Early diagnosis is an important factor in terms of the successful treatment process. Generally, the disease can be diagnosed from chest X-ray images by an expert radiologist. The diagnoses can be subjective for some reasons such as the appearance of disease which can be unclear in chest X-ray images or can be confused with other diseases. Therefore, computer-aided diagnosis systems are needed to guide the clinicians. In this study, we used two well-known convolutional neural network models Xception and Vgg16 for diagnosing of pneumonia. We used transfer learning and fine-tuning in our training stage. The test results showed that Vgg16 network exceed Xception network at the accuracy with 0.87%, 0.82% respectively. However, the Xception network achieved a more successful result in detecting pneumonia cases. As a result, we realized that every network has own special capabilities on the same dataset.IEEE Turkey Sect, IEEE EMB, Erasmus+, EuropassResearch Fund (Scientific Research Projects Coordination Unit) of the Kirikkale University [:2018/40]This work was supported by Research Fund (Scientific Research Projects Coordination Unit) of the Kirikkale University. Project Number:2018/40

Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm

WOS: 000487983100027PubMed: 31295856Skin lesion segmentation has a critical role in the early and accurate diagnosis of skin cancer by computerized systems. However, automatic segmentation of skin lesions in dermoscopic images is a challenging task owing to difficulties including artifacts (hairs, gel bubbles, ruler markers), indistinct boundaries, low contrast and varying sizes and shapes of the lesion images. This paper proposes a novel and effective pipeline for skin lesion segmentation in dermoscopic images combining a deep convolutional neural network named as You Only Look Once (YOLO) and the GrabCut algorithm. This method performs lesion segmentation using a dermoscopic image in four steps: 1. Removal of hairs on the lesion, 2. Detection of the lesion location, 3. Segmentation of the lesion area from the background, 4. Post-processing with morphological operators. The method was evaluated on two publicly well-known datasets, that is the PH2 and the ISBI 2017 (Skin Lesion Analysis Towards Melanoma Detection Challenge Dataset). The proposed pipeline model has achieved a 90% sensitivity rate on the ISBI 2017 dataset, outperforming other deep learning-based methods. The method also obtained close results according to the results obtained from other methods in the literature in terms of metrics of accuracy, specificity, Dice coefficient, and Jaccard index.Research Fund (Scientific Research Projects Coordination Unit) of the Kirikkale University [2018/40]This paper was supported by Research Fund (Scientific Research Projects Coordination Unit) of the Kirikkale University. Project Number: 2018/40

VisDroid: Android malware classification based on local and global image features, bag of visual words and machine learning techniques

Bakour, Khaled/0000-0003-3327-2822WOS:000550612900003In this paper, VisDroid, a novel generic image-based classification method has been suggested and developed for classifying the Android malware samples into its families. To this end, five grayscale image datasets each of which contains 4850 samples have been constructed based on different files from the contents of the Android malware samples sources. Two types of image-based features have been extracted and used to train six machine learning classifiers including Random Forest, K-nearest neighbour, Decision trees, Bagging, AdaBoost and Gradient Boost classifiers. The first type of the extracted features is local features including Scale-Invariant Feature Transform, Speeded Up Robust Features, Oriented FAST and Rotated BRIEF (ORB) and KAZE features. The second type of the extracted features is global features including Colour Histogram, Hu Moments and Haralick Texture. Furthermore, a hybridized ensemble voting classifier has been proposed to test the efficiency of using a number of machine learning classifiers trained using local and global features as voters to make a decision in an ensemble voting classifier. Moreover, two well-known deep learning model, i.e. Residual Neural Network and Inception-v3 have been tested using some of the constructed image datasets. Furthermore, when the results of the proposed model have been compared with the results of some state-of-art works it has been revealed that the proposed model outperforms the compared previous models in term of classification accuracy, computational time, generality and classification mode

The Android malware detection systems between hope and reality

Bakour, Khaled/0000-0003-3327-2822WOS: 000486959300076The widespread use of Android-based smartphones made it an important target for malicious applications' developers. So, a large number of frameworks have been proposed to tackle the huge number of daily published malwares. Despite there are many review papers that have been conducted in order to shed light on the works that achieved in Android malware analysing domain, the number of conducted review papers do not fit with the importance of this research field and with the volume of achieved works. Also, there is no comprehensive taxonomy for all research trends in the field of analysing malicious applications targeting the Android system. Furthermore, none of the existing review papers contains a schematic model that makes it easy for the reader to know the methods and methodologies used in a particular field of research without much effort. This paper aims at proposing a comprehensive taxonomy and suggesting a new schematic review approach.To this end, a review of a large number of works that achieved between 2009 and 2019 has been conducted. The achieved study includes more than 200 papers that have different goals such as apps' behaviour analysis, automatic user interface triggers or packer/unpacker frameworks development. Also, a comprehensive taxonomy has been proposed so that most of the previous works can be classified under it. To the best of our knowledge, the suggested taxonomy is the widest and the most comprehensive in terms of the covered research trends. Moreover, we have proposed a detailed schematic model (called Schematic Review Model) illustrates the process of detecting the malignant applications of an Android in the light of the studied works and the proposed taxonomy. To our knowledge, this is the first time that the Android malware detection methods have been explained in this way with this amount of detail. Furthermore, the studied researches have been analysed according to multiple criteria such as used analysing method, used features, used detection method, and used dataset. Also, the features used in the studied works were discussed in detail by dividing it into multiple classes. Moreover, the challenges facing Android's malware analysing methods were discussed in detail. Finally, it has been concluded that there are gaps between the size and the goal of the conducted works and the number of malicious apps published every day, so some future works areas have been proposed and discussed

A Deep Camouflage: Evaluating Android's Anti-malware Systems Robustness Against Hybridization of Obfuscation Techniques with Injection Attacks

Bakour, Khaled/0000-0003-3327-2822WOS: 000487119100025The threats facing smartphones have become one of the most dangerous cyberspace threats; therefore, many solutions have been developed in the commercial or academic domain to address these threats. This paper aims to test the defence robustness of some well-known commercial anti-malware systems against camouflage techniques. To this end, multiple attacks have been proposed and applied to create multiple camouflaged malware datasets based on well-known malware datasets. First of all, we proposed two injection attacks, namely benign permissions injection attack and benign permissions-code injection attack; these attacks have been used with one more attack called app re-signing attack. To the best of our knowledge, these injection attacks have been used for the first time in the Android OS domain. Furthermore, the proposed attacks have been hybridized with some commonly used obfuscation techniques, namely string encryption, class encryption, and reflection, to obtain a high degree of camouflage and avoiding anti-malware systems' detection. To our knowledge, this is the first time that the obfuscation techniques are hybridized with other attacks. The obtained results showed that the detection accuracy of most tested anti-malware systems dropped to about 10% or less. Moreover, the average number of engines which was able to detect malware samples decreased from 45 engines when the original dataset has been tested to about 12 engines when the camouflaged datasets have been tested

Analysis of a novel high performance induction air heater

This study represents an experimental and numerical investigation of the enhanced prototypes of the induction air heaters. For this purpose, flow field is enhanced in order to avoid turbulence. The air mass flow rate, outlet construction and the application of insulation of the outer surface of the heater were selected as the performance enhancing parameters. Depending on the exit construction, the new designed prototypes are named as K-2 and K-3. Experiments were performed under two groups for three various flow rates. In the first group, non-insulation situation is examined. In the second group tests, insulation is applied to the outside of windings and inlet-outlet flaps which constitute the boundary of the control volume for the prevention of heat losses. The increasing flow rate boosted the thermal efficiency by 9%. Each of insulation and enlarging exit cross section increased the thermal efficiency by 13%. It was observed that the thermal power transferred to air with the new prototypes increased about 246 W more than the previous designs. The thermal efficiencies of the K-2 and K-3 type heaters were calculated as 77.14% and 87.1%, respectively

Automatic Landmark Detection through Circular Hough Transform in Cephalometric X-rays

10th International Conference on Electrical and Electronics Engineering (ELECO) -- NOV 30-DEC 02, 2017 -- Bursa, TURKEYKOKVER, YUNUS/0000-0002-9864-2866; Erdem, Ayhan/0000-0001-7761-1078WOS: 000426978800096In this paper, a knowledge based framework is proposed to detect automatically cephalometric landmarks: Porion (Po), Sella (S), Menton (Me), Pogonion (Pg) and Gnathion (Gn). In this way anomalies can be diagnosed easily by orthodontists. Our framework comprise of two main steps: (1) Adaptive Histogram Equalisation (AHE) is applied to clarify the image which is used to determine the method of treatment in orthodontics and obtained from the plain X-ray. (2) Circular Hough Transform method is used to locate the cephalometric landmarks automatically on the processed image, the method was tested on 7 cephalometric images and our framework accurately and automatically locates these 5 cephalometric landmarks.Chamber Elect Engineers Bursa Sect Branch, Uludag Univ, Fac Engn, Dept Elect & Elect Engn, Istanbul Tech Univ, Fac Elect & Elect Engn, Sci & Technolog Res Council Turkey, IEEE Turkey Sec