Applied sciences

International Journal of Electronics and Telecommunications

Content

International Journal of Electronics and Telecommunications | 2019 | vol. 65 | No 4 |

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Abstract

The article herein presents the method and algorithms for forming the feature space for the base of intellectualized system knowledge for the support system in the cyber threats and anomalies tasks. The system being elaborated might be used both autonomously by cyber threat services analysts and jointly with information protection complex systems. It is shown, that advised algorithms allow supplementing dynamically the knowledge base upon appearing the new threats, which permits to cut the time of their recognition and analysis, in particular, for cases of hard-to-explain features and reduce the false responses in threat recognizing systems, anomalies and attacks at informatization objects. It is stated herein, that collectively with the outcomes of previous authors investigations, the offered algorithms of forming the feature space for identifying cyber threats within decisions making support system are more effective. It is reached at the expense of the fact, that, comparing to existing decisions, the described decisions in the article, allow separate considering the task of threat recognition in the frame of the known classes, and if necessary supplementing feature space for the new threat types. It is demonstrated, that new threats features often initially are not identified within the frame of existing base of threat classes knowledge in the decision support system. As well the methods and advised algorithms allow fulfilling the time-efficient cyber threats classification for a definite informatization object.

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Authors and Affiliations

Timur Kartbayev
Bakhytzhan Akhmetov
Aliya Doszhanova
Valery Lakhno
Feruza Malikova
Sharapatdin Tolybayev
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Abstract

A compact Sierpinski Carpet square fractal multiband antenna operating at 3.9 (WiMAX) /6.6 (Satellite TV) /8.1/10.7/11.8 GHz (X-band) is presented. The proposed Microstrip Patch Antenna (MSPA) consists of a Sierpinski Carpet square fractal radiator in which square slots are etched out and a tapered microstrip feed line. The Sierpinski Carpet square fractal patch modifies the current resonant path thereby making the antenna to operate at five useful bands. Impedance matching at these bands are solely achieved by using Sierpinski square slot and tapered feedline, thus eliminating the need of any external matching circuit. The dimensions of the compact antenna is 32 x 32 x 1,6 mm3 and exhibits S11<-10dB bandwidth of about 4.8% (4.01-3.82 GHz), 2.1% (6.62-6.48 GHz), 2.7% (8.24-8.02 GHz), 2.1% (10.77-10.54 GHz) and 21% (12.1-11.60 GHz) with the gain of 7.57/3.91/3.77/6.74/1.33 dB at the operating frequencies 3.9/6.6/8.1/10.7 and 11.8 GHz, respectively under simulation analysis carried out by using HFSS v.13.0.

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Authors and Affiliations

Imran Khan
Geetha D. Devanagavi
K.R. Sudhindra
K.M. Vandana
M.M. Manohara Pai
Tanweer Ali
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Abstract

New optimized 2x2 slotted array antenna is designed using HFSS to operate at 28.1 GHz, using RT6010 substrate with height of 1.6 mm, tangent loss of 0.0023 and dielectric constant of 10.2 and overall dimension of 12x12 mm2, for 5G mobile applications. The 2x2 slotted linear array antenna achieved a high gain of 18.3 dB at 28.1 GHz with 10 dB bandwidth of 1.39 GHz, and with 83.21% of size reduction.

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Authors and Affiliations

Shaikh Rehan Haneef
Sathish Kumar Selvaperumal
Vikneswary Jayapal
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Abstract

This paper presents a printed dual band monopole antenna working below 250 MHz using meander line and an added stub. Meander line approach is used to reduce the size of the low frequency monopole. The proposed antenna is fed by microstrip line and printed on FR-4 substrate with an overall size of 290 x 83 mm2. The added stub tuned dual band operation at 114 MHz and 221 MHz with measured reflection coefficient of -19 dB at both bands. The antenna has omni-directional characteristics with efficiency greater than 90% and gain of 1.87, 1.7 dBi at both bands respectively. The antenna design is optimized through a detailed parametric study. This study includes varying stub, Meander, feed and ground dimensions. The antenna has been fabricated and measured where dual band operation in the MHz range is verified.

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Authors and Affiliations

Angie R. Eldamak
Khalid M. Ibrahim
Mohamed Elkattan
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Abstract

The paper introduces a topology mutation – the novel concept in Moving Target Defense (MTD). MTD is a new technique that represents a significant shift in cyber defense. Traditional cybersecurity techniques have primarily focused on the passive defense of static networks only. In MTD approach cyber attackers are confused by making the attack surface dynamic, and thus harder to probe and infiltrate. The emergence of Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) technology has opened up new possibilities in network architecture management. The application of combined NFV and SDN technologies provides a unique platform for implementing MTD techniques for securing the network infrastructure by morphing the logical view of the network topology.

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Authors and Affiliations

Mariusz Rawski
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Abstract

Waste management is a challenging problem for most of the countries. The current waste segregation and the collection method are not efficient and cost-effective. In this paper, a prototype is presented for smart waste management. It is also capable of waste segregation at the ground level and providing real-time data to the administrator. Impact and cost analysis of the deployment of smartbin is also presented considering one ward of Ahmedabad Municipal Corporation. It is clear from that deployment of this smartbin will save about 40% of the current expenditure for that ward.

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Authors and Affiliations

Bhupendra Fataniya
Aayush Sood
Deepti Poddar
Dhaval Shah
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Abstract

In this paper a review on biometric person identification has been discussed using features from retinal fundus image. Retina recognition is claimed to be the best person identification method among the biometric recognition systems as the retina is practically impossible to forge. It is found to be most stable, reliable and most secure among all other biometric systems. Retina inherits the property of uniqueness and stability. The features used in the recognition process are either blood vessel features or non-blood vessel features. But the vascular pattern is the most prominent feature utilized by most of the researchers for retina based person identification. Processes involved in this authentication system include pre-processing, feature extraction and feature matching. Bifurcation and crossover points are widely used features among the blood vessel features. Non-blood vessel features include luminance, contrast, and corner points etc. This paper summarizes and compares the different retina based authentication system. Researchers have used publicly available databases such as DRIVE, STARE, VARIA, RIDB, ARIA, AFIO, DRIDB, and SiMES for testing their methods. Various quantitative measures such as accuracy, recognition rate, false rejection rate, false acceptance rate, and equal error rate are used to evaluate the performance of different algorithms. DRIVE database provides 100% recognition for most of the methods. Rest of the database the accuracy of recognition is more than 90%.

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Authors and Affiliations

Poonguzhali Elangovan
Malaya Kumar Nath
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Abstract

In the last few years, a great attention was paid to the deep learning Techniques used for image analysis because of their ability to use machine learning techniques to transform input data into high level presentation. For the sake of accurate diagnosis, the medical field has a steadily growing interest in such technology especially in the diagnosis of melanoma. These deep learning networks work through making coarse segmentation, conventional filters and pooling layers. However, this segmentation of the skin lesions results in image of lower resolution than the original skin image. In this paper, we present deep learning based approaches to solve the problems in skin lesion analysis using a dermoscopic image containing skin tumor. The proposed models are trained and evaluated on standard benchmark datasets from the International Skin Imaging Collaboration (ISIC) 2018 Challenge. The proposed method achieves an accuracy of 96.67% for the validation set .The experimental tests carried out on a clinical dataset show that the classification performance using deep learning-based features performs better than the state-of-the-art techniques.

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Authors and Affiliations

Fatma Sherif
Wael A. Mohamed
A.S. Mohra

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