will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Computer Science, Engineering and Information Technology. The Conference looks for significant contributions to all major fields of the Computer Science and Information Technology in theoretical and practical aspects. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
Second Batch : (Submissions after September 02, 2018)Submission Deadline
Authors are invited to submit papers through Submission System by September 29, 2018. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by Computer Science Conference Proceedings in Computer Science & Information Technology (CS & IT) series (Confirmed).
Hard copy of the Proceedings will be distributed during the Conference. The soft Copy will be available on AIRCC Digital Library.
Selected papers from CSEIT-2018, after further revisions, will be published in the special issue of the following journals
Information Technology in Industry (ITII)New - ESCI-Thomson Reuters Indexed
The implementation of smart grids and advanced metering infrastructure have lately been on the rise in different parts of the world for several good reasons. The smart grid is a cost-efficient, energy saving system for power generation and delivery while providing excellent power reliability and quality. As the smart grid involves data transmitted over a communication network, cybersecurity of the whole system is a serious issue. A 128-bit Advanced Encryption Scheme (AES) implemented on Smart meters resulted in encrypted energy usage data that is secure and only recoverable by its intended recipient.
Android smart phone is one of the fast growing mobile phones and because of these it the one of the most preferred target of malware developer. Malware apps can penetrate the device and gain privileges in which it can perform malicious activities such reading user contact, misusing of private information such as sending SMS and can harm user by exploiting the users private data which is stored in the device. This study is about implementation of detecting malwares on android applications, which would be the basis of all future development regarding malware detection. The Main reason why the researchers came up with this study is that majority of the smartphone users worldwide are not aware of the permissions as the basis of all malicious activities that could possibly operate in an android system and may steal personal and private information. Android operating system is an open system in which users are allowed to install application from any unsafe sites. However permission mechanism of and android system is not enough to guarantee the invulnerability of the application that can harm the user. In this paper, we propose a permission scoring-based analysis that will scrutinized the installed permission and allows user to increase the efficiency of Android permission to inform user about the risk of the installed Android apps. In this paper, we propose a framework that would classify the level of sensitivity of the permission access by the application. The framework uses a formula that will calculate the sensitivity level of the permission and to determine if the installed application is malicious or not. Our result show that, in a collection of 26 malicious application, the framework is able to correctly determine the application's behavior consistently and efficiently.
Conventional Data Mining (DM) algorithms treated data simply as numbers ignoring the semantic relationships among them. Consequently, recent researches claimed that ontology is the best option to represent the domain knowledge for data mining use because of its structural format. Additionally, it is reported that ontology can facilitate different steps in the Bayesian Network (BN) construction task. To this end, this paper investigates the advantages of consolidating the Gene Ontology (GO) and the Hierarchical Bayesian Network (HBN) classifier in a flexible framework, which preserves the advantages of both, ontology and Bayesian theory. The proposed Semantically Aware Hierarchical Bayesian Network (SAHBN) is tested using data set in the biomedical domain. DNA repair genes are classified as either ageing-related or non-ageing-related based on their GO biological process terms. Furthermore, the performance of SAHBN was compared against eight conventional classification algorithms. Overall, SAHBN has outperformed existing algorithms in eight experiments out of eleven.
A new inductive SIW band pass filter designed and optimized in this article. A third order Kaband SIW band pass filter centered in 33.8 GHz with 750 MHz bandwidth, is conceived, modeled and simulated. Good agreement between the simulated HFSS and simulated results by CST is observed.