Smart Card Security Enhancing System By using Symmetric Key Algorithm

Information technology plays a vital rolefor the development of smart cards. Smartcardscan change the form of the delivery ofservices and goods, through theautomatedidentification and verification ofcustomers, resulting in significant efficiency gainsandultimately lower costs for consumers. Peoplefrom different jobs of life extract informationfromthese smart cards. Smart cards have the potentialbenefit to people with the right ofprivacy and giveusers confidence in the trustworthiness ofcommercial organizations andstate institutions. Itcan also provide different kinds of facilities tousers and as well asorganizations such as accessand control.In this paper, the student security forstudent registration, library and school feepayment system is implemented by using Twofishalgorithm for high secure system.This systemprovides data entry for student affair, registrationfor student library card, hiring book for libraryand doing statistic for monthly school fee. Thisproposed system enhance characteristics ofsecurity features for confidentiality, integrity andavailability by using Twofish algorithm and SHA-1

Image Encryption based on AES Stream Cipher in Counter Mode

With the first evolution of digital data exchange, information security is becoming more important in data storage and transmission. Because of widely using images in industrial process, it is important to protect the confidential image data from unauthorized access. In this paper, we intend to develop software based image encryption system by applying AES in Counter Mode (AES-CTR) with an explicit initialization vector (IV). IV generation includes incrementing a counter for each packet and linear feedback shift registers (LFSRs). AES-CTR uses the AES block cipher to create stream cipher. AES-CTR uses the only AES encrypt operation for both encryption and decryption, making AES-CTR implementations smaller than implementations of many other AES modes. It is an attractive encryption algorithm for high-speed networking and improving the security of images from unauthorized access

Analysis on Malware Detection with Multi Classifiers on M0Droid and DroidScreening Datasets

The number of applications for smart mobiledevices is steadily growing with the continuousincrease in the utilization of these devices. theInstallation of malicious applications on smartdevices often arises the security vulnerabilities suchas seizure of personal information or the use of smartdevices in accordance with different purposes bycyber criminals. Therefore, the number of studies inorder to identify malware for mobile platforms hasincreased in recent years. In this study, permissionbasedmodel is used to detect the maliciousapplications on Android which is one of the mostwidely used mobile operating system. M0Droid andDroidScreening data sets have been analyzed usingthe Android application package files andpermission-based features extracted from these files.In our work, permission-based model which appliedpreviously across different data sets investigated toM0Droid and DroidScreening datasets and theexperimental results has been expanded. Whileobtaining results, feature set analyzed using differentclassification techniques. The results show thatpermission-based model is successful on M0Droidand DroidScreening data sets and Random Forestsoutperforms another method. When compared toM0Droid system model, it is obtained much bet terconclusions depend on success rate. Our approachprovides a method for automated static code analysisand malware detection with high accuracy andreduces smartphone malware analysis time