An Android Malware Detection Framework-based on Permissions and Intents

With an exponential growth in smartphone applications targeting useful services such as banks, healthcare, m-commerce, security has become a primary concern. The applications downloaded from unofficial sources pose a security threat as they lack mechanisms for validation of the applications. The malware infected applications may lead to several threats such as leaking user’s private information, enforcing malicious deductions for sending premium SMS, getting root privilege of the android system and so on. Existing anti-viruses depend on signature databases that need to be updated from time to time and are unable to detect zero-day malware. The Android Operating system allows inter-application communication through the use of component reuse by using intents. Unfortunately, message passing is also an application attack surface. A hybrid method for android malware detection by analysing the permissions and intent-filters of the manifest files of the applications is presented. A malware detection framework is developed based on machine learning algorithms and on the basis of the decision tree obtained from ID3 and J48 classifiers available in WEKA. Both algorithms gave same results with an error percentage of 6 per cent. The system improves detection of zero day malware

Designing Secure and Survivable Stegosystems

Steganography, the art and science of carrying out hidden communication, is an emergingsub-discipline of information security. Unlike cryptography, steganography conceals the existenceof a secret message by embedding it in an innocuous container digital media, thereby enablingunobstrusive communication over insecure channels. Detection and extraction of steganographiccontents is another challenge for the information security professional and this activity iscommonly known as steganalysis. Recent progress in steganalysis has posed a challenge fordesign and development of stegosystems with high levels of security and survivability. In thispaper, different strategies have been presented that can be used to escape detection and foilan eavesdropper having high technical capabilities as well as adequate infrastructure. Based onthe strength and weaknesses of current steganographic schemes, ideas have been progressedto make detection and destruction of hidden information more difficult

A Secure Image Encryption Algorithm Based on Hill Cipher System

We present a technique of image encryption based on Hill cipher system that provides better security than existing approach of Bibhudendra Acharya et al. by rendering the image content completely scrambled using multiple self-invertible keys, block shuffling and a new developed pel transformation. The Hill cipher algorithm is one of the symmetric key algorithms having several advantages in encryption. However, the inverse of the matrix used for encrypting the plain text in this algorithm may not always exist. Moreover this algorithm is susceptible to known plain text attack. Our proposed algorithm is aimed at better encryption of all types of images even ones with uniform background and makes the image encryption scheme more secure

Applying Cuckoo Search for analysis of LFSR based cryptosystem

Cryptographic techniques are employed for minimizing security hazards to sensitive information. To make the systems more robust, cyphers or crypts being used need to be analysed for which cryptanalysts require ways to automate the process, so that cryptographic systems can be tested more efficiently. Evolutionary algorithms provide one such resort as these are capable of searching global optimal solution very quickly. Cuckoo Search (CS) Algorithm has been used effectively in cryptanalysis of conventional systems like Vigenere and Transposition cyphers. Linear Feedback Shift Register (LFSR) is a crypto primitive used extensively in design of cryptosystems. In this paper, we analyse LFSR based cryptosystem using Cuckoo Search to find correct initial states of used LFSR. Primitive polynomials of degree 11, 13, 17 and 19 are considered to analyse text crypts of length 200, 300 and 400 characters. Optimal solutions were obtained for the following CS parameters: Levy distribution parameter (β) = 1.5 and Alien eggs discovering probability (pa) = 0.25