Security researchers and anti-virus industries have speckled stress on an Android malware, which can actually damage your phones and threatens the Android markets. In this paper, we propose and develop DroidMD, a scalable self-improvement based tool, based on auto optimisation of signature set, which detect malicious apps in the market at source code level. A prototype has been developed tested and implemented to detect malware in applications. We implement and evaluate our approach on almost 30,000 applications including 27,000 benign and 3,670 malware applications. DroidMD detects malware in different applications at partial level and full level. It analyses only the applications code, which increase its reliability. Our evaluation of DroidMD demonstrates that our approach is very efficient in detecting malware at large scale with high accuracy of 95.5%.

In this study, we revisit the RSA public key cryptosystem in some special case of Boneh and Durfee's attack when the private key d assumes to be larger than the public key e. The attack in this study is the variation of an approach adopted by Luo et al. (2009) based on large decryption exponent. They had chosen a large private key (d > e) and found the weak keys in some specific range between N0.258 ≤ e ≤ N0.857. We highlight the shortcomings and new improvements in our study with more refined bound analysis up to the range between N0.104 ≤ e ≤ N0.923. Our experimental results revealed more refined bounds using lattice-based Coppersmith's method. In our experimental yield, we find the small roots of the devised polynomial, which helps to factorise the RSA modulus of size up to 1,024-bits. We also measure the probability of a specific range of weak keys, which further certify our results about weak keys in an RSA constrained secret key environment.

Summary Many techniques have been developed over the years to detect code clones in different software systems to maintain security measures. These techniques often require the source code to compare the subject system against a very large data set of big code. This paper presents index‐based features extraction technique (IBFET) to detect code clones at a very large‐scale level to billions of LOC at file level granularity. We performed preprocessing, indexing, and clone detection for more than 324 billion of LOC using a Hadoop distributed environment, which is quite faster and more efficient as compared to existing distributed indexing and clone detection techniques; meanwhile, it detects all three types of clones efficiently. The MapReduce rule of divide and conquer is used for a count and retrieve the similar features between different systems. We evaluated the execution time, scalability, precision, and recall of IBFET by using a well‐known clone detection data set IJaDataset and BigCloneBench; furthermore, we compared the results with other state‐of‐the‐art tools. Our approach is faster, flexible, scalable, and provides accurate results with high authenticity and can be implemented at a large‐scale level.

Android became more popular and widely used operating system. It has been noticed that the code clones in Android apps make it difficult to maintain the security flaws in source code. To avoid these problems, it is essential to find, identify, evaluate and recover those code clones as early as possible. In this paper, we propose and design DroidCC, a novel clone detection approach in Android applications, that helps to detect different types of clones from APK's source code. A prototype has been developed and implemented on the dataset of almost 30,000 top rated Android apps. DroidCC detects type-1, type-2 and type-3 clones in Android apps at the source code level. It also detects the similar code fragments, that were injected into many applications, which might be an indication of spreading malware. Meanwhile it can detect full and partial level similarity between applications. We evaluate DroidCC clone detection approach on real time data-set and count the Recall and Precision, which is quite significant. Furthermore, our results show that our approach is very efficient and effective in detecting different types of clones to check the similarity level in Android applications.

Code clone detection is a very hot topic in the field of software maintenance, reuseability and security.There is still a lack of techniques to detect near-miss clones at different level of granularities, especially in big code.This paper presents Distributed Code Clone Detection (DCCD) technique, which detects clones from big code bases based on feature extraction.We performed preprocessing, indexing and clone detection for almost 27 TB of source code (324 billion LOC), DCCD is quite faster and efficient as compared to existing distributed indexing and clone detection techniques, i.e. 36 times faster than Benjamin technique, which is 86 times faster than CCFinder.These two techniques are also distributed and just detect Type-1 and Type-2 clones, but our technique DCCD even detects Type-3 clones, efficiently.Our approach is faster, flexible, scalable and provides 87% accurate results with authenticity, ease of accessibility, upgradeability and maintainability.