Abstract These days it is found that malware authors tend to create new variants of existing Android malware by using various kinds of obfuscation techniques. These kinds of obfuscated malware applications can bypass all the current antimalware products which rely on static analysis techniques to detect the malicious behavior. Hence, it is essential to develop innovative dynamic analysis mechanisms for Android malware detection. It is known that, the malicious behavior of statically obfuscated malware applications can get reflected in the system call (syscall) trace generated by them. Most of the existing syscall based mechanisms depend only on the features derived from the syscall counts for malware detection. These syscall count related features are inadequate to capture many other useful characteristics related to the syscalls in a sequence. In order to overcome this limitation, we modeled the syscall trace of an application as an ordered graph which enabled to infer various kinds of features in the form of centrality measures related to that syscall trace of the application. Then, these centrality measures are fed to an ML model to predict the malicious behavior. From the implementation results, we found that our mechanism can detect malware apps with an accuracy of 0.99.

Finger vein has become an appealing biometric trait due to its intrinsic nature, contactless acquisition and anti-spoofing capability when compared to other dominant biometric traits. The state-of-the-art intrinsic recognition derives vein patterns based on either curvature values, line tracking or deep neural networks. However, these methods extract artifacts such as noise, breaks and texture along with veins due to the problems such as irregular shading, poor contrast and blurriness in NIR images which affect the recognition accuracy. To deal with these issues, we propose a novel acquisition mechanism for vein patterns based on the pulsation of the veins. We propose to capture the pulsations from vein videos to accurately isolate the vein patterns. Besides, the proposed framework has an inherent method of detecting liveness along with recognition of the finger vein. To the best of our knowledge, this is the first work that utilizes the finger vein pulsations for biometric recognition. We acquired a finger vein video dataset, from 320 subjects, to evaluate the proposed method. The experimental results indicate that the proposed approach has a better recognition performance compared to the existing image-based approaches with an EER (%) of 0.8 and a recognition accuracy of 96.35%.

Most of the existing Android malware detection mechanisms are based on machine learning algorithms. The problem with the machine learning approaches is the difficulty in finding the best features that uniquely characterize the malwares. Hence, in this article we explore the property that uniquely characterizes Android malware applications. Toward this, we model the system call sequence generated by a malware application as a stationary first-order ergodic Markov chain and prove the existence of typical patterns which contains the malicious system call code of the application. In our implementation, we find the occurrence of common malicious system call codes in the system call sequence of several malware families. Finally a malware detection mechanism is proposed based on the occurrence of malicious system call codes in the system call sequence of an application. We obtain a consistent accuracy of around 0.95 in balanced, slightly unbalanced, and highly unbalanced data sets. In the balanced and slightly unbalanced data sets we obtain greater precision than 0.90; whereas in the highly unbalanced data sets the precision obtained are slightly lower at 0.72.

Android malware analysis has been an active area of research as the number and types of Android malwares have increased dramatically. Most of the previous works have used permission based model, behavioral analysis, and code analysis to identify the family of a malware. Code Analysis are weak against obfuscated approach, it does not include real time execution of the application. Behavioral analysis captures the runtime behavior but is weak when it comes to obfuscated applications. Permission based model only uses manifest files for analysing malwares. In this paper, we propose a novel graph signature based malware classification mechanism . The proposed graph signature uses sensitive API calls to capture the flow of control which helps to find a caller-callee relationship between the sensitive APIs and the nodes incident on them. A dataset of graph signatures of widely known malware families are then created. A new application's graph signature is compared with graph signatures in the dataset and the application is classified into the respective malware family or declared as goodware/unknown. Experiments with 15 malware families from the AMD dataset and a total of 400 applications gave an average accuracy of 0.97 with an error rate of 0.03.

Finger vein identification has become a promising biometric modality due to its anti-spoofing capability, time-invariant nature, privacy and security when compared to other predominant biometric traits. In the wake of the recent epidemics and pandemics, the world has recognized the need for hygienic and contactless identification techniques such as finger vein. Although finger vein biometrics has been around for some time, there doesn't exist any classification scheme for finger vein images similar to the Henry classes for fingerprints. For large scale biometric identification systems, an accurate and consistent classification mechanism can significantly reduce the search space and time for matching. In this paper, we first show that finger vein patterns can be classified into four classes namely, Fork, Eye, Bridge and Arch (FEBA) and then propose an identification scheme based on this classification. To the best of our knowledge, this is the first-ever attempt on classifying finger vein images based on intrinsic anatomical features. We obtained a classification accuracy of 95.88% using convolutional neural network and an average reduction of 86.89% in matching time on a heterogeneous database consisting of 4 different datasets. Cross dataset validation and comparison with existing algorithms have been performed to show the efficacy of the proposed classification and matching mechanism.

Digital rights management systems are required to provide security and accountability without violating the privacy of the entities involved. However, achieving privacy along with accountability in the same framework is hard as these attributes are mutually contradictory. Thus, most of the current digital rights management systems rely on trusted third parties to provide privacy to the entities involved. However, a trusted third party can become malicious and break the privacy protection of the entities in the system. Hence, in this paper, we propose a novel privacy preserving content distribution mechanism for digital rights management without relying on the trusted third party assumption. We use simple primitives such as blind decryption and one way hash chain to avoid the trusted third party assumption. We prove that our scheme is not prone to the “oracle problem” of the blind decryption mechanism. The proposed mechanism supports access control without degrading user's privacy as well as allows revocation of even malicious users without violating their privacy.

A content distribution mechanism for DRM needs to satisfy the security and accountability requirements while respecting the privacy of the parties involved. However, achieving privacy along with accountability in the same framework is not easy as the requirement for achieving these attributes are conflicting each other. Most of the current content distribution mechanisms rely on trusted third parties to achieve privacy along with these attributes. In this paper, we propose a privacy preserving content distribution mechanism without requiring trust over any third party by using the mechanisms of blind decryption and one way hash chain. We prove that our scheme is not prone to the ‘oracle problem’ of the blind decryption mechanism. Our mechanism supports revocation of even malicious users without violating their privacy.

Now-a-days, surveillance cameras have been widely deployed in various security applications. In many surveillance applications, the background changes very slowly and the foreground objects occupy only a relatively small portion of a video frame. In these type of applications, an efficient solution for transmissions over bandwidth-limited networks is to send only the foreground objects for every frame in real time while the background is sent occasionally. At the receiving end of the transmission, the objects and the most recent background can be fused together and the original frame can be reconstructed. However, protecting the authenticity of the video becomes more challenging in this case as a malicious entity can modify/replace/remove the individual foreground objects and background in the video. In this paper, we propose a Chinese remainder theorem based watermarking mechanism for protecting the authenticity of videos transmitted or stored as objects and background. Our mechanism ensures the authenticity between video objects and their associated background.

In this paper, we propose a joint digital watermarking protocol for the multiparty multilevel DRM architecture using Garner's algorithm for the Chinese remainder theorem (CRT). Our protocol exploits the incremental nature of the computation of CRT by the Garner's algorithm. The proposed joint watermarking protocol embeds a single watermark signal into the content while taking care of the various security concerns such as proof of involvement in the distribution chain, nonrepudiation of the involvement and protection against false framing of the different parties involved. Further, in the event of finding an illegal copy of the content, the identities of all the parties involved in that content distribution chain can be traced back by extracting the watermark information.

For scalability of business, multiparty multilevel digital rights management (DRM) architecture, where a multimedia content is delivered by an owner to a consumer through several levels of distributors has been suggested as an alternative to the traditional two party (buyer-seller) DRM architecture.

Multiparty multilevel digital rights management (DRM) architecture involving several levels of distributors in between an owner and a consumer has been suggested as an alternative business model to the traditional two-party (buyer-seller) DRM architecture for digital content delivery. In the two-party DRM architecture, cryptographic techniques are used for secure delivery of the content, and watermarking techniques are used for protecting the rights of the seller and the buyer. The cryptographic protocols used in the two-party case for secure content delivery can be directly applied to the multiparty multilevel case. However, the watermarking protocols used in the two-party case may not directly carry over to the multiparty multilevel case, as it needs to address the simultaneous security concerns of multiple parties such as the owner, multiple levels of distributors, and consumers. Towards this, in this paper, we propose a joint digital watermarking scheme using Chinese remainder theorem for the multiparty multilevel DRM architecture. In the proposed scheme, watermark information is jointly created by all the parties involved; then a watermark signal is generated out of it and embedded into the content. This scheme takes care of the security concerns of all parties involved. Further, in the event of finding an illegal copy of the content, the violator(s) can be traced back.