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Remote attestation is an important characteristic of trusted computing technology which provides reliable evidence that a trusted environment actually exists. Existing approaches for the realization of remote attestation measure the trustworthiness of a target platform from its binaries, configurations, properties or security policies. All these approaches are low-level attestation techniques only, and none of them define what a trusted behavior actually is and how to specify it. In this paper, we present a novel approach where the trustworthiness of a platform is associated with the behavior of a policy model. In our approach, the behavior of a policy model is attested rather than a software or hardware platform. Thus, the attestation feature is not tied to a specific software or hardware platform, or to a particular remote attestation technique, or to an individual type of security policy. We select usage control (UCON) as our target policy model as it is a comprehensive and flexible model. We propose a framework to identify, specify, and attest different behaviors of UCON.

Wireless body area network (WBAN) is one of the specialized branches of wireless sensor networks (WSNs), which draws attention from various fields of science, such as medicine, engineering, physics, biology, and computer science. It has emerged as an important research area contributing to sports, social welfare, and medical treatment. One of the most important technologies of WBANs is routing technology. For efficient routing in WBANs, multiple network operations, such as network stability, throughput, energy efficiency, end-to-end delay, and packet delivery ratio, must be considered. In this paper, a robust and efficient Energy Harvested-aware Routing protocol with Clustering approach in Body area networks (EH-RCB) is proposed. It is designed with the intent to stabilize the operation of WBANs by choosing the best forwarder node, which is based on optimal calculated Cost Function (C.F). The C.F considers the link SNR, required transmission power, the distance between nodes, and total available energy, i.e., harvested energy and residual energy. Comprehensive simulation has been conducted, supported by NS-2 and C++ simulations tools to compare EH-RCB with existing protocols named DSCB, EERP, RE-ATTEMPT, and EECBSR. The results indicate a significant improvement in the EH-RCB in terms of the end-to-end delay network stability, packet delivery ratio, and network throughput. INDEX TERMS Clustering, end-to-end delay, harvesting, network stability, packet delivery ratio, throughput, WBANs.

Mobile and cell devices have empowered end users to tweak their cell phones more than ever and introduce applications just as we used to with personal computers. Android likewise portrays an uprise in mobile devices and personal digital assistants. It is an open‐source versatile platform fueling incalculable hardware units, tablets, televisions, auto amusement frameworks, digital boxes, and so forth. In a generally shorter life cycle, Android also has additionally experienced a mammoth development in application malware. In this context, a toweringly large measure of strategies has been proposed in theory for the examination and detection of these harmful applications for the Android platform. These strategies attempt to both statically reverse engineer the application and elicit meaningful information as features manually or dynamically endeavor to quantify the runtime behavior of the application to identify malevolence. The overgrowing nature of Android malware has enormously debilitated the support of protective measures, which leaves the platforms such as Android feeble for novel and mysterious malware. Machine learning is being utilized for malware diagnosis in mobile phones as a common practice and in Android distinctively. It is important to specify here that these systems, however, utilize and adapt the learning‐based techniques, yet the overhead of hand‐created features limits ease of use of such methods in reality by an end user. As a solution to this issue, we mean to make utilization of deep learning–based algorithms as the fundamental arrangement for malware examination on Android. Deep learning turns up as another way of research that has bid the scientific community in the fields of vision, speech, and natural language processing. Of late, models set up on deep convolution networks outmatched techniques utilizing handmade descriptive features at various undertakings. Likewise, our proposed technique to cater malware detection is by design a deep learning model making use of generative adversarial networks, which is responsible to detect the Android malware via famous two‐player game theory for a rock‐paper‐scissor problem. We have used three state‐of‐the‐art datasets and augmented a large‐scale dataset of opcodes extracted from the Android Package Kit bytecode and used in our experiments. Our technique achieves F1 score of 99% with a receiver operating characteristic of 99% on the bytecode dataset. This proves the usefulness of our technique and that it can generally be adopted in real life.

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