Biblio
Enterprises round the globe have been searching for a way to securely empower AndroidTM devices for work but have spurned away from the Android platform due to ongoing fragmentation and security concerns. Discrepant vulnerabilities have been reported in Android smartphones since Android Lollipop release. Smartphones can be easily hacked by installing a malicious application, visiting an infectious browser, receiving a crafted MMS, interplaying with plug-ins, certificate forging, checksum collisions, inter-process communication (IPC) abuse and much more. To highlight this issue a manual analysis of Android vulnerabilities is performed, by using data available in National Vulnerability Database NVD and Android Vulnerability website. This paper includes the vulnerabilities that risked the dual persona support in Android 5 and above, till Dec 2017. In our security threat analysis, we have identified a comprehensive list of Android vulnerabilities, vulnerable Android versions, manufacturers, and information regarding complete and partial patches released. So far, there is no published research work that systematically presents all the vulnerabilities and vulnerability assessment for dual persona feature of Android's smartphone. The data provided in this paper will open ways to future research and present a better Android security model for dual persona.
With the development of the Internet of Things (IoT), it has been widely deployed. As many embedded devices are connected to the network and massive amounts of security-sensitive data are stored in these devices, embedded devices in IoT have become the target of attackers. The trusted computing is a key technology to guarantee the security and trustworthiness of devices' execution environment. This paper focuses on security problems on IoT devices, and proposes a security architecture for IoT devices based on the trusted computing technology. This paper implements a security management system for IoT devices, which can perform integrity measurement, real-time monitoring and security management for embedded applications, providing a safe and reliable execution environment and whitelist-based security protection for IoT devices. This paper also designs and implements an embedded security protection system based on trusted computing technology, containing a measurement and control component in the kernel and a remote graphical management interface for administrators. The kernel layer enforces the integrity measurement and control of the embedded application on the device. The graphical management interface communicates with the remote embedded device through the TCP/IP protocol, and provides a feature-rich and user-friendly interaction interface. It implements functions such as knowledge base scanning, whitelist management, log management, security policy management, and cryptographic algorithm performance testing.
Recently, the field of adversarial machine learning has been garnering attention by showing that state-of-the-art deep neural networks are vulnerable to adversarial examples, stemming from small perturbations being added to the input image. Adversarial examples are generated by a malicious adversary by obtaining access to the model parameters, such as gradient information, to alter the input or by attacking a substitute model and transferring those malicious examples over to attack the victim model. Specifically, one of these attack algorithms, Robust Physical Perturbations (RP2), generates adversarial images of stop signs with black and white stickers to achieve high targeted misclassification rates against standard-architecture traffic sign classifiers. In this paper, we propose BlurNet, a defense against the RP2 attack. First, we motivate the defense with a frequency analysis of the first layer feature maps of the network on the LISA dataset, which shows that high frequency noise is introduced into the input image by the RP2 algorithm. To remove the high frequency noise, we introduce a depthwise convolution layer of standard blur kernels after the first layer. We perform a blackbox transfer attack to show that low-pass filtering the feature maps is more beneficial than filtering the input. We then present various regularization schemes to incorporate this lowpass filtering behavior into the training regime of the network and perform white-box attacks. We conclude with an adaptive attack evaluation to show that the success rate of the attack drops from 90% to 20% with total variation regularization, one of the proposed defenses.