Machine learning has seen tremendous advances in the past few years, which has lead to deep learning models being deployed in varied applications of day-to-day life. Attacks on such models using perturbations, particularly in real-life scenarios, pose a severe challenge to their applicability, pushing research into the direction which aims to enhance the robustness of these models. After the introduction of these perturbations by Szegedy et al. [1], significant amount of research has focused on the reliability of such models, primarily in two aspects -white-box, where the adversary has access to the targeted model and related parameters; and the black-box, which resembles a real-life scenario with the adversary having almost no knowledge of the model to be attacked. To provide a comprehensive security cover, it is essential to identify, study, and build defenses against such attacks. Hence, in this paper, we propose to present a comprehensive comparative study of various black-box adversarial attacks and defense techniques. CCS Concepts: • General and references → Surveys and overviews; • Security and privacy → Software and application security; • Computing methodologies → Computer vision; Machine learning.

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IoT (Internet of Things) is increasingly becoming more popular mainly due to the fact that almost all the smart devices nowadays are network enabled to facilitate many current and emerging applications. However, some important issues still need to be addressed before fully realizing the potential of IoT applications. One of the most important issues is to have effective approaches to planning various device actions to satisfy user requirements efficiently and securely in mobile IoT applications. A mobile IoT application can be composed of mobile cloud systems and devices, such as wearable devices, smart phones and smart cars. In this type of systems, mobile networks with elastic resources from various mobile clouds are effective to support IoT applications. In this paper an effective approach to intelligent planning for mobile IoT applications is presented. This approach includes a learning technique for dynamically assessing the users' mobile IoT application and a MDP (Markov Decision Process) planning technique for enhancing efficiency of IoT device action planning. Simulation results are presented to show the effectiveness of our approach.

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