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Low-Earth orbit (LEO) satellite (SAT) networks exhibit ultra-wide coverage under time-varying SAT network topology. Such wide coverage makes the LEO SAT network support the massive IoT, however, such massive access put existing multiple access protocols ill-suited. To overcome this issue, in this paper, we propose a novel contention-based random access solution for massive IoT in LEO SAT networks. Not only showing the performance of our proposed approach (see, Table II), but we also discuss the issue of scalability of deep reinforcement learning (DRL) by showing the convergence behavior (see, Table III and IV). 

This paper proposes a user scheduling and power allocation method for content delivery in wireless caching helper networks without any stringent constraint on the interference model. For supporting delay-sensitive and time-varying user demands, the actual delivery quantity of the requested content should be dynamically controlled by advanced scheduling and power allocation. In addition, it is difficult for a central unit to control the content delivery due to a lack of knowledge of the entire time-varying network; therefore, a belief-propagation (BP)-based algorithm that facilitates distributed decisions on user scheduling and power allocation at every caching helper is presented. The proposed delivery scheme maximizes power efficiency while limiting the average delay of user request satisfactions by managing interference among users well. Simulation results show that the proposed scheme provides almost the same delay performance as the exhaustively found optimal one at the expense of little power consumption. 

In wireless caching networks, a user generally has a concrete purpose of consuming contents in a certain preferred category, and requests multiple contents in sequence. While most existing research on wireless caching and delivery has focused only on one-shot requests, the popularity distribution of contents requested consecutively is definitely different from the one-shot request and has been not considered. Also, especially from the perspective of the service provider, it is advantageous for users to consume as many contents as possible. Thus, this paper proposes two cache allocation policies for categorized contents and consecutive user demands, which maximize 1) the cache hit rate and 2) the number of consecutive content consumption, respectively. Numerical results show how categorized contents and consecutive content requests have impacts on the cache allocation. 

Abstract In this paper, we introduce DRIFT, a system for detecting command and control (C2) domain names in Internet of Things–scale botnets. Using an intrinsic feature of malicious domain name queries prior to their registration (perhaps due to clock drift), we devise a difference‐based lightweight feature for malicious C2 domain name detection. Using NXDomain query and response of a popular malware, we establish the effectiveness of our detector with 99% accuracy and as early as more than 48 hours before they are registered. Our technique serves as a tool of detection where other techniques relying on entropy or domain generating algorithms reversing are impractical.