In the era of rapid network development, IoT devices are being deployed more and more widely, and various kinds of malware programs are gradually appearing at the deployment level. As a widely adopted static analysis approach, structure based analysis such as graph embedding can capture the semantic features of malware binaries and has received much research attention. In this paper, to further improve the robustness of the graph embedding approaches to IoT malware detection, we propose a novel method that incorporates both local and global characterizing features extracted from Function-Call Graphs (FCG) to perform the detection. The caller-callee relationship represents the local semantic features, and the global statistic feature represents the graph’s structural characteristics. The performance of the proposed method is evaluated on a largescale dataset consisting of 112K malware and 89k benignware samples collected from seven CPU architectures. It shows a 99% accuracy on IoT malware detection, outperforming existing graph embedding solutions. Moreover, when CPU architecture is taken into consideration, the proposed method combined with support vector machine and multilayer perception classifier can yield even higher performance.

In this paper, a dual-standard, dual-mode baseband receiver for 60 GHz wireless communication is presented. The receiver is designed to support SC and OFDM modes for both IEEE 802.15.3c and IEEE 802.11ad standards. The receiver is integrated with all-digital synchronization, radix-16 FFT, phase noise cancellation and low-complexity time-domain equalizer for line-of-sight channel application. The hardware utilization achieves 70% by leveraging hardware sharing between two modes and two standards for area efficiency. The receiver is implemented with 65 nm 1P9M process in 7.95 mm2core area. With feed-through architecture, the throughput rate supports up to 7.04 Gb/s and 15.84 Gb/s for SC mode (220 MHz) and OFDM mode (330 MHz), respectively.

We address the problem of information brokerage, where information consumers search for the data acquired by information producers. To the best of our knowledge, there exists no retrieval-guaranteed location-aware information brokerage scheme with a bounded data retrieval path length and bounded replication and retrieval message overhead costs available for use in 3D wireless ad hoc networks to date. In this paper, we propose a novel location-aware information brokerage scheme, termed LAIB, where the network area is divided into cube grids, and data are replicated and retrieved in the hashed geographic location in each grid designated by the producer and the consumer, respectively. In LAIB, a polylogarithmic number of grids are designated by the producer and by the consumer, and at least one grid, whose distance from the grid of the consumer is smaller than the distance from the grid of the consumer to the grid of the producer, is designated by both the producer and the consumer. Simulations show that, as the network area is divided into a moderate number of grids, LAIB has good performance in term of retrieval latency stretch while ensuring moderate replication memory, replication message, and retrieval message overhead costs.