Deep Learning (DL) has gained popularity in network intrusion detection, due to its strong capability of recognizing subtle differences between normal and malicious network activities. Although a variety of methods have been designed to leverage DL models for security protection, whether these systems are vulnerable to adversarial examples is unknown. In this paper, we design a novel adversarial attack against DLbased Network Intrusion Detection Systems (NIDS) in the IoT environment, with only black-box accesses to the DL model in such NIDS. We introduce two techniques: (1) model extraction is adopted to replicate the black-box model with a small amount of training data; (2) a saliency map is then used to disclose the impact of each packet attribute on the detection results, and the most critical features. This enables us to efficiently generate adversarial examples using conventional methods. With these tehniques, we successfully compromise one state-of-the-art NIDS, Kitsune: the adversary only needs to modify less than 0.005% of bytes in the malicious packets to achieve an average 94.31% attack success rate.

With the development of modern Intelligent Transportation System (ITS), reliable and efficient transportation information sharing becomes more and more important. Although there are promising wireless communication schemes such as Vehicle-to-Everything (V2X) communication standards, information sharing in ITS still faces challenges such as the V2X communication overload when a large number of vehicles suddenly appeared in one area. This flash crowd situation is mainly due to the uncertainty of traffic especially in the urban areas during traffic rush hours and will significantly increase the V2X communication latency. In order to solve such flash crowd issues, we propose a novel system that can accurately predict the traffic flow and density in the urban area that can be used to avoid the V2X communication flash crowd situation. By combining the existing grid-based and graph-based traffic flow prediction methods, we use a Topological Graph Convolutional Network (ToGCN) followed with a Sequence-to-sequence (Seq2Seq) framework to predict future traffic flow and density with temporal correlations. The experimentation on a real-world taxi trajectory traffic data set is performed and the evaluation results prove the effectiveness of our method.

With the development of big data and network technology, there are more use cases like edge computing that require more secure and efficient multimedia big data transmission. Data compression methods can help to achieve many tasks like providing data integrity, protection, and efficient transmission. Classical multimedia big data compression relies on methods like the spatial-frequency transformation for compressing with loss. Recent approaches use Deep Learning (DL) to further explore the limit of the data compression methods in communication constrained scenarios like the Internet of Things (IoT). In this paper, we propose a novel method to significantly enhance the transformation-based compression standards like JPEG by transmitting much fewer data of one image at the sender's end. At the receiver's end, we propose a two-step method by combining the state-of-the-art signal processing based recovery method with a deep residual learning model to recover the transmitted data. Therefore, in the scenarios like IoT, the sender like edge device can transmit only 60% data of the original JPEG image without any additional calculation steps but the image quality can still be recovered at the receiver's end like cloud servers with PSNR more than 31 dB.

The rapid development of Deep Learning (DL) enables resource-constrained systems and devices (e.g., Internet of Things) to perform sophisticated Artificial Intelligence (AI) applications. However, AI models like Deep Neural Networks (DNNs) are known to be vulnerable to Adversarial Examples (AEs). Past works on defending against AEs require heavy computations in the model training or inference processes, making them impractical to be applied in IoT systems. In this paper, we propose a novel method, SUPER-IOT, to enhance the security and efficiency of AI applications in distributed IoT systems. Specifically, SUPER-IOT utilizes a pixel drop operation to eliminate adversarial perturbations from the input and reduce network transmission throughput. Then it adopts a sparse signal recovery method to reconstruct the dropped pixels and wavelet-based denoising method to reduce the artificial noise. SUPER-IOT is a lightweight method with negligible computation cost to IoT devices and little impact on the DNN model performance. Extensive evaluations show that it can outperform three existing AE defensive solutions against most of the AE attacks with better transmission efficiency.

Multipacket reception (MPR) is the capability of simultaneous decoding of more than one packet from multiple concurrent transmissions. Continuous investigations on increasing the reception capability are giving new scientific contributions. In this paper, we provide an overview of MPR-related research work covering (1) the theoretically proved impacts and advantages of using MPR from a channel perspective to network capacity and throughput; (2) the various technologies that enable MPR from transmitter, transreceiver, and receiver perspectives; (3) previous work on protocol improvement to better exploit MPR. Indeed, MPR approaches have been applied in modern wireless mobile systems but the focus of this paper is to discuss MPR in random access wireless networks. Using MPR in such multihop environments calls for new adaptation on protocols, especially a cross-layer approach. To this end, we detail a scheduling method that targets full utilization of MPR capability.