Cyber security has become increasingly challenging due to the proliferation of the Internet of things (IoT), where a massive number of tiny, smart devices push trillion bytes of data to the Internet. However, these devices possess various security flaws resulting from the lack of defense mechanisms and hardware security support, therefore making them vulnerable to cyber attacks. In addition, IoT gateways provide very limited security features to detect such threats, especially the absence of intrusion detection methods powered by deep learning. Indeed, deep learning models require high computational power that exceeds the capacity of these gateways. In this paper, we introduce Realguard, an DNN-based network intrusion detection system (NIDS) directly operated on local gateways to protect IoT devices within the network. The superiority of our proposal is that it can accurately detect multiple cyber attacks in real time with a small computational footprint. This is achieved by a lightweight feature extraction mechanism and an efficient attack detection model powered by deep neural networks. Our evaluations on practical datasets indicate that Realguard could detect ten types of attacks (e.g., port scan, Botnet, and FTP-Patator) in real time with an average accuracy of 99.57%, whereas the best of our competitors is 98.85%. Furthermore, our proposal effectively operates on resource-constraint gateways (Raspberry PI) at a high packet processing rate reported about 10.600 packets per second.

The increasing popularity of the Internet of Things (IoT) has significantly impacted our daily lives in the past few years. On one hand, it brings convenience, simplicity, and efficiency for us; on the other hand, the devices are susceptible to various cyber-attacks due to the lack of solid security mechanisms and hardware security support. In this paper, we present IMIDS, an intelligent intrusion detection system (IDS) to protect IoT devices. IMIDS’s core is a lightweight convolutional neural network model to classify multiple cyber threats. To mitigate the training data shortage issue, we also propose an attack data generator powered by a conditional generative adversarial network. In the experiment, we demonstrate that IMIDS could detect nine cyber-attack types (e.g., backdoors, shellcode, worms) with an average F-measure of 97.22% and outperforms its competitors. Furthermore, IMIDS’s detection performance is notably improved after being further trained by the data generated by our attack data generator. These results demonstrate that IMIDS can be a practical IDS for the IoT scenario.

There are many kinds of cassava leaf diseases firmly harm cassava yield, including four main types as followings: Cassava Bacterial Blight (CBB), Cassava Brown Streak Disease (CBSD), Cassava Green Mottle (CGM), and Cassava Mosaic Disease (CMD). In a traditional way, leaf diseases were diagnosed intuitively by farmers. This process is inefficient and unreliable. Several studies have recently relied on deep neural networks for identifying leaf diseases. In this research, we exploit the novel model named Vision Transformer (ViT) in place of a convolution neural network (CNN) for classifying cassava leaf diseases. Experimental results show that this model can obtain competitive accuracy at least 1% higher than popular CNN models (EfficientNet, Resnet50d) on Cassava Leaf Disease Dataset. These results also indicate the potential superiority of the ViT over established methods in analyzing leaf diseases. Next, we quantize the original model and successfully deploy it onto the Edge device named Raspberry Pi 4, which can be attached to a drone that allows farmers to automatically and efficiently detect infected leaves. This result has a significant capability for many future applications in smart agriculture.

Under power system short-circuits, the Doubly-Fed Induction Generator (DFIG) Wind Turbines (WT) are required to be equipped with crowbar protections to preserve the lifetime of power electronics devices. When the crowbar is switched on, the rotor windings are short-circuited. In this case, the DFIG behaves like a squirrel-cage induction generator (SCIG) and can adsorb reactive power, which can affect the power system. A DFIG based-fault-ride through (FRT) scheme with crowbar, rotor-side and grid-side converters has recently been proposed for improving the transient stability: in particular, a hybrid cascade Fuzzy-PI-based controlling technique has been demonstrated to be able to control the Insulated Gate Bipolar Transistor (IGBT) based frequency converter in order to enhance the transient stability. The performance of this hybrid control scheme is analyzed here and compared to other techniques, under a three-phase fault condition on a single machine connected to the grid. In particular, the transient operation of the system is investigated by comparing the performance of the hybrid system with conventional proportional-integral and fuzzy logic controller, respectively. The system validation is carried out in Simulink, confirming the effectiveness of the coordinated advanced fuzzy logic control.