Medical image registration using unsupervised deep neural network: A scoping literature review

In medicine, image registration is vital in image-guided interventions and other clinical applications. However, it is a difficult subject to be addressed which by the advent of machine learning, there have been considerable progress in algorithmic performance has recently been achieved for medical image registration in this area. The implementation of deep neural networks provides an opportunity for some medical applications such as conducting image registration in less time with high accuracy, playing a key role in countering tumors during the operation. The current study presents a comprehensive scoping review on the state-of-the-art literature of medical image registration studies based on unsupervised deep neural networks is conducted, encompassing all the related studies published in this field to this date. Here, we have tried to summarize the latest developments and applications of unsupervised deep learning-based registration methods in the medical field. Fundamental and main concepts, techniques, statistical analysis from different viewpoints, novelties, and future directions are elaborately discussed and conveyed in the current comprehensive scoping review. Besides, this review hopes to help those active readers, who are riveted by this field, achieve deep insight into this exciting field

A Deep Recurrent Neural Network Based Approach for Internet of Things Malware Threat Hunting

Internet of Things (IoT) devices are increasingly deployed in different industries and for different purposes (e.g. sensing/collecting of environmental data in both civilian and military settings). The increasing presence in a broad range of applications, and their increasing computing and processing capabilities make them a valuable attack target, such as malware designed to compromise specific IoT devices. In this paper, we explore the potential of using Recurrent Neural Network (RNN) deep learning in detecting IoT malware. Specifically, our approach uses RNN to analyze ARM-based IoT applications’ execution operation codes (OpCodes). To train our models, we use an IoT application dataset comprising 281 malware and 270 benign ware. Then, we evaluate the trained model using 100 new IoT malware samples (i.e. not previously exposed to the model) with three different Long Short Term Memory (LSTM) configurations. Findings of the 10-fold cross validation analysis show that the second configuration with 2-layer neurons has the highest accuracy (98.18%) in the detection of new malware samples. A comparative summary with other machine learning classifiers also demonstrate that the LSTM approach delivers the best possible outcome

AI4SAFE-IoT: an AI-powered secure architecture for edge layer of Internet of things

© 2020, Springer-Verlag London Ltd., part of Springer Nature. With the increasing use of the Internet of things (IoT) in diverse domains, security concerns and IoT threats are constantly rising. The computational and memory limitations of IoT devices have resulted in emerging vulnerabilities in most IoT-run environments. Due to the low processing ability, IoT devices are often not capable of running complex defensive mechanisms. Lack of an architecture for a safer IoT environment is referred to as the most important barrier in developing a secure IoT system. In this paper, we propose a secure architecture for IoT edge layer infrastructure, called AI4SAFE-IoT. This architecture is built upon AI-powered security modules at the edge layer for protecting IoT infrastructure. Cyber threat attribution, intelligent web application firewall, cyber threat hunting, and cyber threat intelligence are the main modules proposed in our architecture. The proposed modules detect, attribute, and further identify the stage of an attack life cycle based on the Cyber Kill Chain model. In the proposed architecture, we define each security module and show its functionality against different threats in real-world applications. Moreover, due to the integration of AI security modules in a different layer of AI4SAFE-IoT, each threat in the edge layer will be handled by its corresponding security module delivered by a service. We compared the proposed architecture with the existing models and discussed our architecture independence of the underlying IoT layer and its comparatively low overhead according to delivering security as service for the edge layer of IoT architecture instead of embed implementation. Overall, we evaluated our proposed architecture based on the IoT service management score. The proposed architecture obtained 84.7 out of 100 which is the highest score among peer IoT edge layer security architectures