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Selective forwarding is a major problem in wireless sensor networks (WSNs). The nature of sensor environments and the sensitivity of collected measurements in some fields such as war fields increase the need to prevent, detect, or mitigate the problem. One of the most used countermeasures for such problem is the use of voting system based on watchdogs' votes. However, this approach is not applicable in the case of mobile sensors. Mobile WSNs (MWSNs) is growing immensely due to the exposure of applications of mobile computing, vehicular networks, and Internet of things. This exposure has shed light on the security of using mobile sensors and raises the need to set appropriate methods for securing MWSNs against many attacks such as selective forwarding attacks. This paper introduces the problem of selective forwarding in MWSNs and discusses how the voting system used for mitigation; this problem in WSNs is not applicable in handling the problem in MWSNs due to sensors mobility. Therefore, the paper proposes a model that provides a global monitoring capability for tracing moving sensors and detecting malicious ones. The model leverages the infrastructure of fog computing to achieve this purpose. In addition, the paper suggests using software defined systems to be used along with the proposed model, which generalize the model to be used to secure MWSNs against other types of attacks easily and flexibly. The paper provides a complete algorithm, a comprehensive discussion and experiments that show the correctness and importance of the proposed approach.

A large number of intelligent models for masked face recognition (MFR) has been recently presented and applied in various fields, such as masked face tracking for people safety or secure authentication. Exceptional hazards such as pandemics and frauds have noticeably accelerated the abundance of relevant algorithm creation and sharing, which has introduced new challenges. Therefore, recognizing and authenticating people wearing masks will be a long-established research area, and more efficient methods are needed for real-time MFR. Machine learning has made progress in MFR and has significantly facilitated the intelligent process of detecting and authenticating persons with occluded faces. This survey organizes and reviews the recent works developed for MFR based on deep learning techniques, providing insights and thorough discussion on the development pipeline of MFR systems. State-of-the-art techniques are introduced according to the characteristics of deep network architectures and deep feature extraction strategies. The common benchmarking datasets and evaluation metrics used in the field of MFR are also discussed. Many challenges and promising research directions are highlighted. This comprehensive study considers a wide variety of recent approaches and achievements, aiming to shape a global view of the field of MFR.

This article describes how as network traffic grows, attacks on traffic become more complicated and harder to detect. Recently, researchers have begun to explore machine learning techniques with cloud computing technologies to classify network threats. So, new and creative ways are needed to enhance intrusion detection system. This article addresses the source of the above issues through detecting an intrusion in cloud computing before it further disrupts normal network operations, because the complexity of malicious attack techniques have evolved from traditional malicious attack technologies (direct malicious attack), which include different malicious attack classes, such as DoS, Probe, R2L, and U2R malicious attacks, especially the zero-day attack in online mode. The proposed online intrusion detection cloud system (OIDCS) adopts the principles of the new spiking neural network architecture called NeuCube algorithm. It is proposed that this system is the first filtering system approach that utilizes the NeuCube algorithm. The OIDCS inherits the hybrid (supervised/unsupervised) learning feature of the NeuCube algorithm and uses this algorithm in an online system with lifelong learning to classify input while learning the system. The system is accurate, especially when working with a zero-day attack, reaching approximately 97% accuracy based on the to-be-remembered (TBR) encoding algorithm.

Wireless Sensor Networks (WSNs)

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