The digital transformation brought on by 5G is redefining current models of end-to-end (E2E) connectivity and service reliability to include security-by-design principles necessary to enable 5G to achieve its promise. 5G trustworthiness highlights the importance of embedding security capabilities from the very beginning while the 5G architecture is being defined and standardized. Security requirements need to overlay and permeate through the different layers of 5G systems (physical, network, and application) as well as different parts of an E2E 5G architecture within a risk-management framework that takes into account the evolving security-threats landscape. 5G presents a typical use-case of wireless communication and computer networking convergence, where 5G fundamental building blocks include components such as Software Defined Networks (SDN), Network Functions Virtualization (NFV) and the edge cloud. This convergence extends many of the security challenges and opportunities applicable to SDN/NFV and cloud to 5G networks. Thus, 5G security needs to consider additional security requirements (compared to previous generations) such as SDN controller security, hypervisor security, orchestrator security, cloud security, edge security, etc. At the same time, 5G networks offer security improvement opportunities that should be considered. Here, 5G architectural flexibility, programmability and complexity can be harnessed to improve resilience and reliability. The working group scope fundamentally addresses the following: •5G security considerations need to overlay and permeate through h the different layers of the 5G systems (physical, network, and application) as well as different parts of an E2E 5G architecture including a risk management framework that takes into account the evolving security threats landscape. •5G exemplifies a use-case of heterogeneous access and computer networking convergence, which extends a unique set of security challenges and opportunities (e.g., related to SDN/NFV and edge cloud, etc.) to 5G networks. Similarly, 5G networks by design offer potential security benefits and opportunities through harnessing the architecture flexibility, programmability and complexity to improve its resilience and reliability. •The IEEE FNI security WG s roadmap framework follows a taxonomic structure, differentiating the 5G functional pillars and corresponding cybersecurity risks. As part of cross collaboration, the security working group will also look into the security issues associated with other roadmap working groups within the IEEE Future Network Initiative.

In this fast growing technology and tight integration of physical devices in conventional networks, the resource management and adaptive scalability is a problematic undertaking particularly when it comes to network security measures. Current work focuses on software defined network (SDN) and network function virtualization (NFV) based security solution to address problems in network and security management. However, deployment, configuration and implementation of SDN/NFVbased security solution remains a real challenge. To overcome this research challenge, this paper presents the implementation of SDN-NFVs based network security solution. The proposed methodology is based on using open network operating system (ONOS) SDN Controller with Zodiac FX Openflow switches and virtual network functions (VNF). VNF comprises of virtual security functions (VSF) which includes firewall, intrusion prevention system (IPS) and intrusion detection system (IDS). One of the main contributions of this research is the implementation of security solution of an enterprise, utilizing SDN-NFV platform and commodity hardware. We demonstrate the successful implementation, configuration and deployment of the proposed NFVbased network security solution for an enterprise.

Virtualization is essential in assisting businesses in lowering operational costs while still ensuring increased productivity, better hardware utilization, and flexibility. According to Patrick Lin, Senior Director of Product Management for VMware, "virtualization is both an opportunity and a threat." This survey gives a review of the literature on major virtualization technology security concerns. Our study primarily focuses on several open security flaws that virtualization introduces into the environment. Virtual machines (VMs) are overtaking physical machine infrastructures due to their capacity to simulate hardware environments, share hardware resources, and make use of a range of operating systems (OS). By offering a higher level of hardware abstraction and isolation, efficient external monitoring and recording, and on-demand access, VMs offer more effective security architecture than traditional machines. It concentrates on virtual machine-specific security concerns. The security risks mentioned in this proposal apply to all of the virtualization technologies now on the market; they are not unique to any one particular virtualization technology. In addition to some security advantages that come along with virtualization, the survey first gives a brief review of the various virtualization technologies that are now on the market. It conclude by going into great depth on a number of security gaps in the virtualized environment.

5G core network introduces service based architecture, software defined network, network function virtualization and other new technologies, showing the characteristics of IT and Internet. The new architecture and new technologies not only bring convenience to 5G but also introduce new security threats, especially the unknown security threats caused by unknown vulnerabilities or backdoors. This paper mainly introduces the security threats after the application of software defined network, network function virtualization and other technologies to 5G, summarizes the security solutions proposed by standardization organizations and academia, and puts forward a new idea of building a high-level secure 5G core network based on the endogenous safety and security.

By analyzing the design requirements of a secure desktop virtualization information system, this paper proposes the security virtualization technology of "whitelist" security mechanism, the virtualization layer security technology of optimized design, and the virtual machine security technology of resource and network layer isolation. On this basis, this paper constructs the overall architecture of the secure desktop virtualization information system. This paper studies the desktop virtualization technology research based on VMware using VMware server virtualization solution to transform and upgrade the traditional intelligent desktop virtualization system, improve server resource utilization rate, and reduce operation and maintenance costs.

This paper is an in-depth analysis of Virtualization Software, specifically – Oracle VM VirtualBox. Here, we analyze the existing system and determine the first two phases of the Secure Software Development Process. Here we go over the requirements elicitation, the architecture, and design phases of the secure software development lifecycle. We selected SQUARE methodology to identify the security requirements. Also, we used the Microsoft Threat Modeler tool for threat modeling. Finally, we identified major secure design patterns.

The experimental results demonstrated that, With the development of cloud computing, more and more people use cloud computing to do all kinds of things. However, for cloud computing, the most important thing is to ensure the stability of user data and improve security at the same time. From an analysis of the experimental results, it can be found that Cloud computing makes extensive use of technical means such as computing virtualization, storage system virtualization and network system virtualization, abstracts the underlying physical facilities into external unified interfaces, maps several virtual networks with different topologies to the underlying infrastructure, and provides differentiated services for external users. By comparing and analyzing the experimental results, it is clear that virtualization technology will be the main way to solve cloud computing security. Virtualization technology introduces a virtual layer between software and hardware, provides an independent running environment for applications, shields the dynamics, distribution and differences of hardware platforms, supports the sharing and reuse of hardware resources, provides each user with an independent and isolated computer environment, and facilitates the efficient and dynamic management and maintenance of software and hardware resources of the whole system. Applying virtualization technology to cloud security reduces the hardware cost and management cost of "cloud security" enterprises to a certain extent, and improves the security of "cloud security" technology to a certain extent. This paper will outline the basic cloud computing security methods, and focus on the analysis of virtualization cloud security technology.

To improve the quality of network security service, the physical device service mode in traditional security service is improved, and the NFV network security service system is constructed by combining software defined networking (SDN) and network function virtualization technology (NFV). Where, network service is provided in the form of security service chain, and Web security scan service is taken as the task, finally the implementation and verification of the system are carried out. The test result shows that the security service system based on NFV can balance the load between the security network service devices in the Web security scan, which proves that the network security system based on software defined security and NFV technology can meet certain service requirements, and lays the research foundation for the improvement of the subsequent user network security service.

Cloud computing is a cutting-edge innovation that will improve the design of applications in terms of elasticity, functionality, and collaborative execution. It is a computer system that mainly depends on the Internet. The most important feature of cloud computing is virtualization, which enables on-site dynamic allocation of academic computing resources or industrial resources. Virtualization can be defined as "forming a virtual version of something, such as a server, desktop, storage device, operating system, or network resource," according to Wikipedia. The goal of this study is to demonstrate how virtualization can contribute to the improvement of cloud computing services. This study also takes a deeper look at source virtualization strategies, as well as emerging security challenges and future research goals.

In this paper, the reader s attention is directed to the problem of inefficiency of the add-on information security tools, that are installed in operating systems, including virtualization systems. The paper shows the disadvantages, that significantly affect the maintenance of an adequate level of security in the operating system. The results allowing to control all areas hierarchical of protection of the specialized operating system are presented.

System is used independently, for sudden emergencies, the traditional security protection system can t inform the staff relevant situations comprehensively and automatically. It is not conductive for the staff to catch early warning and handle emergency events. Meanwhile, the management of independent subsystems is complicated. So, establishment of a unified management and control platform is proposed to integrate sorts of information. The paper elaborates information integration architecture based on video surveillance, supporting technologies and linkage application functions. By establishing logical relationship, all subsystems are integrated into a united and interactive security protection system which has the ability of automatic identification, automatic forecasting and processing. It reflects the economic philosophy that equipment utilization maximization.

In the field operation, crossing the fence is a common illegal behavior, which needs to be paid attention to. Especially, the live part of the power station site is mixed with the power outage part, and some construction workers cross the fence to enter the live area, which can easily cause safety problems. The power station has a wide range of operations, and the manual monitoring method is inefficient. With the popularization of video monitoring devices in power stations, this paper proposes a detection and identification method for fence crossing violations based on video monitoring. The method extracts video frames as input, uses convolution to extract temporal and spatial features, and classifies and regresses the features fused in time and space, which can effectively identify fence crossing behaviors. Finally, a video processing platform is built to process alarms for illegal operations. Engineering practice shows that the method shown in the article can effectively predict the illegal crossing of the fence in the power station and improve the intelligent monitoring level of the power station.

Video anomaly detection in the surveillance video is one of the essential components of the intelligent video surveillance system. However, anomaly detection remains an ill-defined problem, despite the diverse applications due to its rareness and equivocal nature. A Long Short Term Memory - Variational Autoencoder (LSTM-VAE) model is proposed to detect video anomalies. The model consists of a spatial encoder comprised of convolutional layers, a temporal encoder as well as a decoder comprised of Convolutional LSTM (ConvLSTM), and a spatial decoder consisting of transposed convolution layers. The generative model is trained only on normal video clips with the objective of minimizing the reconstruction error. Then, the trained model is tested on the test video sequences comprised of both normal and abnormal incidents. The reconstruction error corresponding to the test frame sequences having video anomalies will be very high as the model is not trained to reconstruct them. Subsequently, the corresponding frames will have a low regularity score. An appropriate threshold regularity score is set to segregate the anomaly frames from the normal ones. Frames having a regularity score less than the set threshold value are considered as anomalous frames. The model is developed by using one of the publicly available bench-marked video anomaly datasets, i.e., UCSD Ped2. The performance metrics of the proposed model are promising.

A smart university is supposed to be a safe university. At this moment we observe multiple cameras in different locations in the Hall University and rooms to detect suspicious behavior such as violation, larceny or persons in a state of alcohol or drug intoxication. Samples of the video footage is monitored 24/7 by operators in control rooms. Currently the recorded videos are visual assessed after a suspicious event has occurred. There is a requirement for realtime surveillance with smart cameras which can detect, track and analyze suspicious behavior over place and time. The expanding number of cameras requires an enormous measure of observing operators. This paper proposes a distributed intelligent surveillance system based on smart cameras. We seek to improve the Quality of Experience QoE operator side or QoEvideo surveillance expressed in function of i- resource availability constraints, ii- false detection of suspicious behavior, iii- define an optimal perimeter for intrusion detection (subset of cameras, network parameters required . . . ).

Smart Surveillance Systems are becoming an important aspect of our lives, reducing man labour and additionally increasing the accuracy of detection by reducing false positives. Specifically for an ATM, Surveillance system is very crucial because of the transactions happening being sensitive along with that drop-box containing confidential documents like cheques and bank forms. Hence, there is a need to develop a fool-proof system which can handle a lot of load and perform various surveillance tasks. Moreover, the systems also need to have network security to protect the data from being illegally traced and changed. In this paper, we will be reviewing and comparing various smart surveillance system methods which involve various technologies.

Understanding dynamic human behavior based on online video has many applications in security control, crime surveillance, sports, and industrial IoT systems. This paper solves the problem of classifying video data recorded on surveillance cameras in order to identify fragments with instances of shoplifting. It is proposed to use a classifier that is a symbiosis of two neural networks: convolutional and recurrent. The convolutional neural network is used for extraction of features from each frame of the video fragment, and the recurrent network for processing the temporal sequence of processed frames and subsequent classification.

With the development of technology, the technological informationization of the security network video surveillance service industry has become the demand of the times. How to improve the functions of the video surveillance system and build an open security integrated monitoring management platform has become the research point of this article. This article intends to build a video surveillance system based on database technology to meet the multi-functional requirements of the surveillance system. This article mainly uses experimental methods to test the data of the monitoring system designed in this article, and then uses the comparative method to compare the speed of the three methods to calculate the data, and the data results are obtained. According to the experiment, the data processing time of the binary algorithm in the video surveillance system is within 15s. Image detection in database technology uses binary algorithms to operate and analyze it more quickly.

Surveillance is an observation of a place, large areas, behavior, or a variety of activities to acquire information, influence, manage, or guide it. When people talk about surveillance solutions, the growing demand for large area monitoring becomes one of the key trends in the security industry. Surveillance video is used in real-time to watch known threats. Suspicious activities through surveillance video are a major topic in image processing and deep learning research.Residential area security is very much important to people nowadays. The proposed system is concerned with the development of a surveillance video framework in the residential area to detect any type of suspicious robbery activity. This system makes effective use of deep learning techniques of yolo, this includes techniques like object detection and eventually identifying the actions required to prevent robberies.Surveillance cameras are used here to remotely monitor a residential area or building by transmitting recorded images or videos to a control station to thwart suspicious activities. As a result, deep learning techniques are employed to achieve outstanding detection of suspicious actions that yielded positive results..

In recent years, in order to continuously promote the construction of safe cities, security monitoring equipment has been widely used all over the country. How to use computer vision technology to realize effective intelligent analysis of violence in video surveillance is very important to maintain social stability and ensure people s life and property safety. Video surveillance system has been widely used because of its intuitive and convenient advantages. However, the existing video monitoring system has relatively single function, and generally only has the functions of monitoring video viewing, query and playback. In addition, relevant researchers pay less attention to the complex abnormal behavior of violence, and relevant research often ignores the differences between violent behaviors in different scenes. At present, there are two main problems in video abnormal behavior event detection: the video data of abnormal behavior is less and the definition of abnormal behavior in different scenes cannot be clearly distinguished. The main existing methods are to model normal behavior events first, and then define videos that do not conform to the normal model as abnormal, among which the learning method of video space-time feature representation based on deep learning shows a good prospect. In the face of massive surveillance videos, it is necessary to use deep learning to identify violent behaviors, so that the machine can learn to identify human actions, instead of manually monitoring camera images to complete the alarm of violent behaviors. Network training mainly uses video data set to identify network training.

As an important component of security systems, the number of video surveillance systems is growing rapidly year by year. However, video surveillance systems often have many network security problems, and there is no perfect solution at present. To address these security issues, we propose a TPM-based security enhancement design for video surveillance systems. We enhance the security of the video surveillance system from the perspective of its own environmental security, video data security and device authentication, combined with the TPM module s trusted metrics, trusted authentication and key management mechanisms. We have developed and implemented a prototype video surveillance system and conducted experiments. The experimental results show that the framework we designed can greatly enhance the security of the video surveillance system while ensuring performance.

Wearables Security 2022 - In the twenty-first century, given the worldwide situation, the first concern of any female is her personal protection. Women Labor Day and night to sustain themselves and their families. These women are more susceptible to attacks and assaults, and their security and safety are paramount issues. This technique proposed several new goods to safeguard women. Among the products that may be employed is a smart jacket for women s safety. The proposed approach also includes features to send alert notification to family members with Geo location live tracking and live camera video streaming placed on the jacket for the emergency attention when women are not secure. This gadget is an appeal to all women to earn the right to a safe and secure planet.

Wearables Security 2022 - In aura and era of the Internet of Things (IoT) and the fourth industrial revolution, modern wearable electronic devices and their communication networks are marching into every corner of modern society and changing every aspect of our daily life. Thus, the progress of digitalization including miniaturization of sensor and wearable technology and its growing importance of physical and psychological wellbeing have a tremendous impact on almost all consumer goods from wearable to nonwearable industries. Different types of signals are used in communication between the devices for wireless transmission of data. such as Radio Frequency, Infrared, and Lightwave Transmissions. Wearable devices are becoming a hot topic in many fields such as medical, fashion, education, etc. Digital dependency of WIoT devices, introduced new security challenges, and vulnerabilities. This research is focused on Fitness Wearable Technology Devices Security and Privacy Vulnerability Analysis and highlights the importance of this topic by revealing the potential security concerns. Fog Computing, Sidera and Blockchain technologies were researched as Security Techniques to enhance security and efficiency while providing access to medical and personal records.

Wearables Security 2022 - Mobile devices such as smartphones are increasingly being used to record personal, delicate, and security information such as images, emails, and payment information due to the growth of wearable computing. It is becoming more vital to employ smartphone sensor-based identification to safeguard this kind of information from unwanted parties. In this study, we propose a sensor-based user identification approach based on individual walking patterns and use the sensors that are pervasively embedded into smartphones to accomplish this. Individuals were identified using a convolutional neural network (CNN). Four data augmentation methods were utilized to produce synthetically more data. These approaches included jittering, scaling, and time-warping. We evaluate the proposed identification model’s accuracy, precision, recall, F1-score, FAR, and FRR utilizing a publicly accessible dataset named the UIWADS dataset. As shown by the experiment findings, the CNN with the timewarping approach operates with very high accuracy in user identification, with the lowest false positive rate of 8.80\% and the most incredible accuracy of 92.7\%.

Wearables Security 2022 - Healthcare has become one of the most important aspects of people s lives, resulting in a surge in medical big data. Healthcare providers are increasingly using Internet of Things (IoT)-based wearable technologies to speed up diagnosis and treatment. In recent years, Through the Internet, billions of sensors, gadgets, and vehicles have been connected. One such example is for the treatment and care of patients, technology—remote patient monitoring—is already commonplace. However, these technologies also offer serious privacy and data security problems. Data transactions are transferred and logged. These medical data security and privacy issues might ensue from a pause in therapy, putting the patient s life in jeopardy. We planned a framework to manage and analyse healthcare large data in a safe manner based on blockchain. Our model s enhanced privacy and security characteristics are based on data sanitization and restoration techniques. The framework shown here make data and transactions more secure.

Wearables Security 2022 - One of the biggest new trends in artificial intelligence is the ability to recognise people s movements and take their actions into account. It can be used in a variety of ways, including for surveillance, security, human-computer interaction, and content-based video retrieval. There have been a number of researchers that have presented vision-based techniques to human activity recognition. Several challenges need to be addressed in the creation of a vision-based human activity recognition system, including illumination variations in human activity recognition, interclass similarity between scenes, the environment and recording setting, and temporal variation. To overcome the above mentioned problem, by capturing or sensing human actions with help of wearable sensors, wearable devices, or IoT devices. Sensor data, particularly one-dimensional time series data, are used in the work of human activity recognition. Using 1D-Convolutional Neural Network (CNN) models, this works aims to propose a new approach for identifying human activities. The Wireless Sensor Data Mining (WISDM) dataset is utilised to train and test the 1D-CNN model in this dissertation. The proposed HAR-CNN model has a 95.2\%of accuracy, which is far higher than that of conventional methods.