Operating Systems Security - 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.

Operating Systems Security - The operating system is the core of the smart power terminal. It is designed to strengthen security from five aspects: terminal container security, system security, security audit, communication protocol security, and hardware access control. By formulating a verification strategy, a comparative security test was carried out for the security hardening and non-security hardening operating systems of smart power terminals, and a detailed comparison test table was formed, demonstrating the importance of security hardening and security hardening for the operating systems of smart power terminals The advantages. The security-hardened operating system can effectively ensure the security of the operating environment of the terminal body and prevent illegal access by malicious programs.

Object Oriented Security - Several software vulnerabilities emerge during the design phase of a software development process, which can be addressed using secure design patterns. However, using these patterns over web application vulnerabilities is comparatively more tricky for developers than using traditional design patterns. Although several practices exist for addressing software security vulnerabilities, they are sometimes difficult to reuse due to their implementation-specific nature. In this study, we discuss the secure design patterns that are intended to prevent vulnerabilities from being accidentally introduced into code or reduce the effects of flaws. The patterns are created by combining current best security design practices and adding security-specific functionality to the existing design patterns. Hence, this work outlines a convenient mechanism for deciding which secure design patterns to use for addressing online application vulnerabilities. We have demonstrated the applicability of our concept over a prevalent database security threat, namely SQL injection.

Object Oriented Security - Aerial surveillance plays an important role for security applications. It can be further used to monitor borders, restricted zones and critical infrastructure. With the help of drones one can perform surveillance and get the exact location of various objects. Aerial object detection comes with many challenges like the object size which can be as low as 20×20 pixels. Images taken from satellites are hundreds of megapixels. Traditional methods like Histogram of oriented gradients (HOG) and Scale invariant feature transformation (SIFT) were used to extract features from the objects. Then these features were given to machine learning classifier like logistic regression, Support vector machine (SVM) and Random forest (RF) for detection and classification. However, the issue with these methods is that they are highly inaccurate and generated many false detections and misclassifications too. With the evolution of Graphics processing units (GPU) and the introduction of convolutional neural networks (CNN) as well as Deep Learning algorithms situation got changed. Now, it is possible to extract more information and provide better accuracy. In this paper for object detection You only look once version 4 (YOLOv4) is used which is one of the state-of-the-art algorithms. It uses Darknet 53 which is a type of CNN as a backbone for feature extraction. In this work the YOLOv4 based proposed system detect and localize vehicles present in the restricted zone and then geotag them.

Object Oriented Security - Service-oriented architecture (SOA) is a widely adopted architecture that uses web services, which have become increasingly important in the development and integration of applications. Its purpose is to allow information system technologies to interact by exchanging messages between sender and recipient using the simple object access protocol (SOAP), an XML document, or the HTTP protocol.

Object Oriented Security - Smart distribution grids have new protection concepts known as fault self-healing whereby Intelligent Electronic Devices (IEDs) can automatically reconfigure the power circuits to isolate faults and restore power to the relevant sections. This is typically implemented with IEDs exchanging IEC 61850 Generic Object Oriented Substation Event (GOOSE) messages in a peer-to-peer communication network. However, a selfhealing application may be faced by challenges of emerging cyber-physical security threats. These can result in disruption to the applications’ operations thereby affecting the power system reliability. Blockchain is one technology that has been deployed in several applications to offer security and bookkeeping. In this paper, we propose a novel concept using blockchain as a second-tier security mechanism to support time-critical selfhealing operations in smart distribution grids. We show through a simulation study the impact of our proposed architecture when compared with a normal self healing architecture. The results show that our proposed architecture can achieve significant savings in time spent in no-power state by portions of the grid during cyber-physical attacks.

Object Oriented Security - In object-oriented software development, UML has become a de facto modeling standard. However, although UML is easy to understand and apply, it has inaccurate semantics, and UML is a semi-formal modeling language, which cannot be formally verified. Event-B is a formal method based on a large number of mathematical predicate logic, which is precise but difficult to understand and apply. Therefore, how to combine the advantages of UML diagram and Event-B method is the focus of the research. The previous transformation methods are based on the transformation from UML scatter diagram to Event-B, which is prone to conflict and inconsistency. Therefore, we propose a systematic transformation method that can realize the corresponding unification of elements in UML and those in Event-B. The general software system is a medium-sized system. We believe that the medium-sized system can be clearly expressed by using use case diagram, class diagram, state diagram and sequence diagram. In this paper, the transformation methods from these four diagrams to EventB are given respectively. The transformation method of the system is applied to the elevator control system which requires high safety and reliability. The system transformation method from UML to Event-B not only improves the accuracy of UML and is easy for software practitioners to use, but also enhances the comprehensibility of formal methods and is conducive to the promotion and application of formal methods.

Object Oriented Security - The spread of the Internet of Things (IoT) and the use of smart control systems in many mission-critical or safetycritical applications domains, like automotive or aeronautical, make devices attractive targets for attackers. Nowadays, several of these are mixed-criticality systems, i.e., they run both highcriticality tasks (e.g., a car control system) and low-criticality ones (e.g., infotainment). High-criticality routines often employ Real-Time Operating Systems (RTOS) to enforce hard real-time requirements, while the tasks with lower constraints can be delegated to more generic-purpose operating systems (GPOS).

Object Oriented Security - At present, the traditional substation auxiliary control system is faced with the following four problems: poor real-time capability to abnormal response, high dependence on people when solving malfunctions, the communication, deployment and expansion of different underlying devices, and the lack of security mechanism. To solve these problems or optimize the functions, an intelligent substation auxiliary control system is proposed. The system innovatively applies OPC UA to the construction of the auxiliary control system. First, through the use of OPC UA s unique object-oriented modeling method as well as the joint specification modeling of OPC UA and IEC61850, to solve the data communication problems caused by heterogeneous devices. Second, applying the Client/Server mode to realize the remote access from authorized mobile clients and give instructions, to cope with abnormal conditions, which reduces the dependency on people. Clients of other authorized enterprises are allowed to access the working data of the devices they are interested in, makes full use of massive data and ensures the information security of the system. Third, Pub/Sub mode is applied to enable the underlying devices to communicate directly with each other through the middleware, which reduces the response time of equipment joint debugging and improve the real-time performance. In addition, through OPC UA, the industrial data of the system can be transmitted over the Internet, realizing the combination of the Internet of Things and the Internet, which is an idea of the combination of the two in the future.

Object Oriented Security - In Production System Engineering (PSE), domain experts aim at effectively and efficiently analyzing and mitigating information security risks to product and process qualities for manufacturing. However, traditional security standards do not connect security analysis to the value stream of the production system nor to production quality requirements. This paper aims at facilitating security analysis for production quality already in the design phase of PSE. In this paper, we (i) identify the connection between security and production quality, and (ii) introduce the Production Security Network (PSN) to efficiently derive reusable security requirements and design patterns for PSE. We evaluate the PSN with threat scenarios in a feasibility study. The study results indicate that the PSN satisfies the requirements for systematic security analysis. The design patterns provide a good foundation for improving the communication of domain experts by connecting security and quality concerns.

Object Oriented Security - For the last 20 years, the number of vulnerabilities has increased near 20 times, according to NIST statistics. Vulnerabilities expose companies to risks that may seriously threaten their operations. Therefore, for a long time, it has been suggested to apply security engineering – the process of accumulating multiple techniques and practices to ensure a sufficient level of security and to prevent vulnerabilities in the early stages of software development, including establishing security requirements and proper security testing. The informal nature of security requirements makes it uneasy to maintain system security, eliminate redundancy and trace requirements down to verification artifacts such as test cases. To deal with this problem, Seamless Object-Oriented Requirements (SOORs) promote incorporating formal requirements representations and verification means together into requirements classes.

Object Oriented Security - A growing number of attacks and the introduction of new security standards, e.g. ISO 21434, are increasingly shifting the focus of industry and research to the cybersecurity of vehicles. Being cyber-physical systems, compromised vehicles can pose a safety risk to occupants and the environment. Updates over the air and monitoring of the vehicle fleet over its entire lifespan are therefore established in current and future vehicles. Elementary components of such a strategy are security sensors in the form of firewalls and intrusion detection systems, for example, and an operations center where monitoring and response activities are coordinated. A critical step in defending against, detecting, and remediating attacks is providing knowledge about the vehicle and fleet context. Whether a vehicle is driving on the highway or parked at home, what software version is installed, or what security incidents have occurred affect the legitimacy of data and network traffic. However, current security measures lack an understanding of how to operate in an adjusted manner in different contexts. This work is therefore dedicated to a concept to make security measures for vehicles context-aware. We present our approach, which consists of an object-oriented model of relevant context information within the vehicle and a Knowledge Graph for the fleet. With this approach, various use cases can be addressed, according to the different requirements for the use of context knowledge in the vehicle and operations center.

Neural Style Transfer - With the emergence of deep perceptual image features, style transfer has become a popular application that repaints a picture while preserving the geometric patterns and textures from a sample image. Our work is devoted to the combination of perceptual features from multiple style images, taken at different scales, e.g. to mix large-scale structures of a style image with fine-scale textures. Surprisingly, this turns out to be difficult, as most deep neural representations are learned to be robust to scale modifications, so that large structures tend to be tangled with smaller scales. Here a multi-scale convolutional architecture is proposed for bi-scale style transfer. Our solution is based on a modular auto-encoder composed of two lightweight modules that are trained independently to transfer style at specific scales, with control over styles and colors.

Neural Style Transfer - With the development of economical society, the problem of product piracy security is becoming more and more serious. In order to protect the copyright of brands, based on the image neural style transfer, this paper proposes an automatic generation algorithm of anti-counterfeiting logo with security shading, which increases the difficulty of illegal copying and packaging production. VGG19 deep neural network is used to extract image features and calculate content response loss and style response loss. Based on the original neural style transfer algorithm, the content loss is added, and the generated security shading is fused with the original binary logo image to generate the anti-counterfeiting logo image with higher recognition rate. In this paper, the global loss function is composed of content loss, content response loss and style response loss. The L-BFGS optimization algorithm is used to iteratively reduce the global loss function, and the relationship between the weight adjustment, the number of iterations and the generated anti-counterfeiting logo among the three losses is studied. The secret keeping of shading style image used in this method increases the anti-attack ability of the algorithm. The experimental results show that, compared with the original logo, this method can generate the distinguishable logo content, complex security shading, and has convergence and withstand the attacks.

Neural Style Transfer - As one of the fields of computer art creation, style transfer has become more and more popular. However, in order to obtain good visual effects, a large number of neural style transfer algorithms use semantic map to guide the style transfer between the correct regions. As an important means to ensure the quality of style transfer, semantic map can meaningfully control the results of style transfer. However, the method of manually generating semantic graph is cumbersome and inefficient. In this paper, we introduce a semantic segmentation network to automatically generate the semantic map required by neural style transfer, and combine it with neural style transfer network, we propose a new neural style transfer algorithm. Experiments show that our algorithm not only avoids cumbersome manual work, but also generates high-quality style transfer results.

Neural Style Transfer - Style transfer is an optimizing technique that aims to blend style of input image to content image. Deep neural networks have previously surpassed humans in tasks such as object identification and detection. Deep neural networks, on the contrary, had been lagging behind in generating higher quality creative products until lately. This article introduces deep-learning techniques, which are vital in accomplishing human characteristics and open up a new world of prospects. The system employs a pre-trained CNN so that the styles of the provided image is transferred to the content image to generate high quality stylized image. The designed systems effectiveness is evaluated based on Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Metrics (SSIM), it is noticed that the designed method effectively maintains the structural and textural information of the cover image.

Neural Style Transfer - Arbitrary image style transfer is a challenging task which aims to stylize a content image conditioned on arbitrary style images. In this task the feature-level content-style transformation plays a vital role for proper fusion of features. Existing feature transformation algorithms often suffer from loss of content or style details, non-natural stroke patterns, and unstable training. To mitigate these issues, this paper proposes a new feature-level style transformation technique, named Style Projection, for parameter-free, fast, and effective content-style transformation. This paper further presents a real-time feed-forward model to leverage Style Projection for arbitrary image style transfer, which includes a regularization term for matching the semantics between input contents and stylized outputs. Extensive qualitative analysis, quantitative evaluation, and user study have demonstrated the effectiveness and efficiency of the proposed methods.

Neural Style Transfer - Deep learning has shown promising results in several computer vision applications, such as style transfer applications. Style transfer aims at generating a new image by combining the content of one image with the style and color palette of another image. When applying style transfer to a 4D Light Field (LF) that represents the same scene from different angular perspectives, new challenges and requirements are involved. While the visually appealing quality of the stylized image is an important criterion in 2D images, cross-view consistency is essential in 4D LFs. Moreover, the need for large datasets to train new robust models arises as another challenge due to the limited LF datasets that are currently available. In this paper, a neural style transfer approach is used, along with a robust propagation based on over-segmentation, to stylize 4D LFs. Experimental results show that the proposed solution outperforms the state-ofthe-art without any need for training or fine-tuning existing ones while maintaining consistency across LF views.

Neural Style Transfer - Text style transfer is a relevant task, contributing to theoretical and practical advancement in several areas, especially when working with non-parallel data. The concept behind nonparallel style transfer is to change a specific dimension of the sentence while retaining the overall context. Previous work used adversarial learning to perform such a task. Although it was not initially created to work with textual data, it proved very effective. Most of the previous work has focused on developing algorithms capable of transferring between binary styles, with limited generalization capabilities and limited applications. This work proposes a framework capable of working with multiple styles and improving content retention (BLEU) after a transfer. The proposed framework combines supervised learning of latent spaces and their separation within the architecture. The results suggest that the proposed framework improves content retention in multi-style scenarios while maintaining accuracy comparable to state-of-the-art.

Neural Style Transfer - Reducing inter-subject variability between new users and the measured source subjects, and effectively using the information of classification models trained by source subject data, is very important for human–machine interfaces. In this study, we propose a style transfer mapping (STM) and fine-tuning (FT) subject transfer framework using convolutional neural networks (CNNs). To evaluate the performance, we used two types of public surface electromyogram datasets named MyoDatasets and NinaPro database 5. Our proposed framework, STM-FT-CNN, showed the best performances in all cases compared with conventional subject transfer frameworks. In the future, we will build an online processing system that includes this subject transfer framework and verify its performance in online experiments.

Neural Style Transfer - Image style transfer is an important research content related to image processing in computer vision. Compared with traditional artificial computing methods, deep learning-based convolutional neural networks in the field of machine learning have powerful advantages. This new method has high computational efficiency and a good style transfer effect. To further improve the quality and efficiency of image style transfer, the pre-trained VGG-16 neural network model and VGG-19 neural network model are used to achieve image style transfer, and the transferred images generated by the two neural networks are compared. The research results show that the use of the VGG-16 convolutional neural network to achieve image style transfer is better and more efficient.

Neural Network Security - Trust is an essential concept in ad hoc network security. Creating and maintaining trusted relationships between nodes is a challenging task. This paper proposes a decentralized method for evaluating trust in ad hoc networks. The method uses neural networks and local information to predict the trust of neighboring nodes. The method was compared with the original centralized version, showing that even without global information knowledge, the method has, on average, 97\% accuracy in classification and 94\% in regression problem. An important contribution of this paper is overcoming the main limitation of the original method, which is the centralized evaluation of trust. Moreover, the decentralized method output is a perfect fit to use as an input to enhance routing in ad hoc networks.

Neural Network Security - Software-Defined Network (SDN) is a new networking paradigm that adopts centralized control logic and provides more control to the network operators over the network infrastructure to meet future network requirements. SDN controller known as operation system, which is responsible for running network applications and maintaining the different network services and functionalities. Despite all its great capabilities, SDN is facing different security threats due to its various architectural entities and centralized nature. Distributed Denial of Service (DDoS) is a promptly growing attack and becomes a major threat for the SDN. To date, most of the studies focus on detecting high-rate DDoS attacks at the control layer of SDN and low-rate DDoS attacks are high concealed because they are difficult to detect. Furthermore, the existing methods are useful for the detection of high-rate DDoS, so need to focus on low-rate DDoS attacks separately. Hence, the use of machine learning algorithms is growing for the detection of low-rate DDoS attacks in the SDN, but they achieved low accuracy against this attack. To improve the detection accuracy, this paper first describes the attack s mechanism and then proposes a Recurrent Neural Network (RNN) based method. The extracted features from the flow rules are used by the RNN for the detection of low-rate attacks. The experimental results show that the proposed method intelligently detects the attack, and its detection accuracy reaches 98.59\%. The proposed method achieves good detection accuracy as compared to existing studies.

Neural Network Security - Aiming at the network security problem caused by the rapid development of network, this paper uses a network traffic anomaly detection method of industrial control system based on convolutional neural network. In the traditional machine learning algorithm, the processing of features has a high impact on the performance of the model, and the model is highly dependent on features. This method uses the characteristics of convolutional neural network to autonomously learn features, which avoids this problem. In order to verify the superiority of the model, this paper takes accuracy as the evaluation index, and compares it with the traditional machine learning algorithm. The results show that the overall accuracy of the method is 99.88 \%, which has higher accuracy than traditional machine learning algorithms such as decision tree algorithm (ID3), adaptive boosting tree (Adboost) and naive Bayesian model. Therefore, this method can be better applied to the anomaly detection of network traffic in industrial control system, and has practical application value.

Neural Network Security - With the development of computer and network technology, industrial control systems are connecting with the Internet and other public networks in various ways, viruses, trojans and other threats are spreading to industrial control systems, industrial control system information security issues are becoming increasingly prominent. Under this background, it is necessary to construct the network security evaluation model of industrial control system based on the safety evaluation criteria and methods, and complete the safety evaluation of the industrial control system network according to the design scheme. Based on back propagation (BP) neural network’s evaluation of the network security status of industrial control system, this paper determines the number of neurons in BP neural network input layer, hidden layer and output layer by analyzing the actual demand, empirical equation calculation and experimental comparison, and designs the network security evaluation index system of industrial control system according to factors affecting industrial control safety, and constructs a safety rating table. Finally, by comparing the performance of BP neural network and multilinear regression to the evaluation of the network security status of industrial control system through experimental simulation, it can be found that BP neural network has higher accuracy for the evaluation of network security status of industrial control system.