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2019-08-12
Wang, Bingning, Liu, Kang, Zhao, Jun.  2018.  Deep Semantic Hashing with Multi-Adversarial Training. Proceedings of the 27th ACM International Conference on Information and Knowledge Management. :1453–1462.
With the amount of data has been rapidly growing over recent decades, binary hashing has become an attractive approach for fast search over large databases, in which the high-dimensional data such as image, video or text is mapped into a low-dimensional binary code. Searching in this hamming space is extremely efficient which is independent of the data size. A lot of methods have been proposed to learn this binary mapping. However, to make the binary codes conserves the input information, previous works mostly resort to mean squared error, which is prone to lose a lot of input information [11]. On the other hand, most of the previous works adopt the norm constraint or approximation on the hidden representation to make it as close as possible to binary, but the norm constraint is too strict that harms the expressiveness and flexibility of the code. In this paper, to generate desirable binary codes, we introduce two adversarial training procedures to the hashing process. We replace the L2 reconstruction error with an adversarial training process to make the codes reserve its input information, and we apply another adversarial learning discriminator on the hidden codes to make it proximate to binary. With the adversarial training process, the generated codes are getting close to binary while also conserves the input information. We conduct comprehensive experiments on both supervised and unsupervised hashing applications and achieves a new state of the arts result on many image hashing benchmarks.
Benzer, R., Yildiz, M. C..  2018.  YOLO Approach in Digital Object Definition in Military Systems. 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT). :35–37.

Today, as surveillance systems are widely used for indoor and outdoor monitoring applications, there is a growing interest in real-time generation detection and there are many different applications for real-time generation detection and analysis. Two-dimensional videos; It is used in multimedia content-based indexing, information acquisition, visual surveillance and distributed cross-camera surveillance systems, human tracking, traffic monitoring and similar applications. It is of great importance for the development of systems for national security by following a moving target within the scope of military applications. In this research, a more efficient solution is proposed in addition to the existing methods. Therefore, we present YOLO, a new approach to object detection for military applications.

Eetha, S., Agrawal, S., Neelam, S..  2018.  Zynq FPGA Based System Design for Video Surveillance with Sobel Edge Detection. 2018 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS). :76–79.

Advancements in semiconductor domain gave way to realize numerous applications in Video Surveillance using Computer vision and Deep learning, Video Surveillances in Industrial automation, Security, ADAS, Live traffic analysis etc. through image understanding improves efficiency. Image understanding requires input data with high precision which is dependent on Image resolution and location of camera. The data of interest can be thermal image or live feed coming for various sensors. Composite(CVBS) is a popular video interface capable of streaming upto HD(1920x1080) quality. Unlike high speed serial interfaces like HDMI/MIPI CSI, Analog composite video interface is a single wire standard supporting longer distances. Image understanding requires edge detection and classification for further processing. Sobel filter is one the most used edge detection filter which can be embedded into live stream. This paper proposes Zynq FPGA based system design for video surveillance with Sobel edge detection, where the input Composite video decoded (Analog CVBS input to YCbCr digital output), processed in HW and streamed to HDMI display simultaneously storing in SD memory for later processing. The HW design is scalable for resolutions from VGA to Full HD for 60fps and 4K for 24fps. The system is built on Xilinx ZC702 platform and TVP5146 to showcase the functional path.

Liu, Y., Yang, Y., Shi, A., Jigang, P., Haowei, L..  2019.  Intelligent monitoring of indoor surveillance video based on deep learning. 2019 21st International Conference on Advanced Communication Technology (ICACT). :648–653.

With the rapid development of information technology, video surveillance system has become a key part in the security and protection system of modern cities. Especially in prisons, surveillance cameras could be found almost everywhere. However, with the continuous expansion of the surveillance network, surveillance cameras not only bring convenience, but also produce a massive amount of monitoring data, which poses huge challenges to storage, analytics and retrieval. The smart monitoring system equipped with intelligent video analytics technology can monitor as well as pre-alarm abnormal events or behaviours, which is a hot research direction in the field of surveillance. This paper combines deep learning methods, using the state-of-the-art framework for instance segmentation, called Mask R-CNN, to train the fine-tuning network on our datasets, which can efficiently detect objects in a video image while simultaneously generating a high-quality segmentation mask for each instance. The experiment show that our network is simple to train and easy to generalize to other datasets, and the mask average precision is nearly up to 98.5% on our own datasets.

Fok, Wilton W. T., Chan, Louis C. W., Chen, Carol.  2018.  Artificial Intelligence for Sport Actions and Performance Analysis Using Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). Proceedings of the 2018 4th International Conference on Robotics and Artificial Intelligence. :40–44.
The development of Human Action Recognition (HAR) system is getting popular. This project developed a HAR system for the application in the surveillance system to minimize the man-power for providing security to the citizens such as public safety and crime prevention. In this research, deep learning network using Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) are used to analyze dynamic video motion of sport actions and classify different types of actions and their performance. It could classify different types of human motion with a small number of video frame for efficiency and memory saving. The current accuracy achieved is up to 92.9% but with high potential of further improvement.
Wu, Yifan, Drucker, Steven, Philipose, Matthai, Ravindranath, Lenin.  2018.  Querying Videos Using DNN Generated Labels. Proceedings of the Workshop on Human-In-the-Loop Data Analytics. :6:1–6:6.
Massive amounts of videos are generated for entertainment, security, and science, powered by a growing supply of user-produced video hosting services. Unfortunately, searching for videos is difficult due to the lack of content annotations. Recent breakthroughs in image labeling with deep neural networks (DNNs) create a unique opportunity to address this problem. While many automated end-to-end solutions have been developed, such as natural language queries, we take on a different perspective: to leverage both the development of algorithms and human capabilities. To this end, we design a query language in tandem with a user interface to help users quickly identify segments of interest from the video based on labels and corresponding bounding boxes. We combine techniques from the database and information visualization communities to help the user make sense of the object labels in spite of errors and inconsistencies.
Verdoliva, Luisa.  2018.  Deep Learning in Multimedia Forensics. Proceedings of the 6th ACM Workshop on Information Hiding and Multimedia Security. :3–3.
With the widespread diffusion of powerful media editing tools, falsifying images and videos has become easier and easier in the last few years. Fake multimedia, often used to support fake news, represents a growing menace in many fields of life, notably in politics, journalism, and the judiciary. In response to this threat, the signal processing community has produced a major research effort. A large number of methods have been proposed for source identification, forgery detection and localization, relying on the typical signal processing tools. The advent of deep learning, however, is changing the rules of the game. On one hand, new sophisticated methods based on deep learning have been proposed to accomplish manipulations that were previously unthinkable. On the other hand, deep learning provides also the analyst with new powerful forensic tools. Given a suitably large training set, deep learning architectures ensure usually a significant performance gain with respect to conventional methods, and a much higher robustness to post-processing and evasions. In this talk after reviewing the main approaches proposed in the literature to ensure media authenticity, the most promising solutions relying on Convolutional Neural Networks will be explored with special attention to realistic scenarios, such as when manipulated images and videos are spread out over social networks. In addition, an analysis of the efficacy of adversarial attacks on such methods will be presented.
Khryashchev, Vladimir, Ivanovsky, Leonid, Priorov, Andrey.  2018.  Deep Learning for Real-Time Robust Facial Expression Analysis. Proceedings of the International Conference on Machine Vision and Applications. :66–70.
The aim of this investigation is to classify real-life facial images into one of six types of emotions. For solving this problem, we propose to use deep machine learning algorithms and convolutional neural network (CNN). CNN is a modern type of neural network, which allows for rapid detection of various objects, as well as to make an effective object classification. For acceleration of CNN learning stage, we use supercomputer NVIDIA DGX-1. This process was implemented in parallel on a large number of independent streams on GPU. Numerical experiments for algorithms were performed on the images of Multi-Pie image database with various lighting of scene and angle rotation of head. For developed models, several metrics of quality were calculated. The designing algorithm was used in real-time video processing in human-computer interaction systems. Moreover, expression recognition can apply in such fields as retail analysis, security, video games, animations, psychiatry, automobile safety, educational software, etc.
Islam, Ashraful, Zhang, Yuexi, Yin, Dong, Camps, Octavia, Radke, Richard J..  2018.  Correlating Belongings with Passengers in a Simulated Airport Security Checkpoint. Proceedings of the 12th International Conference on Distributed Smart Cameras. :14:1–14:7.
Automatic algorithms for tracking and associating passengers and their divested objects at an airport security screening checkpoint would have great potential for improving checkpoint efficiency, including flow analysis, theft detection, line-of-sight maintenance, and risk-based screening. In this paper, we present algorithms for these tracking and association problems and demonstrate their effectiveness in a full-scale physical simulation of an airport security screening checkpoint. Our algorithms leverage both hand-crafted and deep-learning-based approaches for passenger and bin tracking, and are able to accurately track and associate objects through a ceiling-mounted multicamera array. We validate our algorithm on ground-truthed datasets collected at the simulated checkpoint that reflect natural passenger behavior, achieving high rates of passenger/object/transfer event detection while maintaining low false alarm and mismatch rates.
Peixoto, Bruno Malveira, Avila, Sandra, Dias, Zanoni, Rocha, Anderson.  2018.  Breaking Down Violence: A Deep-learning Strategy to Model and Classify Violence in Videos. Proceedings of the 13th International Conference on Availability, Reliability and Security. :50:1–50:7.
Detecting violence in videos through automatic means is significant for law enforcement and analysis of surveillance cameras with the intent of maintaining public safety. Moreover, it may be a great tool for protecting children from accessing inappropriate content and help parents make a better informed decision about what their kids should watch. However, this is a challenging problem since the very definition of violence is broad and highly subjective. Hence, detecting such nuances from videos with no human supervision is not only technical, but also a conceptual problem. With this in mind, we explore how to better describe the idea of violence for a convolutional neural network by breaking it into more objective and concrete parts. Initially, our method uses independent networks to learn features for more specific concepts related to violence, such as fights, explosions, blood, etc. Then we use these features to classify each concept and later fuse them in a meta-classification to describe violence. We also explore how to represent time-based events in still-images as network inputs; since many violent acts are described in terms of movement. We show that using more specific concepts is an intuitive and effective solution, besides being complementary to form a more robust definition of violence. When compared to other methods for violence detection, this approach holds better classification quality while using only automatic features.
2019-08-05
Zhang, Chengyu, Yan, Yichen, Zhou, Hanru, Yao, Yinbo, Wu, Ke, Su, Ting, Miao, Weikai, Pu, Geguang.  2018.  Smartunit: Empirical Evaluations for Automated Unit Testing of Embedded Software in Industry. Proceedings of the 40th International Conference on Software Engineering: Software Engineering in Practice. :296-305.

In this paper, we aim at the automated unit coverage-based testing for embedded software. To achieve the goal, by analyzing the industrial requirements and our previous work on automated unit testing tool CAUT, we rebuild a new tool, SmartUnit, to solve the engineering requirements that take place in our partner companies. SmartUnit is a dynamic symbolic execution implementation, which supports statement, branch, boundary value and MC/DC coverage. SmartUnit has been used to test more than one million lines of code in real projects. For confidentiality motives, we select three in-house real projects for the empirical evaluations. We also carry out our evaluations on two open source database projects, SQLite and PostgreSQL, to test the scalability of our tool since the scale of the embedded software project is mostly not large, 5K-50K lines of code on average. From our experimental results, in general, more than 90% of functions in commercial embedded software achieve 100% statement, branch, MC/DC coverage, more than 80% of functions in SQLite achieve 100% MC/DC coverage, and more than 60% of functions in PostgreSQL achieve 100% MC/DC coverage. Moreover, SmartUnit is able to find the runtime exceptions at the unit testing level. We also have reported exceptions like array index out of bounds and divided-by-zero in SQLite. Furthermore, we analyze the reasons of low coverage in automated unit testing in our setting and give a survey on the situation of manual unit testing with respect to automated unit testing in industry.

Yao, Zhihao, Ma, Zongheng, Liu, Yingtong, Amiri Sani, Ardalan, Chandramowlishwaran, Aparna.  2018.  Sugar: Secure GPU Acceleration in Web Browsers. Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems. :519-534.

Modern personal computers have embraced increasingly powerful Graphics Processing Units (GPUs). Recently, GPU-based graphics acceleration in web apps (i.e., applications running inside a web browser) has become popular. WebGL is the main effort to provide OpenGL-like graphics for web apps and it is currently used in 53% of the top-100 websites. Unfortunately, WebGL has posed serious security concerns as several attack vectors have been demonstrated through WebGL. Web browsers\guillemotright solutions to these attacks have been reactive: discovered vulnerabilities have been patched and new runtime security checks have been added. Unfortunately, this approach leaves the system vulnerable to zero-day vulnerability exploits, especially given the large size of the Trusted Computing Base of the graphics plane. We present Sugar, a novel operating system solution that enhances the security of GPU acceleration for web apps by design. The key idea behind Sugar is using a dedicated virtual graphics plane for a web app by leveraging modern GPU virtualization solutions. A virtual graphics plane consists of a dedicated virtual GPU (or vGPU) as well as all the software graphics stack (including the device driver). Sugar enhances the system security since a virtual graphics plane is fully isolated from the rest of the system. Despite GPU virtualization overhead, we show that Sugar achieves high performance. Moreover, unlike current systems, Sugar is able to use two underlying physical GPUs, when available, to co-render the User Interface (UI): one GPU is used to provide virtual graphics planes for web apps and the other to provide the primary graphics plane for the rest of the system. Such a design not only provides strong security guarantees, it also provides enhanced performance isolation.

Xu, Cheng, Xu, Jianliang, Hu, Haibo, Au, Man Ho.  2018.  When Query Authentication Meets Fine-Grained Access Control: A Zero-Knowledge Approach. Proceedings of the 2018 International Conference on Management of Data. :147-162.

Query authentication has been extensively studied to ensure the integrity of query results for outsourced databases, which are often not fully trusted. However, access control, another important security concern, is largely ignored by existing works. Notably, recent breakthroughs in cryptography have enabled fine-grained access control over outsourced data. In this paper, we take the first step toward studying the problem of authenticating relational queries with fine-grained access control. The key challenge is how to protect information confidentiality during query authentication, which is essential to many critical applications. To address this challenge, we propose a novel access-policy-preserving (APP) signature as the primitive authenticated data structure. A useful property of the APP signature is that it can be used to derive customized signatures for unauthorized users to prove the inaccessibility while achieving the zero-knowledge confidentiality. We also propose a grid-index-based tree structure that can aggregate APP signatures for efficient range and join query authentication. In addition to this, a number of optimization techniques are proposed to further improve the authentication performance. Security analysis and performance evaluation show that the proposed solutions and techniques are robust and efficient under various system settings.

Gennaro, Rosario, Minelli, Michele, Nitulescu, Anca, Orrù, Michele.  2018.  Lattice-Based Zk-SNARKs from Square Span Programs. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. :556-573.

Zero-knowledge SNARKs (zk-SNARKs) are non-interactive proof systems with short and efficiently verifiable proofs. They elegantly resolve the juxtaposition of individual privacy and public trust, by providing an efficient way of demonstrating knowledge of secret information without actually revealing it. To this day, zk-SNARKs are being used for delegating computation, electronic cryptocurrencies, and anonymous credentials. However, all current SNARKs implementations rely on pre-quantum assumptions and, for this reason, are not expected to withstand cryptanalitic efforts over the next few decades. In this work, we introduce the first designated-verifier zk-SNARK based on lattice assumptions, which are believed to be post-quantum secure. We provide a generalization in the spirit of Gennaro et al. (Eurocrypt'13) to the SNARK of Danezis et al. (Asiacrypt'14) that is based on Square Span Programs (SSPs) and relies on weaker computational assumptions. We focus on designated-verifier proofs and propose a protocol in which a proof consists of just 5 LWE encodings. We provide a concrete choice of parameters as well as extensive benchmarks on a C implementation, showing that our construction is practically instantiable.

Glaser, Alexander.  2018.  Hardware Security at the Limit: Nuclear Verification and Arms Control. Proceedings of the 2018 Workshop on Attacks and Solutions in Hardware Security. :40-40.

Nuclear weapons have re-emerged as one the main global security challenges of our time. Any further reductions in the nuclear arsenals will have to rely on robust verification mechanisms. This requires, in particular, trusted measurement systems to confirm the authenticity of nuclear warheads based on their radiation signatures. These signatures are considered extremely sensitive information, and inspection systems have to be designed to protect them. To accomplish this task, so-called information barriers" have been proposed. These devices process sensitive information acquired during an inspection, but only display results in a pass/fail manner. Traditional inspection systems rely on complex electronics both for data acquisition and processing. Several research efforts have produced prototype systems, but after almost thirty years of research and development, no viable and widely accepted system has emerged. This talk highlights recent efforts to overcome this impasse. A first approach is to avoid electronics in critical parts of the measurement process altogether and to rely instead on physical phenomena to detect radiation and to confirm a unique fingerprint of the inspected warhead using a zero-knowledge protocol. A second approach is based on a radiation detection system using vintage electronics built around a 6502 processor. Hardware designed in the distant past, at a time when its use for sensitive measurements was never envisioned, may drastically reduce concerns that another party implemented backdoors or hidden switches. Sensitive information is only stored on traditional punched cards. The talk concludes with a roadmap and highlights opportunities for researchers from the hardware security community to make critical contributions to nuclear arms control and global security in the years ahead.

Tao, Y., Lei, Z., Ruxiang, P..  2018.  Fine-Grained Big Data Security Method Based on Zero Trust Model. 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS). :1040-1045.

With the rapid development of big data technology, the requirement of data processing capacity and efficiency result in failure of a number of legacy security technologies, especially in the data security domain. Data security risks became extremely important for big data usage. We introduced a novel method to preform big data security control, which comprises three steps, namely, user context recognition based on zero trust, fine-grained data access authentication control, and data access audit based on full network traffic to recognize and intercept risky data access in big data environment. Experiments conducted on the fine-grained big data security method based on the zero trust model of drug-related information analysis system demonstrated that this method can identify the majority of data security risks.

Samaniego, M., Deters, R..  2018.  Zero-Trust Hierarchical Management in IoT. 2018 IEEE International Congress on Internet of Things (ICIOT). :88-95.

Internet of Things (IoT) is experiencing exponential scalability. This scalability introduces new challenges regarding management of IoT networks. The question that emerges is how we can trust the constrained infrastructure that shortly is expected to be formed by millions of 'things.' The answer is not to trust. This research introduces Amatista, a blockchain-based middleware for management in IoT. Amatista presents a novel zero-trust hierarchical mining process that allows validating the infrastructure and transactions at different levels of trust. This research evaluates Amatista on Edison Arduino Boards.

Vanickis, R., Jacob, P., Dehghanzadeh, S., Lee, B..  2018.  Access Control Policy Enforcement for Zero-Trust-Networking. 2018 29th Irish Signals and Systems Conference (ISSC). :1-6.

The evolution of the enterprise computing landscape towards emerging trends such as fog/edge computing and the Industrial Internet of Things (IIoT) are leading to a change of approach to securing computer networks to deal with challenges such as mobility, virtualized infrastructures, dynamic and heterogeneous user contexts and transaction-based interactions. The uncertainty introduced by such dynamicity introduces greater uncertainty into the access control process and motivates the need for risk-based access control decision making. Thus, the traditional perimeter-based security paradigm is increasingly being abandoned in favour of a so called "zero trust networking" (ZTN). In ZTN networks are partitioned into zones with different levels of trust required to access the zone resources depending on the assets protected by the zone. All accesses to sensitive information is subject to rigorous access control based on user and device profile and context. In this paper we outline a policy enforcement framework to address many of open challenges for risk-based access control for ZTN. We specify the design of required policy languages including a generic firewall policy language to express firewall rules. We design a mechanism to map these rules to specific firewall syntax and to install the rules on the firewall. We show the viability of our design with a small proof-of-concept.

Marchal, Xavier, Cholez, Thibault, Festor, Olivier.  2018.  $M$NDN: An Orchestrated Microservice Architecture for Named Data Networking. Proceedings of the 5th ACM Conference on Information-Centric Networking. :12-23.

As an extension of Network Function Virtualization, microservice architectures are a promising way to design future network services. At the same time, Information-Centric Networking architectures like NDN would benefit from this paradigm to offer more design choices for the network architect while facilitating the deployment and the operation of the network. We propose $μ$NDN, an orchestrated suite of microservices as an alternative way to implement NDN forwarding and support functions. We describe seven essential micro-services we developed, explain the design choices behind our solution and how it is orchestrated. We evaluate each service in isolation and the entire microservice architecture through two realistic scenarios to show its ability to react and mitigate some performance and security issues thanks to the orchestration. Our results show that $μ$NDN can replace a monolithic NDN forwarder while being more powerful and scalable.

Kaiafas, G., Varisteas, G., Lagraa, S., State, R., Nguyen, C. D., Ries, T., Ourdane, M..  2018.  Detecting Malicious Authentication Events Trustfully. NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium. :1-6.

Anomaly detection on security logs is receiving more and more attention. Authentication events are an important component of security logs, and being able to produce trustful and accurate predictions minimizes the effort of cyber-experts to stop false attacks. Observed events are classified into Normal, for legitimate user behavior, and Malicious, for malevolent actions. These classes are consistently excessively imbalanced which makes the classification problem harder; in the commonly used Los Alamos dataset, the malicious class comprises only 0.00033% of the total. This work proposes a novel method to extract advanced composite features, and a supervised learning technique for classifying authentication logs trustfully; the models are Random Forest, LogitBoost, Logistic Regression, and ultimately Majority Voting which leverages the predictions of the previous models and gives the final prediction for each authentication event. We measure the performance of our experiments by using the False Negative Rate and False Positive Rate. In overall we achieve 0 False Negative Rate (i.e. no attack was missed), and on average a False Positive Rate of 0.0019.

Sun, M., Li, M., Gerdes, R..  2018.  Truth-Aware Optimal Decision-Making Framework with Driver Preferences for V2V Communications. 2018 IEEE Conference on Communications and Network Security (CNS). :1-9.

In Vehicle-to-Vehicle (V2V) communications, malicious actors may spread false information to undermine the safety and efficiency of the vehicular traffic stream. Thus, vehicles must determine how to respond to the contents of messages which maybe false even though they are authenticated in the sense that receivers can verify contents were not tampered with and originated from a verifiable transmitter. Existing solutions to find appropriate actions are inadequate since they separately address trust and decision, require the honest majority (more honest ones than malicious), and do not incorporate driver preferences in the decision-making process. In this work, we propose a novel trust-aware decision-making framework without requiring an honest majority. It securely determines the likelihood of reported road events despite the presence of false data, and consequently provides the optimal decision for the vehicles. The basic idea of our framework is to leverage the implied effect of the road event to verify the consistency between each vehicle's reported data and actual behavior, and determine the data trustworthiness and event belief by integrating the Bayes' rule and Dempster Shafer Theory. The resulting belief serves as inputs to a utility maximization framework focusing on both safety and efficiency. This framework considers the two basic necessities of the Intelligent Transportation System and also incorporates drivers' preferences to decide the optimal action. Simulation results show the robustness of our framework under the multiple-vehicle attack, and different balances between safety and efficiency can be achieved via selecting appropriate human preference factors based on the driver's risk-taking willingness.

Severson, T., Rodriguez-Seda, E., Kiriakidis, K., Croteau, B., Krishnankutty, D., Robucci, R., Patel, C., Banerjee, N..  2018.  Trust-Based Framework for Resilience to Sensor-Targeted Attacks in Cyber-Physical Systems. 2018 Annual American Control Conference (ACC). :6499-6505.
Networked control systems improve the efficiency of cyber-physical plants both functionally, by the availability of data generated even in far-flung locations, and operationally, by the adoption of standard protocols. A side-effect, however, is that now the safety and stability of a local process and, in turn, of the entire plant are more vulnerable to malicious agents. Leveraging the communication infrastructure, the authors here present the design of networked control systems with built-in resilience. Specifically, the paper addresses attacks known as false data injections that originate within compromised sensors. In the proposed framework for closed-loop control, the feedback signal is constructed by weighted consensus of estimates of the process state gathered from other interconnected processes. Observers are introduced to generate the state estimates from the local data. Side-channel monitors are attached to each primary sensor in order to assess proper code execution. These monitors provide estimates of the trust assigned to each observer output and, more importantly, independent of it; these estimates serve as weights in the consensus algorithm. The authors tested the concept on a multi-sensor networked physical experiment with six primary sensors. The weighted consensus was demonstrated to yield a feedback signal within specified accuracy even if four of the six primary sensors were injecting false data.
Ma, S., Zeng, S., Guo, J..  2018.  Research on Trust Degree Model of Fault Alarms Based on Neural Network. 2018 12th International Conference on Reliability, Maintainability, and Safety (ICRMS). :73-77.

False alarm and miss are two general kinds of alarm errors and they can decrease operator's trust in the alarm system. Specifically, there are two different forms of trust in such systems, represented by two kinds of responses to alarms in this research. One is compliance and the other is reliance. Besides false alarm and miss, the two responses are differentially affected by properties of the alarm system, situational factors or operator factors. However, most of the existing studies have qualitatively analyzed the relationship between a single variable and the two responses. In this research, all available experimental studies are identified through database searches using keyword "compliance and reliance" without restriction on year of publication to December 2017. Six relevant studies and fifty-two sets of key data are obtained as the data base of this research. Furthermore, neural network is adopted as a tool to establish the quantitative relationship between multiple factors and the two forms of trust, respectively. The result will be of great significance to further study the influence of human decision making on the overall fault detection rate and the false alarm rate of the human machine system.

Gerard, B., Rebaï, S. B., Voos, H., Darouach, M..  2018.  Cyber Security and Vulnerability Analysis of Networked Control System Subject to False-Data Injection. 2018 Annual American Control Conference (ACC). :992-997.

In the present paper, the problem of networked control system (NCS) cyber security is considered. The geometric approach is used to evaluate the security and vulnerability level of the controlled system. The proposed results are about the so-called false data injection attacks and show how imperfectly known disturbances can be used to perform undetectable, or at least stealthy, attacks that can make the NCS vulnerable to attacks from malicious outsiders. A numerical example is given to illustrate the approach.

Ghugar, U., Pradhan, J..  2018.  NL-IDS: Trust Based Intrusion Detection System for Network Layer in Wireless Sensor Networks. 2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC). :512-516.

From the last few years, security in wireless sensor network (WSN) is essential because WSN application uses important information sharing between the nodes. There are large number of issues raised related to security due to open deployment of network. The attackers disturb the security system by attacking the different protocol layers in WSN. The standard AODV routing protocol faces security issues when the route discovery process takes place. The data should be transmitted in a secure path to the destination. Therefore, to support the process we have proposed a trust based intrusion detection system (NL-IDS) for network layer in WSN to detect the Black hole attackers in the network. The sensor node trust is calculated as per the deviation of key factor at the network layer based on the Black hole attack. We use the watchdog technique where a sensor node continuously monitors the neighbor node by calculating a periodic trust value. Finally, the overall trust value of the sensor node is evaluated by the gathered values of trust metrics of the network layer (past and previous trust values). This NL-IDS scheme is efficient to identify the malicious node with respect to Black hole attack at the network layer. To analyze the performance of NL-IDS, we have simulated the model in MATLAB R2015a, and the result shows that NL-IDS is better than Wang et al. [11] as compare of detection accuracy and false alarm rate.