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2021-05-20
Schaerer, Jakob, Zumbrunn, Severin, Braun, Torsten.  2020.  Veritaa - The Graph of Trust. 2020 2nd Conference on Blockchain Research Applications for Innovative Networks and Services (BRAINS). :168—175.

Today the integrity of digital documents and the authenticity of their origin is often hard to verify. Existing Public Key Infrastructures (PKIs) are capable of certifying digital identities but do not provide solutions to immutably store signatures, and the process of certification is often not transparent. In this work we propose Veritaa, a Distributed Public Key Infrastructure and Signature Store (DPKISS). The major innovation of Veritaa is the Graph of Trust, a directed graph that uses relations between identity claims to certify the identities and stores signed relations to digital document identifiers. The distributed architecture of Veritaa and the Graph of Trust enables a transparent certification process. To ensure non-repudiation and immutability of all actions that have been signed on the Graph of Trust, an application specific Distributed Ledger Technology (DLT) is used as secure storage. In this work a reference implementation of the proposed architecture was designed and implemented. Furthermore, a testbed was created and used for the evaluation of Veritaa. The evaluation of Veritaa shows the benefits and the high performance of the proposed architecture.

2021-05-18
Ogawa, Yuji, Kimura, Tomotaka, Cheng, Jun.  2020.  Vulnerability Assessment for Machine Learning Based Network Anomaly Detection System. 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan). :1–2.
In this paper, we assess the vulnerability of network anomaly detection systems that use machine learning methods. Although the performance of these network anomaly detection systems is high in comparison to that of existing methods without machine learning methods, the use of machine learning methods for detecting vulnerabilities is a growing concern among researchers of image processing. If the vulnerabilities of machine learning used in the network anomaly detection method are exploited by attackers, large security threats are likely to emerge in the near future. Therefore, in this paper we clarify how vulnerability detection of machine learning network anomaly detection methods affects their performance.
2021-05-03
Sharma, Mohit, Strathman, Hunter J., Walker, Ross M..  2020.  Verification of a Rapidly Multiplexed Circuit for Scalable Action Potential Recording. 2020 IEEE International Symposium on Circuits and Systems (ISCAS). :1–1.
This report presents characterizations of in vivo neural recordings performed with a CMOS multichannel chip that uses rapid multiplexing directly at the electrodes, without any pre-amplification or buffering. Neural recordings were taken from a 16-channel microwire array implanted in rodent cortex, with comparison to a gold-standard commercial bench-top recording system. We were able to record well-isolated threshold crossings from 10 multiplexed electrodes and typical local field potential waveforms from 16, with strong agreement with the standard system (average SNR = 2.59 and 3.07 respectively). For 10 electrodes, the circuit achieves an effective area per channel of 0.0077 mm2, which is \textbackslashtextgreater5× smaller than typical multichannel chips. Extensive characterizations of noise and signal quality are presented and compared to fundamental theory, as well as results from in vivo and in vitro experiments. By demonstrating the validation of rapid multiplexing directly at the electrodes, this report confirms it as a promising approach for reducing circuit area in massively-multichannel neural recording systems, which is crucial for scaling recording site density and achieving large-scale sensing of brain activity with high spatiotemporal resolution.
2021-04-27
Tolsdorf, J., Iacono, L. Lo.  2020.  Vision: Shred If Insecure – Persuasive Message Design as a Lesson and Alternative to Previous Approaches to Usable Secure Email Interfaces. 2020 IEEE European Symposium on Security and Privacy Workshops (EuroS PW). :172–177.
Despite the advances in research on usable secure email, the majority of mail user agents found in practice still violates best practices in UI design and uses ineffective and inhomogeneous design strategies to communicate and let users control the security status of an email message.We propose a novel interaction and design concept that we refer to as persuasive message design. Our approach is derived from heuristics and a systematic meta-study of existing HCI literature on email management, usable secure email and phishing research. Concluding on this body of knowledge we propose the design of interfaces that suppress weak cues and instead manipulate the display of emails according to their technical security level. Persuasive message design addresses several shortcomings of current secure email user interfaces and provides a consistent user experience that can be deployed even by email providers.
2021-03-18
Vaibhavi Deshmukh, Swarnima Deshmukh, Shivani Deosatwar, Reva Sarda, Lalit Kulkarni.  2020.  Versatile CAPTCHA Generation Using Machine Learning and Image Processing.

Due to the significant increase in the size of the internet and the number of users on this platform there has been a tremendous increase in load on various websites and web-based applications. This load is from the user end which causes unforeseen conditions which leads to unacceptable consequences such as crash or a data loss scenario at the webserver end. Therefore, there is a need to reduce the load on the server as well as the chances of network attacks that increase with the increased user base. The undue consequences such as data loss and server crash are caused due to two main reasons: the first one being an overload of users and the second due to an increased number of automatic programs or robots. A technique can be utilized to overcome this scenario by introducing a delay in the operation speed on the user end through the use of a CAPTCHA mechanism. Most of the classical approaches use a single method for the generation of the CAPTCHA, to overcome this proposed model uses the versatile image CAPTCHA generation mechanism. We have introduced a system that utilizes manualbased, face detection-based, colour based and random object insertion technique to generate 4 different random types of CAPTCHA. The proposed methodology implements a region of interest and convolutional neural networks to achieve the generation of the CAPTCHA effectively.

2021-02-22
Suwannasa, A., Broadbent, M., Mauthe, A..  2020.  Vicinity-based Replica Finding in Named Data Networking. 2020 International Conference on Information Networking (ICOIN). :146–151.
In Named Data Networking (NDN) architectures, a content object is located according to the content's identifier and can be retrieved from all nodes that hold a replica of the content. The default forwarding strategy of NDN is to forward an Interest packet along the default path from the requester to the server to find a content object according to its name prefix. However, the best path may not be the default path, since content might also be located nearby. Hence, the default strategy could result in a sub-optimal delivery efficiency. To address this issue we introduce a vicinity-based replica finding scheme. This is based on the observation that content objects might be requested several times. Therefore, replicas can be often cached within a particular neighbourhood and thus it might be efficient to specifically look for them in order to improve the content delivery performance. Within this paper, we evaluate the optimal size of the vicinity within which content should be located (i.e. the distance between the requester and its neighbours that are considered within the content search). We also compare the proposed scheme with the default NDN forwarding strategy with respect to replica finding efficiency and network overhead. Using the proposed scheme, we demonstrate that the replica finding mechanism reduces the delivery time effectively with acceptable overhead costs.
2021-02-15
Rout, S., Mohapatra, R. K..  2020.  Video Steganography using Curvelet Transform and Elliptic Curve Cryptography. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1–7.
Video steganography mainly deals with secret data transmission in a carrier video file without being visually noticeable by intruders. Video steganography is preferred over image steganography because a video carries more space in comparison to an image. The main concept of information hiding consists of a cover media, which is a greyscale or a color video, a secret data, which is an image or text, and a stego key. Here a secure video steganography method has been proposed which uses Curvelet Transform for secret data embedding, Elliptic Curve Cryptography for stego key encryption and a threshold algorithm for the determination of the amount of secret data to be encoded per frame. A video is a collection of various frames. The frames are selected randomly from the cover video and the frame number of the respective frames has been indexed in the stego key to find the secret data embedding location. Here, the selection of frames in a sequential manner has been avoided to improve security. For enhanced security, the stego key is also encrypted using Elliptic Curve Integrated Encryption Scheme (ECIES). Fast Discrete Curvelet Transform (FDCT) has been applied to the frames of the cover video and the curvelet coefficients have been modified to obscure the secret data to produce the stego video.
2021-02-08
Geetha, C. R., Basavaraju, S., Puttamadappa, C..  2013.  Variable load image steganography using multiple edge detection and minimum error replacement method. 2013 IEEE Conference on Information Communication Technologies. :53—58.

This paper proposes a steganography method using the digital images. Here, we are embedding the data which is to be secured into the digital image. Human Visual System proved that the changes in the image edges are insensitive to human eyes. Therefore we are using edge detection method in steganography to increase data hiding capacity by embedding more data in these edge pixels. So, if we can increase number of edge pixels, we can increase the amount of data that can be hidden in the image. To increase the number of edge pixels, multiple edge detection is employed. Edge detection is carried out using more sophisticated operator like canny operator. To compensate for the resulting decrease in the PSNR because of increase in the amount of data hidden, Minimum Error Replacement [MER] method is used. Therefore, the main goal of image steganography i.e. security with highest embedding capacity and good visual qualities are achieved. To extract the data we need the original image and the embedding ratio. Extraction is done by taking multiple edges detecting the original image and the data is extracted corresponding to the embedding ratio.

2021-02-03
Ani, U. D., He, H., Tiwari, A..  2020.  Vulnerability-Based Impact Criticality Estimation for Industrial Control Systems. 2020 International Conference on Cyber Security and Protection of Digital Services (Cyber Security). :1—8.

Cyber threats directly affect the critical reliability and availability of modern Industry Control Systems (ICS) in respects of operations and processes. Where there are a variety of vulnerabilities and cyber threats, it is necessary to effectively evaluate cyber security risks, and control uncertainties of cyber environments, and quantitative evaluation can be helpful. To effectively and timely control the spread and impact produced by attacks on ICS networks, a probabilistic Multi-Attribute Vulnerability Criticality Analysis (MAVCA) model for impact estimation and prioritised remediation is presented. This offer a new approach for combining three major attributes: vulnerability severities influenced by environmental factors, the attack probabilities relative to the vulnerabilities, and functional dependencies attributed to vulnerability host components. A miniature ICS testbed evaluation illustrates the usability of the model for determining the weakest link and setting security priority in the ICS. This work can help create speedy and proactive security response. The metrics derived in this work can serve as sub-metrics inputs to a larger quantitative security metrics taxonomy; and can be integrated into the security risk assessment scheme of a larger distributed system.

2021-01-15
Bose, A. J., Aarabi, P..  2019.  Virtual Fakes: DeepFakes for Virtual Reality. 2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP). :1—1.
The proliferation of data and computational resources has led into many advancements in computer vision for facial data including easily replacing a face in one video with another one, the so called DeepFake. In this paper, we apply techniques to generate DeepFakes for virtual reality applications. We empirically validate our method by generating, for the first time, Deep Fake videos in virtual reality.
Korshunov, P., Marcel, S..  2019.  Vulnerability assessment and detection of Deepfake videos. 2019 International Conference on Biometrics (ICB). :1—6.
It is becoming increasingly easy to automatically replace a face of one person in a video with the face of another person by using a pre-trained generative adversarial network (GAN). Recent public scandals, e.g., the faces of celebrities being swapped onto pornographic videos, call for automated ways to detect these Deepfake videos. To help developing such methods, in this paper, we present the first publicly available set of Deepfake videos generated from videos of VidTIMIT database. We used open source software based on GANs to create the Deepfakes, and we emphasize that training and blending parameters can significantly impact the quality of the resulted videos. To demonstrate this impact, we generated videos with low and high visual quality (320 videos each) using differently tuned parameter sets. We showed that the state of the art face recognition systems based on VGG and Facenet neural networks are vulnerable to Deepfake videos, with 85.62% and 95.00% false acceptance rates (on high quality versions) respectively, which means methods for detecting Deepfake videos are necessary. By considering several baseline approaches, we found the best performing method based on visual quality metrics, which is often used in presentation attack detection domain, to lead to 8.97% equal error rate on high quality Deep-fakes. Our experiments demonstrate that GAN-generated Deepfake videos are challenging for both face recognition systems and existing detection methods, and the further development of face swapping technology will make it even more so.
2021-01-11
Wu, N., Farokhi, F., Smith, D., Kaafar, M. A..  2020.  The Value of Collaboration in Convex Machine Learning with Differential Privacy. 2020 IEEE Symposium on Security and Privacy (SP). :304–317.
In this paper, we apply machine learning to distributed private data owned by multiple data owners, entities with access to non-overlapping training datasets. We use noisy, differentially-private gradients to minimize the fitness cost of the machine learning model using stochastic gradient descent. We quantify the quality of the trained model, using the fitness cost, as a function of privacy budget and size of the distributed datasets to capture the trade-off between privacy and utility in machine learning. This way, we can predict the outcome of collaboration among privacy-aware data owners prior to executing potentially computationally-expensive machine learning algorithms. Particularly, we show that the difference between the fitness of the trained machine learning model using differentially-private gradient queries and the fitness of the trained machine model in the absence of any privacy concerns is inversely proportional to the size of the training datasets squared and the privacy budget squared. We successfully validate the performance prediction with the actual performance of the proposed privacy-aware learning algorithms, applied to: financial datasets for determining interest rates of loans using regression; and detecting credit card frauds using support vector machines.
2020-12-21
Guo, W., Atthanayake, I., Thomas, P..  2020.  Vertical Underwater Molecular Communications via Buoyancy: Gaussian Velocity Distribution of Signal. ICC 2020 - 2020 IEEE International Conference on Communications (ICC). :1–6.
Underwater communication is vital for a variety of defence and scientific purposes. Current optical and sonar based carriers can deliver high capacity data rates, but their range and reliability is hampered by heavy propagation loss. A vertical Molecular Communication via Buoyancy (MCvB) channel is experimentally investigated here, where the dominant propagation force is buoyancy. Sequential puffs representing modulated symbols are injected and after the initial loss of momentum, the signal is driven by buoyancy forces which apply to both upwards and downwards channels. Coupled with the complex interaction of turbulent and viscous diffusion, we experimentally demonstrate that sequential symbols exhibit a Gaussian velocity spatial distribution. Our experimental results use Particle Image Velocimetry (PIV) to trace molecular clusters and infer statistical characteristics of their velocity profile. We believe our experimental paper's results can be the basis for long range underwater vertical communication between a deep sea vehicle and a surface buoy, establishing a covert and reliable delay-tolerant data link. The statistical distribution found in this paper is akin to the antenna pattern and the knowledge can be used to improve physical security.
2020-11-09
Bouzar-Benlabiod, L., Méziani, L., Rubin, S. H., Belaidi, K., Haddar, N. E..  2019.  Variational Encoder-Decoder Recurrent Neural Network (VED-RNN) for Anomaly Prediction in a Host Environment. 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI). :75–82.
Intrusion detection systems (IDS) are important security tools. NIDS monitors network's traffic and HIDS filters local one. HIDS are often based on anomaly detection. Several studies deal with anomaly detection using system-call traces. In this paper, we propose an anomaly detection and prediction approach. System-call traces, invoked by the running programs, are analyzed in real time. For prediction, we use a Sequence to sequence model based on variational encoder-decoder (VED) and variants of Recurrent Neural Networks (RNN), these architectures showed their performance on natural language processing. To make the analogy, we exploit the semantics behind the invoking order of system-calls that are then seen as sentences. A preprocessing phase is added to optimize the prediction model input data representation. A one-class classification is done to categorize the sequences into normal or abnormal. Tests are achieved on the ADFA-LD dataset and showed the advantage of the prediction for the intrusion detection/prediction task.
2020-11-04
Liu, D. Y. W., Leung, A. C. Y., Au, M. H., Luo, X., Chiu, P. H. P., Im, S. W. T., Lam, W. W. M..  2019.  Virtual Laboratory: Facilitating Teaching and Learning in Cybersecurity for Students with Diverse Disciplines. 2019 IEEE International Conference on Engineering, Technology and Education (TALE). :1—6.

Cybersecurity education is a pressing need, when computer systems and mobile devices are ubiquitous and so are the associated threats. However, in the teaching and learning process of cybersecurity, it is challenging when the students are from diverse disciplines with various academic backgrounds. In this project, a number of virtual laboratories are developed to facilitate the teaching and learning process in a cybersecurity course. The aim of the laboratories is to strengthen students’ understanding of cybersecurity topics, and to provide students hands-on experience of encountering various security threats. The results of this project indicate that virtual laboratories do facilitate the teaching and learning process in cybersecurity for diverse discipline students. Also, we observed that there is an underestimation of the difficulty of studying cybersecurity by the students due to the general image of cybersecurity in public, which had a negative impact on the student’s interest in studying cybersecurity.

2020-10-26
Astaburuaga, Ignacio, Lombardi, Amee, La Torre, Brian, Hughes, Carolyn, Sengupta, Shamik.  2019.  Vulnerability Analysis of AR.Drone 2.0, an Embedded Linux System. 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC). :0666–0672.
The goal of this work was to identify and try to solve some of the vulnerabilities present in the AR Drone 2.0 by Parrot. The approach was to identify how the system worked, find and analyze vulnerabilities and flaws in the system as a whole and in the software, and find solutions to those problems. Analyzing the results of some tests showed that the system has an open WiFi network and the communication between the controller and the drone are unencrypted. Analyzing the Linux operating system that the drone uses, we see that "Pairing Mode" is the only way the system protects itself from unauthorized control. This is a feature that can be easily bypassed. Port scans reveal that the system has all the ports for its services open and exposed. This makes it susceptible to attacks like DoS and takeover. This research also focuses on some of the software vulnerabilities, such as Busybox that the drone runs. Lastly, this paper discuses some of the possible methods that can be used to secure the drone. These methods include securing the messages via SSH Tunnel, closing unused ports, and re-implementing the software used by the drone and the controller.
2020-10-06
Ur-Rehman, Attiq, Gondal, Iqbal, Kamruzzuman, Joarder, Jolfaei, Alireza.  2019.  Vulnerability Modelling for Hybrid IT Systems. 2019 IEEE International Conference on Industrial Technology (ICIT). :1186—1191.

Common vulnerability scoring system (CVSS) is an industry standard that can assess the vulnerability of nodes in traditional computer systems. The metrics computed by CVSS would determine critical nodes and attack paths. However, traditional IT security models would not fit IoT embedded networks due to distinct nature and unique characteristics of IoT systems. This paper analyses the application of CVSS for IoT embedded systems and proposes an improved vulnerability scoring system based on CVSS v3 framework. The proposed framework, named CVSSIoT, is applied to a realistic IT supply chain system and the results are compared with the actual vulnerabilities from the national vulnerability database. The comparison result validates the proposed model. CVSSIoT is not only effective, simple and capable of vulnerability evaluation for traditional IT system, but also exploits unique characteristics of IoT devices.

2020-09-28
Liu, Kai, Zhou, Yun, Wang, Qingyong, Zhu, Xianqiang.  2019.  Vulnerability Severity Prediction With Deep Neural Network. 2019 5th International Conference on Big Data and Information Analytics (BigDIA). :114–119.
High frequency of network security incidents has also brought a lot of negative effects and even huge economic losses to countries, enterprises and individuals in recent years. Therefore, more and more attention has been paid to the problem of network security. In order to evaluate the newly included vulnerability text information accurately, and to reduce the workload of experts and the false negative rate of the traditional method. Multiple deep learning methods for vulnerability text classification evaluation are proposed in this paper. The standard Cross Site Scripting (XSS) vulnerability text data is processed first, and then classified using three kinds of deep neural networks (CNN, LSTM, TextRCNN) and one kind of traditional machine learning method (XGBoost). The dropout ratio of the optimal CNN network, the epoch of all deep neural networks and training set data were tuned via experiments to improve the fit on our target task. The results show that the deep learning methods evaluate vulnerability risk levels better, compared with traditional machine learning methods, but cost more time. We train our models in various training sets and test with the same testing set. The performance and utility of recurrent convolutional neural networks (TextRCNN) is highest in comparison to all other methods, which classification accuracy rate is 93.95%.
2020-09-11
Shukla, Ankur, Katt, Basel, Nweke, Livinus Obiora.  2019.  Vulnerability Discovery Modelling With Vulnerability Severity. 2019 IEEE Conference on Information and Communication Technology. :1—6.
Web browsers are primary targets of attacks because of their extensive uses and the fact that they interact with sensitive data. Vulnerabilities present in a web browser can pose serious risk to millions of users. Thus, it is pertinent to address these vulnerabilities to provide adequate protection for personally identifiable information. Research done in the past has showed that few vulnerability discovery models (VDMs) highlight the characterization of vulnerability discovery process. In these models, severity which is one of the most crucial properties has not been considered. Vulnerabilities can be categorized into different levels based on their severity. The discovery process of each kind of vulnerabilities is different from the other. Hence, it is essential to incorporate the severity of the vulnerabilities during the modelling of the vulnerability discovery process. This paper proposes a model to assess the vulnerabilities present in the software quantitatively with consideration for the severity of the vulnerabilities. It is possible to apply the proposed model to approximate the number of vulnerabilities along with vulnerability discovery rate, future occurrence of vulnerabilities, risk analysis, etc. Vulnerability data obtained from one of the major web browsers (Google Chrome) is deployed to examine goodness-of-fit and predictive capability of the proposed model. Experimental results justify the fact that the model proposed herein can estimate the required information better than the existing VDMs.
2020-08-03
Xiong, Chen, Chen, Hua, Cai, Ming, Gao, Jing.  2019.  A Vehicle Trajectory Adversary Model Based on VLPR Data. 2019 5th International Conference on Transportation Information and Safety (ICTIS). :903–912.
Although transport agency has employed desensitization techniques to deal with the privacy information when publicizing vehicle license plate recognition (VLPR) data, the adversaries can still eavesdrop on vehicle trajectories by certain means and further acquire the associated person and vehicle information through background knowledge. In this work, a privacy attacking method by using the desensitized VLPR data is proposed to link the vehicle trajectory. First the road average speed is evaluated by analyzing the changes of traffic flow, which is used to estimate the vehicle's travel time to the next VLPR system. Then the vehicle suspicion list is constructed through the time relevance of neighboring VLPR systems. Finally, since vehicles may have the same features like color, type, etc, the target trajectory will be located by filtering the suspected list by the rule of qualified identifier (QI) attributes and closest time method. Based on the Foshan City's VLPR data, the method is tested and results show that correct vehicle trajectory can be linked, which proves that the current VLPR data publication way has the risk of privacy disclosure. At last, the effects of related parameters on the proposed method are discussed and effective suggestions are made for publicizing VLPR date in the future.
2020-07-20
Xu, Tangwei, Lu, Xiaozhen, Xiao, Liang, Tang, Yuliang, Dai, Huaiyu.  2019.  Voltage Based Authentication for Controller Area Networks with Reinforcement Learning. ICC 2019 - 2019 IEEE International Conference on Communications (ICC). :1–5.
Controller area networks (CANs) are vulnerable to spoofing attacks such as frame falsifying attacks, as electronic control units (ECUs) send and receive messages without any authentication and encryption. In this paper, we propose a physical authentication scheme that exploits the voltage features of the ECU signals on the CAN bus and applies reinforcement learning to choose the authentication mode such as the protection level and test threshold. This scheme enables a monitor node to optimize the authentication mode via trial-and-error without knowing the CAN bus signal model and spoofing model. Experimental results show that the proposed authentication scheme can significantly improve the authentication accuracy and response compared with a benchmark scheme.
2020-07-13
Almohanna, S., Alogayyel, M. S., Ajaji, A. A., Alkhdrawi, H. A., Alleli, M. A., Tareq, Q., Mukhtar, Sani, Mohammed Khan, Z. M..  2019.  Visible-NIR Laser Based Bi-directional Indoor Optical Wireless Communication. 2019 IEEE 10th GCC Conference Exhibition (GCC). :1–4.
We propose and demonstrate an indoor optical bi-directional communication system employing near-infrared (NIR) and visible light as carriers. Such a communication technology is attractive wherein red color could be deployed for down streaming purpose via, for instance, LiFi (light fidelity) system, and NIR color for up streaming purpose. This system concept is implemented over a simultaneous bidirectional audio signal transmission and reception over 0.6m indoor wireless channel. Besides, designing the transceiver circuits from off the shelf components, frequency scrambling encryption and decryption technique is also integrated in the system for security purpose. The communication system is optically characterized in terms of line-of-sight laser misalignment and communication distance.
2020-07-03
Suo, Yucong, Zhang, Chen, Xi, Xiaoyun, Wang, Xinyi, Zou, Zhiqiang.  2019.  Video Data Hierarchical Retrieval via Deep Hash Method. 2019 IEEE 11th International Conference on Communication Software and Networks (ICCSN). :709—714.

Video retrieval technology faces a series of challenges with the tremendous growth in the number of videos. In order to improve the retrieval performance in efficiency and accuracy, a novel deep hash method for video data hierarchical retrieval is proposed in this paper. The approach first uses cluster-based method to extract key frames, which reduces the workload of subsequent work. On the basis of this, high-level semantical features are extracted from VGG16, a widely used deep convolutional neural network (deep CNN) model. Then we utilize a hierarchical retrieval strategy to improve the retrieval performance, roughly can be categorized as coarse search and fine search. In coarse search, we modify simHash to learn hash codes for faster speed, and in fine search, we use the Euclidean distance to achieve higher accuracy. Finally, we compare our approach with other two methods through practical experiments on two videos, and the results demonstrate that our approach has better retrieval effect.

2020-06-12
Ay, Betül, Aydın, Galip, Koyun, Zeynep, Demir, Mehmet.  2019.  A Visual Similarity Recommendation System using Generative Adversarial Networks. 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). :44—48.

The goal of content-based recommendation system is to retrieve and rank the list of items that are closest to the query item. Today, almost every e-commerce platform has a recommendation system strategy for products that customers can decide to buy. In this paper we describe our work on creating a Generative Adversarial Network based image retrieval system for e-commerce platforms to retrieve best similar images for a given product image specifically for shoes. We compare state-of-the-art solutions and provide results for the proposed deep learning network on a standard data set.

2020-04-24
Pan, Huan, Lian, Honghui, Na, Chunning.  2019.  Vulnerability Analysis of Smart Grid under Community Attack Style. IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society. 1:5971—5976.
The smart grid consists of two parts, one is the physical power grid, the other is the information network. In order to study the cascading failure, the vulnerability analysis of the smart grid is done under a kind of community attack style in this paper. Two types of information networks are considered, i.e. topology consistency and scale-free cyber networks, respectively. The concept of control center is presented and the controllable power nodes and observable power lines are defined. Minimum load reduction model(MLRM) is given and described as a linear programming problem. A index is introduced to assess the vulnerability. New England 39 nodes system is applied to simulate the cascading failure process to demonstrate the effectiveness of the proposed MLRM where community the attack methods include attack the power lines among and in power communities.