Biblio

In order to improve the accuracy and efficiency of transmission line safety early warning, a transmission line safety early warning system based on big data variable analysis is proposed. Firstly, the overall architecture of the system is designed under the B / S architecture. Secondly, in the hardware part of the system, the security data real-time monitoring module, data transmission module and security warning module are designed to meet the functional requirements of the system. Finally, in the system software design part, the big data variable analysis method is used to calculate the hidden danger of transmission line safety, so as to improve the effectiveness of transmission safety early warning. The experimental results show that, compared with the traditional security early warning system, the early warning accuracy and efficiency of the designed system are significantly improved, which can ensure the safe operation of the transmission line.

Deep Neural Networks (DNN) has gained great success in solving several challenging problems in recent years. It is well known that training a DNN model from scratch requires a lot of data and computational resources. However, using a pre-trained model directly or using it to initialize weights cost less time and often gets better results. Therefore, well pre-trained DNN models are valuable intellectual property that we should protect. In this work, we propose DeepTrace, a framework for model owners to secretly fingerprinting the target DNN model using a special trigger set and verifying from outputs. An embedded fingerprint can be extracted to uniquely identify the information of model owner and authorized users. Our framework benefits from both white-box and black-box verification, which makes it useful whether we know the model details or not. We evaluate the performance of DeepTrace on two different datasets, with different DNN architectures. Our experiment shows that, with the advantages of combining white-box and black-box verification, our framework has very little effect on model accuracy, and is robust against different model modifications. It also consumes very little computing resources when extracting fingerprint.

This paper describes the process of developing data visualisations to enhance a commercial software platform for combating insider threat, whose existing UI, while perfectly functional, was limited in its ability to allow analysts to easily spot the patterns and outliers that visualisation naturally reveals. We describe the design and development process, proceeding from initial tasks/requirements gathering, understanding the platform’s data formats, the rationale behind the visualisations’ design, and then refining the prototype through gathering feedback from representative domain experts who are also current users of the software. Through a number of example scenarios, we show that the visualisation can support the identified tasks and aid analysts in discovering and understanding potentially risky insider activity within a large user base.

Currently, more and more malicious insiders are making threats, and the detection of insider threats is becoming more challenging. The malicious insider often uses legitimate access privileges and mimic normal behaviors to evade detection, which is difficult to be detected via using traditional defensive solutions. In this paper, we propose DeepMIT, a malicious insider threat detection framework, which utilizes Recurrent Neural Network (RNN) to model user behaviors as time sequences and predict the probabilities of anomalies. This framework allows DeepMIT to continue learning, and the detections are made in real time, that is, the anomaly alerts are output as rapidly as data input. Also, our framework conducts further insight of the anomaly scores and provides the contributions to the scores and, thus, significantly helps the operators to understand anomaly scores and take further steps quickly(e.g. Block insider's activity). In addition, DeepMIT utilizes user-attributes (e.g. the personality of the user, the role of the user) as categorical features to identify the user's truly typical behavior, which help detect malicious insiders who mimic normal behaviors. Extensive experimental evaluations over a public insider threat dataset CERT (version 6.2) have demonstrated that DeepMIT has outperformed other existing malicious insider threat solutions.

The rapid progress experienced in the Internet of Things (IoT) space is one that has introduced new and unique challenges for cybersecurity and IoT-Forensics. One of these problems is how digital forensics and incident response (DFIR) are handled in IoT. Since enormous users use IoT platforms to accomplish their day to day task, massive amounts of data streams are transferred with limited hardware resources; conducting DFIR needs a new approach to mitigate digital evidence and incident response challenges owing to the facts that there are no unified standard or classified principles for IoT forensics. Today's IoT DFIR relies on self-defined best practices and experiences. Given these challenges, IoT-related incidents need a more structured approach in identifying problems of DFIR. In this paper, we examined the major DFIR challenges in IoT by exploring the different phases involved in a DFIR when responding to IoT-related incidents. This study aims to provide researchers and practitioners a road-map that will help improve the standards of IoT security and DFIR.

Digital forensics is concerned with identifying, reporting and responding to security breaches. It is about how to acquire, analyze and report digital evidence and using the technical skills, discovering the traces of Cyber Crime. The field of digital forensics is in high demand due to the constant threats of data breaches and information hacks. Digital Forensics is utilized in the identification and elimination of crimes in any controversy where evidence is preserved in online space. This is the use of specialized techniques for retrieval, authentication and electronic data analysis. Computer forensics deals with the identification, preservation, analysis, documentation and presentation of digital evidence. The paper has analyzed the present-day trends that includes IoT forensics, cloud forensics, network forensics and social media forensics. Recent researches have shown a wide range of threats and cyber-attacks, which requires forensic investigators and forensics scientists to simplify the digital world. Hence, all our research gives a clear view of digital forensics which could be of a great help in forensic investigation. In this research paper we have discussed about the need and way to preserve the digital evidence, so that it is not compromised at any point in time and an unalter evidence can be presented before the court of law.

Digital forensics investigation process begins with the acquisition, investigation until the presentation of investigation findings. Investigators are required to manage bits and pieces of digital evidence in the cloud and to correlate with evidence found in physical machines and network. The process could be made easy with a proper case management tool that is hosted in the web. The challenge of maintaining chain of custody, determining access to evidence, assignment of forensics investigator could be overcome when digital evidence is fully integrated in a single platform. Our proposed case management tool streamlines information gathering and integrates information on different platforms, shares information, tracks cases, and uploads data directly into a database. In addition, the case management tool facilitates the collaboration of investigators through sharing of forensics findings. These features allow case owner or administrator to track and monitor investigation progress in a forensically sound manner.

In the computer era, various digital devices are used along with networking technology for data communication in secured manner. But sometimes these systems are misused by the attackers. Information security with the high efficiency devices, tools are utilized for protecting the communication media and valuable data. In case of any unwanted incidents and security breaches, digital forensics methods and measures are well utilized for detecting the type of attacks, sources of attacks, their purposes. By utilizing information related to security measures, digital forensics evidences with suitable methodologies, digital forensics investigators detect the cyber-crimes. It is also necessary to prove the cyber-crimes before the law enforcement department. During this process investigators type to collect different types of information from the digital devices concerned to the cyber-attack. One of the major tasks of the digital investigator is collecting and managing the seizure records from the crime-scene. The present paper discusses the seizure record framework for digital forensics investigations.

Both the Internet of Things (IoT) and Information Centric Networking (ICN) have gathered a lot of attention from both research and industry in recent years. While ICN has proved to be beneficial in many situations, it is not widely deployed outside research projects, also not addressing needs of IoT application programming interfaces (APIs). On the other hand, today's IoT solutions are built on top of the host-centric communication model associated with the usage of the Internet Protocol (IP). This paper contributes a discussion on the need of an integration of a specific form of IoT APIs, namely WebSocket based streaming APIs, into an ICN. Furthermore, different access models are discussed and requirements are derived from real world APIs. Finally, the design of an ICN-style extension is presented using one of the examined APIs.

Autonomous vehicles (AVs) offer a wide range of promising benefits by reducing traffic accidents, environmental pollution, traffic congestion and land usage etc. However, to reap the intended benefits of AVs, it is inevitable that this technology should be trusted and accepted by the public. The consumer's substantial trust upon AVs will lead to its widespread adoption in the real-life. It is well understood that the preservation of strong security and privacy features influence a consumer's trust on a product in a positive manner. In this paper, we introduce a novel concept of digital labels for AVs to increase consumers awareness and trust regarding the security level of their vehicle. We present an architecture called Cybersecurity Box (CSBox) that leverages digital labels to display and inform consumers and passengers about cybersecurity status of the AV in use. The introduction of cybersecurity digital labels on the dashboard of AVs would attempt to increase the trust level of consumers and passengers on this promising technology.

The project uses 3D immersive technology to innovatively apply virtual reality technology to the monitoring field, and proposes the concept and technical route of remote 3D immersive intelligent control. A design scheme of a three-dimensional immersive remote somatosensory intelligent controller is proposed, which is applied to the remote three-dimensional immersive control of a crawler mobile robot, and the test and analysis of the principle prototype are completed.

At present, the limitation of intangible cultural experience lies in the lack of long-term immersive cultural experience for users. In order to solve this problem, this study divides the process from the perspective of Freudian psychology and combines the theoretical research on intangible cultural heritage and flow experience to get the preliminary research direction. Then, based on the existing interactive experience cases of intangible cultural heritage, a set of method model of immersive interactive experience of intangible cultural heritage based on flow theory is summarized through user interviews in this research. Finally, through data verification, the model is proved to be correct. In addition, this study offers some important insights into differences between primary users and experienced users, and proposed specific guiding suggestions for immersive interactive experience design of intangible cultural heritage based on flow theory in the future.

The transmission and storage process for the power data in an intelligent grid has problems such as a single point of failure in the central node, low data credibility, and malicious manipulation or data theft. The characteristics of decentralization and tamper-proofing of blockchain and its distributed storage architecture can effectively solve malicious manipulation and the single point of failure. However, there are few safe and reliable data transmission methods for the significant number and various identities of users and the complex node types in the power blockchain. Thus, this paper proposes a collaborative control scheme between on-chain and off-chain power data based on the distributed oracle technology. By building a trusted on-chain transmission mechanism based on distributed oracles, the scheme solves the credibility problem of massive data transmission and interactive power data between smart contracts and off-chain physical devices safely and effectively. Analysis and discussion show that the proposed scheme can realize the collaborative control between on-chain and off-chain data efficiently, safely, and reliably.

Attack surface detection for the complex software is needed to find targets for the fuzzing, because testing the whole system with many inputs is not realistic. Researchers that previously applied taint analysis for dealing with different security tasks in the virtual machines did not examined how to apply it for attack surface detection. I.e., getting the program modules and functions, that may be affected by input data. We propose using taint tracking within a virtual machine and virtual machine introspection to create a new approach that can detect the internal module interfaces that can be fuzz tested to assure that software is safe or find the vulnerabilities.

Scene text detection methods have developed greatly in the past few years. However, due to the limitation of the diversity of the text background of natural scene, the previous methods often failed when detecting more complicated text instances (e.g., super-long text and arbitrarily shaped text). In this paper, a text detection method based on multi -channel bounding box fusion is designed to address the problem. Firstly, the convolutional neural network is used as the basic network for feature extraction, including shallow text feature map and deep semantic text feature map. Secondly, the whole convolutional network is used for upsampling of feature map and fusion of feature map at each layer, so as to obtain pixel-level text and non-text classification results. Then, two independent text detection boxes channels are designed: the boundary box regression channel and get the bounding box directly on the score map channel. Finally, the result is obtained by combining multi-channel boundary box fusion mechanism with the detection box of the two channels. Experiments on ICDAR2013 and ICDAR2015 demonstrate that the proposed method achieves competitive results in scene text detection.

Hardware Trojans are modifications made by malicious insiders or third party providers during the design or fabrication phase of the IC (Integrated Circuits) design cycle in a covert manner. These cause catastrophic consequences ranging from manipulating the functionality of individual blocks to disabling the entire chip. Thus, a need for detecting trojans becomes necessary. In this work, we propose a deep learning based approach for detecting trojans in IC chips. In particular, we insert trojans at the circuit-level and generate data by measuring power during normal operation and under attack. Further, we develop deep learning models using Neural networks and Auto-encoders to analyze datasets for outlier detection by profiling the normal behavior and leveraging them to detect anomalies in power consumption. Our approach is generic and non-invasive in that it can be applied to any block without any modifications to the design. Evaluation of the proposed approach shows an accuracy ranging from 92.23% to 99.33% in detecting trojans.

Memristive nano crossbar array has paved the way for high density memories but in a very low power environment. But such high density circuits face multiple problems at the time of implementation. The sneak path problem in crossbar array is one such problem which causes difficulty in distinguishing the logical states of the memristors. On the other hand, hardware Trojan causes malfunctioning of the circuit or performance degradation. If any of these are present in the nano crossbar, it is difficult to identify whether the performance degradation is due to the sneak path problem or due to that of Hardware Trojan.This paper makes a comparative study of the sneak path problem and the hardware Trojan to understand the performance difference between both. It is observed that some parameters are affected by sneak path problem but remains unaffected in presence of Hardware Trojan and vice versa. Analyzing these parameters, we can classify whether the performance degradation is due to sneak path or due to Hardware Trojan. The experimental results well establish the proposed methods of detection of hardware Trojan in presence of sneak path in memristive nano crossbar circuits.

In order to improve the continuity of cryptographic services and ensure the quality of services in the cloud environment, a dynamic migration framework of cryptographic service virtual machines based on the network shared storage system is proposed. Based on the study of the security threats in the migration process, a dynamic migration attack model is established, and the security requirement of dynamic migration is analyzed. It designs and implements the dynamic security migration management software, which includes a dynamic migration security enhancement module based on the Libvirt API, role-based access control policy, and transmission channel protection module. A cryptographic service virtual machine migration environment is built, and the designed management software and security mechanism are verified and tested. The experimental results show that the method proposed in the paper can effectively improve the security of cryptographic service virtual machine migration.

Nowadays is becoming trivial to have multiple virtual machines working in parallel on hardware platforms with high processing power. This appropriate cost effective approach can be found at Internet Service Providers, in cloud service providers’ environments, in research and development lab testing environment (for example Universities’ student’s lab), in virtual application for security evaluation and in many other places. In the aforementioned cases, it is often necessary to start and/or stop virtual machines on the fly. In cloud service providers all the creation / tear down actions are triggered by a customer request and cannot be postponed or delayed for later evaluation. When a new virtual machine is created, it is imperative to assign unique IP addresses to all network interfaces and also domain name system DNS records that contain text based data, IP addresses, etc. Even worse, if a virtual machine has to be stopped or torn down, the critical network resources such as IP addresses and DNS records have to be carefully controlled in order to avoid IP addresses conflicts and name resolution problems between an old virtual machine and a newly created virtual machine. This paper proposes a provisioning mechanism to avoid both DNS records and IP addresses conflicts due to human misconfiguration, problems that can cause networking operation service disruptions.

Broadcasting applications such as video surveillance systems are using High Definition (HD) videos. The use of high-resolution videos increases significantly the data volume of video coding standards such as High-Efficiency Video Coding (HEVC) and Advanced Video Coding (AVC), which increases the challenge for storing, processing, encrypting, and transmitting these data over different communication channels. Video compression standards use state-of-the-art techniques to compress raw video sequences more efficiently, such techniques require high computational complexity and memory utilization. With the emergent of using HEVC and video surveillance systems, many security risks arise such as man-in-the-middle attacks, and unauthorized disclosure. Such risks can be mitigated by encrypting the traffic of HEVC. The most widely used encryption algorithm is the Advanced Encryption Standard (AES). Most of the computational complexity in AES hardware-implemented is due to S-box or sub-byte operation and that because it needs many resources and it is a non-linear structure. The proposed AES S-box ROM design considers the latest HEVC used for homeland security video surveillance systems. This paper presents different designs for VHDL efficient ROM implementation of AES S-box using IP core generator, ROM components, and using Functions, which are all supported by Xilinx. IP core generator has Block Memory Generator (BMG) component in its library. S-box IP core ROM is implemented using Single port block memory. The S-box lookup table has been used to fill the ROM using the .coe file format provided during the initialization of the IP core ROM. The width is set to 8-bit to address the 256 values while the depth is set to 8-bit which represents the data filed in the ROM. The whole design is synthesized using Xilinx ISE Design Suite 14.7 software, while Modelism (version10.4a) is used for the simulation process. The proposed IP core ROM design has shown better memory utilization compared to non-IP core ROM design, which is more suitable for memory-intensive applications. The proposed design is suitable for implementation using the FPGA ROM design. Hardware complexity, frequency, memory utilization, and delay are presented in this paper.

Conventional Security Systems are improving with the advancement of Internet of Things (IoT) based technology. For better security, in addition to the currently available technology, surveillance systems are used. In this research, a Smart Surveillance System with machine-learning capabilities is designed to detect security breaches and it will resolve safety concerns. Machine learning algorithms are implemented to detect intruders as well as suspicious activities. Enery efficiency is the major concern for constant monitoring systems. As a result, the designed system focuses on power consumption by calibrating the system so that it can work on bare minimum power and additionally provides the required output. Fire sensor has also been integrated to detect fire for safety purposes. By adding upon the security infrastructure, next-generation smart surveillance systems can be created for a safe future. The developed system contains the necessary tools to recognize intruders by face recognition. Also using the ambient sensors (PIR sensor, fire detecting sensor), a secure environment is provided during working and non-working hours. The system shows high accuracy in human & flame detection. A more reliable security system can be created with the further development of this research.

A Visible Light Communication (VLC) is a fast growing technology became ubiquitous in the Optical wireless communication domain. It has the benefits of high security, high bandwidth, less power consumption, free from Electro Magnetic radiation hazards. VLC can help to address the looming spectrum crunch problem with secure communication in an unlimited spectrum. VLC provides extensive wireless connectivity with larger data densities than Wi-Fi along with added security features that annihilate unwanted external network invasion. The problem such as energy consumption and infrastructure complexity has been reduced by integrating the illumination and data services. The objective is to provide fast data communication with uninterrupted network connectivity and high accuracy to the user. In this paper, a proposed visible light communication system for transmitting text information using amplitude shift keying modulation (ASK) has been presented. Testing of transmitter and receiver block based on frequency, power and distance has been analyzed. The results show that the receiver is capable of receiving input data with minimum length under direct communication with the transmitter.

The goal of the edge computing framework is to solve the problem of management and control in the access of massive 5G terminals in the power Internet of things. Firstly, this paper analyzes the needs of IOT agent in 5G ubiquitous connection, equipment management and control, intelligent computing and other aspects. In order to meet with these needs, paper develops the functions and processes of the edge computing framework, including unified access of heterogeneous devices, protocol adaptation, edge computing, cloud edge collaboration, security control and so on. Finally, the performance of edge computing framework is verified by the pressure test of 5G wireless ubiquitous connection.

In recent times, the use of cloud computing has gained popularity all over the world in the context of performing smart computations on big data. The privacy of sensitive data of the client is of utmost important issues. Data leakage or hijackers may theft significant information about the client that ultimately may affect the reputation and prestige of its owner (bank) and client (customers). In general, to save the privacy of our banking data it is preferred to store, process, and transmit the data in the form of encrypted text. But now the main concern leads to secure computation over encrypted text or another possible way to perform computation over clouds makes data more vulnerable to hacking and attacks. Existing classical encryption techniques such as RSA, AES, and others provide secure transaction procedures for data over clouds but these are not fit for secure computation over data in the clouds. In 2009, Gentry comes with a solution for such issues and presents his idea as Homomorphic encryption (HE) that can perform computation over encrypted text without decrypting the data itself. Now a day's privacy-enhancing techniques (PET) are there to explore more potential benefits in security issues and useful in historical cases of privacy failure. Differential privacy, Federated analysis, homomorphic encryption, zero-knowledge proof, and secure multiparty computation are a privacy-enhancing technique that may useful in financial services as these techniques provide a fully-fledged mechanism for financial institutes. With the collaboration of industries, these techniques are may enable new data-sharing agreements for a more secure solution over data. In this paper, the primary concern is to investigate the different standards and properties of homomorphic encryption in digital banking and financial institutions.

In this work, an IF(intermediate frequency) module of a hyperspectral microwave radiometer based on a polyphase filter bank (PFB) and Discrete Fourier Transformation (DFT)is introduced. The IF module is designed with an 800MSPS sampling-rate ADC and a Xilinx Virtex-7 FPGA. The module can achieve 512 channels and a bandwidth of 400M and process all the sampled data in real-time. The test results of this module are given and analyzed, such as linearity, accuracy, etc. It can be used in various applications of microwave remote sensing. The system has strong expandability.