Biblio

Low-power wireless network technology is one of the main key characteristics in communication systems that are needed by the Internet of Things (IoT). Nowadays, the 6LoWPAN standard is one of the communication protocols which has been identified as an important protocol in IoT applications. Networking technology in 6LoWPAN transfer IPv6 packets efficiently in link-layer framework that is well-defined by IEEE 802.14.5 protocol. 6Lo WPAN development is still having problems such as threats and entrust crises. The most important part when developing this new technology is the challenge to secure the network. Data security is viewed as a major consideration in this network communications. Many researchers are working to secure 6LoWPAN communication by analyzing the architecture and network features. 6LoWPAN security weakness or vulnerability is exposed to various forms of network attack. In this paper, the security solutions for 6LoWPAN have been investigated. The requirements of safety in 6LoWPAN are also presented.

With the growing use of the Robot Operating System (ROS), it can be argued that it has become a de-facto framework for developing robotic solutions. ROS is used to build robotic applications for industrial automation, home automation, medical and even automatic robotic surveillance. However, whenever ROS is utilized, security is one of the main concerns that needs to be addressed in order to ensure a secure network communication of robots. Cyber-attacks may hinder evolution and adaptation of most ROS-enabled robotic systems for real-world use over the Internet. Thus, it is important to address and prevent security threats associated with the use of ROS-enabled applications. In this paper, we propose a novel approach for securing ROS-enabled robotic system by integrating ROS with the Message Queuing Telemetry Transport (MQTT) protocol. We manage to secure robots' network communications by providing authentication and data encryption, therefore preventing man-in-the-middle and hijacking attacks. We also perform real-world experiments to assess how the performance of a ROS-enabled robotic surveillance system is affected by the proposed approach.

This paper explores an innovative approach to the telerobotics reasoning architecture and networking, which offer a reliable and adaptable operational process for complex tasks. There are many operational challenges in the remote control for manufacturing that can be introduced by the network communications and Iatency. A new protocol, named compact Reliable UDP (compact-RUDP), has been developed to combine both data channelling and media streaming for robot teleoperation. The original approach ensures connection reliability by implementing a TCP-like sliding window with UDP packets. The protocol provides multiple features including data security, link status monitoring, bandwidth control, asynchronous file transfer and prioritizing transfer of data packets. Experiments were conducted on a 5DOF robotic arm where a cutting tool was mounted at its distal end. A light sensor was used to guide the robot movements, and a camera device to provide a video stream of the operation. The data communication reliability is evaluated using Round-Trip Time (RTT), and advanced robot path planning for distributed decision making between endpoints. The results show 88% correlation between the remotely and locally operated robots. The file transfers and video streaming were performed with no data loss or corruption on the control commands and data feedback packets.

The growing adoption of IoT devices in our daily life engendered a need for secure systems to safely store and analyze sensitive data as well as the real-time data processing system to be as fast as possible. The cloud services used to store and process sensitive data are often come out to be vulnerable to outside threats. Furthermore, to analyze streaming IoT data swiftly, they are in need of a fast and efficient system. The Paper will envision the aspects of complexity dealing with real time data from various devices in parallel, building solution to ingest data from different IOT devices, forming a secure platform to process data in a short time, and using various techniques of IOT edge computing to provide meaningful intuitive results to users. The paper envisions two modules of building a real time data analytics system. In the first module, we propose to maintain confidentiality and integrity of IoT data, which is of paramount importance, and manage large-scale data analytics with real-time data collection from various IoT devices in parallel. We envision a framework to preserve data privacy utilizing Trusted Execution Environment (TEE) such as Intel SGX, end-to-end data encryption mechanism, and strong access control policies. Moreover, we design a generic framework to simplify the process of collecting and storing heterogeneous data coming from diverse IoT devices. In the second module, we envision a drone-based data processing system in real-time using edge computing and on-device computing. As, we know the use of drones is growing rapidly across many application domains including real-time monitoring, remote sensing, search and rescue, delivery of goods, security and surveillance, civil infrastructure inspection etc. This paper demonstrates the potential drone applications and their challenges discussing current research trends and provide future insights for potential use cases using edge and on-device computing.

Nowadays big data has getting more and more attention in both the academic and the industrial research. With the development of big data, people pay more attention to data security. A significant feature of big data is the large size of the data. In order to improve the encryption speed of the large size of data, this paper uses the deep pipeline and full expansion technology to implement the AES encryption algorithm on FPGA. Achieved throughput of 31.30 Gbps with a minimum latency of 0.134 us. This design can quickly encrypt large amounts of data and provide technical support for the development of big data.

The paper considers data security issues in vehicle-to-infrastructure communications, where vehicles stream data to a road side unit. We assume aggregated data in road side units can be stored or used for data analytics. In this environment, there are issues in regards to the scalability of key management and computation limitations at the edge of the network. To address these issues, we suggest the formation of groups in the vehicle layer, where a group leader is assigned to communicate with group devices and the road side unit. We propose a lightweight permutation mechanism for preserving the confidentiality of sensory data.

Network security is an important issue in today's world with existence of network systems that communicate data and information about all aspects of our life, work and business. Network security is an important issue with connected networks and data communication between organisations of that specialized in different areas. Network security engineers spend a considerable amount of time to investigate network for security breaches and to enhance the security of their networks and data communications on their networks. They use Attack Graphs (AGs) which are graphical representation of networks to assist them in analysing large networks. With increase size of networks and their complexity, the use of attack graphs alone does not provide the necessary risk analysis and assessment facilities. There is a need for automated intelligent systems such as multiagent systems to assist in analysing, assessing and testing networks. Network systems changes with the increase in the size of organisation and connectivity of network of organisations based on the business needs or organisational or governmental rules and regulations. In this paper a multi-agent system is developed assist in analysing interconnected network to identify security risks. The multi-agent system is capable of security network analysis to identify paths using an attack graph of the network under consideration to protect network systems, as the networks grow and change, against possible attacks. The multiagent system uses a model developed by Mohammadian [3] for converting AGs to Fuzzy Cognitive Maps (FCMs) to identify attack paths from attack graphs and perform security risk analysis. In this paper a novel decision-making approach using FCMs is employed.

The advent of high performance fog and edge computing and high bandwidth connectivity has brought about changes to Internet-of-Things (IoT) service architectures, allowing for greater quantities of high quality information to be extracted from their environments to be processed. However, recently introduced international regulations, along with heightened awareness among consumers, have strengthened requirements to ensure data security, with significant financial and reputational penalties for organisations who fail to protect customers' data. This paper proposes the leveraging of fog and edge computing to facilitate processing of confidential user data, to reduce the quantity and availability of raw confidential data at various levels of the IoT architecture. This ultimately reduces attack surface area, however it also increases efficiency of the architecture by distributing processing amongst nodes and transmitting only processed data. However, such an approach is vulnerable to device level attacks. To approach this issue, a proposed System Security Manager is used to continuously monitor system resources and ensure confidential data is confined only to parts of the device that require it. In event of an attack, critical data can be isolated and the system informed, to prevent data confidentiality breach.

Privacy and security are the most important issues to the popularity of cloud computing service. In recent years, there are many research schemes of cloud computing privacy protection based on access control, attribute-based encryption (ABE), trust and reputation, but they are scattered and lack unified logic. In this paper, we systematically review and analyze relevant research achievements. First, we discuss the architecture, concepts and several shortcomings of cloud computing, and propose a framework of privacy protection; second, we discuss and analyze basic ABE, KP-ABE (key policy attribute-based encryption), CP-ABE (ciphertext policy attribute-based encryption), access structure, revocation mechanism, multi-authority, fine-grained, trace mechanism, proxy re-encryption (PRE), hierarchical encryption, searchable encryption (SE), trust, reputation, extension of tradition access control and hierarchical key; third, we propose the research challenge and future direction of the privacy protection in the cloud computing; finally, we point out corresponding privacy protection laws to make up for the technical deficiencies.

Currently, the guidelines for business entities to collect and use consumer information from online sources is guided by the Fair Information Practice Principles set forth by the Federal Trade Commission in the United States. These guidelines are inadequate, outdated, and provide little protection for consumers. Moreover, there are many techniques to anonymize the stored data that was collected by large companies and governments. However, what does not exist is a framework that is capable of evaluating and scoring the effects of this information in the event of a data breach. In this work, a framework for scoring and evaluating the vulnerability of private data is presented. This framework is created to be used in parallel with currently adopted frameworks that are used to score and evaluate other areas of deficiencies within the software, including CVSS and CWSS. It is dubbed the Privacy Assessment Vulnerability Scoring System (PAVSS) and quantifies the privacy-breach vulnerability an individual takes on when using an online platform. This framework is based on a set of hypotheses about user behavior, inherent properties of an online platform, and the usefulness of available data in performing a cyber attack. The weight each of these metrics has within our model is determined by surveying cybersecurity experts. Finally, we test the validity of our user-behavior based hypotheses, and indirectly our model by analyzing user posts from a large twitter data set.

At present, cloud computing technology has made outstanding contributions to the Internet in data unification and sharing applications. However, the problem of information security in cloud computing environment has to be paid attention to and effective measures have to be taken to solve it. In order to control the data security under cloud services, the DS evidence theory method is introduced. The trust management mechanism is established from the source of big data, and a cloud computing security assessment model is constructed to achieve the quantifiable analysis purpose of cloud computing security assessment. Through the simulation, the innovative way of quantifying the confidence criterion through big data trust management and DS evidence theory not only regulates the data credible quantification mechanism under cloud computing, but also improves the effectiveness of cloud computing security assessment, providing a friendly service support platform for subsequent cloud computing service.

Recent advances in machine learning enable wider applications of prediction models in cyber-physical systems. Smart grids are increasingly using distributed sensor settings for distributed sensor fusion and information processing. Load forecasting systems use these sensors to predict future loads to incorporate into dynamic pricing of power and grid maintenance. However, these inference predictors are highly complex and thus vulnerable to adversarial attacks. Moreover, the adversarial attacks are synthetic norm-bounded modifications to a limited number of sensors that can greatly affect the accuracy of the overall predictor. It can be much cheaper and effective to incorporate elements of security and resilience at the earliest stages of design. In this paper, we demonstrate how to analyze the security and resilience of learning-based prediction models in power distribution networks by utilizing a domain-specific deep-learning and testing framework. This framework is developed using DeepForge and enables rapid design and analysis of attack scenarios against distributed smart meters in a power distribution network. It runs the attack simulations in the cloud backend. In addition to the predictor model, we have integrated an anomaly detector to detect adversarial attacks targeting the predictor. We formulate the stealthy adversarial attacks as an optimization problem to maximize prediction loss while minimizing the required perturbations. Under the worst-case setting, where the attacker has full knowledge of both the predictor and the detector, an iterative attack method has been developed to solve for the adversarial perturbation. We demonstrate the framework capabilities using a GridLAB-D based power distribution network model and show how stealthy adversarial attacks can affect smart grid prediction systems even with a partial control of network.

With the development of IoT, smart health has significantly improved the quality of people's life. A large amount of smart health monitoring system has been proposed, which provides an opportunity for timely and efficient diagnosis. Nevertheless, most of them ignored the impact of environment on patients' health. Due to the openness of the communication channel, data security and privacy preservation are crucial problems to be solved. In this work, an environment aware privacy-preserving authentication protocol based on the fuzzy extractor and elliptic curve cryptography (ecc) is designed for health monitoring system with mutual authentication and anonymity. Edge computing unit can authenticate all environmental sensors at one time. Fuzzy synthetic evaluation model is utilized to evaluate the environment equality with the patients' temporal health index (THI) as an assessment factor, which can help to predict the appropriate environment. The session key is established for secure communication based on the predicted result. Through security analysis, the proposed protocol can prevent common attacks. Moreover, performance analysis shows that the proposed protocol is applicable for resource-limited smart devices in edge computing health monitoring system.

Energy is the material basis for human society to survive and has a very important strategic position in the national economy. With the advancement of Internet technology and the extensive use of clean energy, the energy industry has demonstrated a new development trend. Based on blockchain technology, this paper analyzes energy data security and multi-source synergy mechanism, processes and classifies a large amount of energy data in energy system, and builds a blockchain-based energy data supervision and transaction model. A summary tree of energy data is proposed; a consensus mechanism based on multi-source collaboration is proposed to ensure efficient negotiation; and finally, blockchain is verified in the energy scenario. This provides reference for the application of blockchain technology in the energy industry.

Automatic test systems designed to validate the performance of military and aerospace products have always been held to a higher standard; moreover, emerging threats to data security and instrumentation integrity continue to raise this bar. Engineers are faced with growing pressure to not only ensure that the unit under test (UUT) meets all design criteria, but that it remains safe from malicious attacks aimed at gaining access to test parameters or results, controlling of test sequences and functionality, downloading malware, or impacting functionality by way of counterfeit parts installed in instrumentation. This paper will delve into the cybersecurity issue from the perspective of the test development environment, including the use of test executives, and the challenges associated with minimizing impact to data integrity and access to control. An undetected data breach on military / aerospace automated test equipment (ATE) holds significance beyond just the test system, since mission critical electronics associated with avionics, radar, electronic warfare and missile assemblies must also be protected. One topic discussed will be the impact of adopting methods and procedures detailed in the Department of Defense's (DoD) Application Security Technical Implementation Guide, which is based on NIST documents and details how to manage and maintain a secure software-based system such as an ATE system. Another aspect of cybersecurity that is often overlooked in the world of commercial-off-the-shelf (COTS) instrumentation and switching systems is the potential impact on the UUT from substandard counterfeit parts and those embedded with malware. Concerns with counterfeit material can encompass a range of threats including the re-purposing of used parts and new knockoff parts with substandard operating characteristics represented and sold as new hardware. One of the most concerning aspects, parts intentionally infected with malware, can pose a significant risk to personnel and national security. We will discuss various strategies aimed at countering these threats, including the adoption of policies and procedures outlined in AS9100D and AS5553, which can mitigate these risks.

In the era of digitization, data security is a vital role in message transmission and all systems that deal with users require stronger encryption techniques that against brute force attack. Honey encryption (HE) algorithm is a user data protection algorithm that can deceive the attackers from unauthorized access to user, database and websites. The main part of conventional HE is distribution transforming encoder (DTE). However, the current DTE process using cumulative distribution function (CDF) has the weakness in message space limitation because CDF cannot solve the probability theory in more than four messages. So, we propose a new method in DTE process using discrete distribution function in order to solve message space limitation problem. In our proposed honeywords generation method, the current weakness of existing honeywords generation method such as storage overhead problem can be solved. In this paper, we also describe the case studies calculation of DTE in order to prove that new DTE process has no message space limitation and mathematical model using discrete distribution function for DTE process facilitates the distribution probability theory.

Today, data is all around us, every device that has computation power is generating the data and we can assume that in today's world there is about 2 quintillion bytes of data is been generating every day. as data increase in the database of the world servers so as the risk of data leak where we are talking about unlimited confidential data that is available online but as humans are developing their data online so as its security, today we've got hundreds of way to secure out data but not all are very successful or compatible there the big question arises that how to secure our data to hide our all the confidential information online, in other words one's all life work can be found online which is on risk of leak. all that says is today we have cloud above all of our data centers that stores all the information so that one can access anything from anywhere. in this paper we are introducing a new multimodal biometric system that is possible for the future smartphones to be supported where one can upload, download or modify the files using cloud without worrying about the unauthorized access of any third person as this security authentication uses combination of multiple security system available today that are not easy to breach such as DNA encryption which mostly is based on AES cipher here in this paper there we have designed triple layer of security.

To solve the problem of big data security and privacy protection, and expound the concept of cloud computing, big data and the relationship between them, the existing security and privacy protection method characteristic and problems were studied. A reference model is proposed which is based on cloud platform. In this model the physical level, data layer, interface layer and application layer step by step in to implement the system security risk early warning and threat perception, this provides an effective solution for the research of big data security. At the same time, a future research direction that uses the blockchain to solve cloud security and privacy protection is also pointed out.

Ubiquitous or pervasive computing is a new kind of computing, where specialized elements of hardware and software will have such high level of deployment that their use will be fully integrated with the environment. Augmented reality extends reality with virtual elements but tries to place the computer in a relatively unobtrusive, assistive role. To our knowledge, there is no specialized network middleware solution for large-scale mobile and pervasive augmented reality games. We present a work that focus on the creation of such network middleware for mobile and pervasive entertainment, applied to the area of large scale augmented reality games. In, this context, mechanisms are being studied, proposed and evaluated to deal with issues such as scalability, multimedia data heterogeneity, data distribution and replication, consistency, security, geospatial location and orientation, mobility, quality of service, management of networks and services, discovery, ad-hoc networking and dynamic configuration

Big data will bring revolutionary changes from life to thinking for society as a whole. At the same time, the massive data and potential value of big data are subject to many security risks. Aiming at the above problems, a data privacy protection model for big data platform is proposed. First, the data privacy protection model of big data for data owners is introduced in detail, including protocol design, logic design, complexity analysis and security analysis. Then, the query privacy protection model of big data for ordinary users is introduced in detail, including query protocol design and query mode design. Complexity analysis and safety analysis are performed. Finally, a stand-alone simulation experiment is built for the proposed privacy protection model. Experimental data is obtained and analyzed. The feasibility of the privacy protection model is verified.

As an inevitable trend of information development of hospitals, Internet hospitals provide a series of convenient online services for patients such as registration, consultation, queuing, payment and medicine pick-up. However, hospitals have to face huge challenges, and deploy an Internet medical care-oriented service security open platform to ensure the security of personal privacy data and avoid malicious attacks from the Internet, so as to prevent illegal stealing of medical data. The service security open platform provides visualized control for the unified and standardized connection process and data access process.

Expected and unexpected risks in cloud computing, which included data security, data segregation, and the lack of control and knowledge, have led to some dilemmas in several fields. Among all of these dilemmas, the privacy problem is even more paramount, which has largely constrained the prevalence and development of cloud computing. There are several privacy protection algorithms proposed nowadays, which generally include two categories, Anonymity algorithm, and differential privacy mechanism. Since many types of research have already focused on the efficiency of the algorithms, few of them emphasized the different orientation and demerits between the two algorithms. Motivated by this emerging research challenge, we have conducted a comprehensive survey on the two popular privacy protection algorithms, namely K-Anonymity Algorithm and Differential Privacy Algorithm. Based on their principles, implementations, and algorithm orientations, we have done the evaluations of these two algorithms. Several expectations and comparisons are also conducted based on the current cloud computing privacy environment and its future requirements.

Cloud computing is a new promising technology paradigm that can provide clients from the whole network with scalable storage resources and on-demand high-quality services. However, security concerns are raised when sensitive data are outsourced. Searchable encryption is a kind of cryptographic primitive that enables clients to selectively retrieve encrypted data, the existing schemes that support for sub-linear boolean queries are only considered in symmetric key setting, which makes a limitation for being widely deployed in many cloud applications. In order to address this issue, we propose a novel searchable asymmetric encryption scheme to support for sub-linear boolean query over encrypted data in a multi-client model that is extracted from an important observation that the outsourced database in cloud is continuously contributed and searched by multiple clients. For the purpose of introducing the scheme, we combine both the ideas of symmetric searchable encryption and public key searchable encryption and then design a novel secure inverted index. Furthermore, a detailed security analysis for our scheme is given under the simulation-based security definition. Finally, we conduct experiments for our construction on a real dataset (Enron) along with a performance analysis to show its practicality.

In a computer world, to identify anyone by doing a job or to authenticate by checking their identification and give access to computer. Access Control model comes in to picture when require to grant the permissions to individual and complete the duties. The access control models cannot give complete security when dealing with cloud computing area, where access control model failed to handle the attributes which are requisite to inhibit access based on time and location. When the data outsourced in the cloud, the information holders expect the security and confidentiality for their outsourced data. The data will be encrypted before outsourcing on cloud, still they want control on data in cloud server, where simple encryption is not a complete solution. To irradiate these issues, unlike access control models proposed Attribute Based Encryption standards (ABE). In ABE schemes there are different types like Key Policy-ABE (KP-ABE), Cipher Text-ABE (CP-ABE) and so on. The proposed method applied the access control policy of CP-ABE with Advanced Encryption Standard and used elliptic curve for key generation by using multi stage encryption which divides the users into two domains, public and private domains and shuffling the data base records to protect from inference attacks.

Cloud computing has a major role in the development of commercial systems. It enables companies like Microsoft, Amazon, IBM and Google to deliver their services on a large scale to its users. A cloud service provider manages cloud computing based services and applications. For any organization a cloud service provider (CSP) is an entity which works within it. So it suffers from vulnerabilities associated with organization, including internal and external attacks. So its challenge to organization to secure a cloud service provider while providing quality of service. Attribute based encryption can be used to provide data security with Key policy attribute based encryption (KP-ABE) or ciphertext policy attribute based encryption (CP-ABE). But these schemes has lack of scalability and flexibility. Hierarchical CP-ABE scheme is proposed here to provide fine grained access control. Data security is achieved using encryption, authentication and authorization mechanisms. Attribute key generation is proposed for implementing authorization of users. The proposed system is prevented by SQL Injection attack.