Biblio

In spite of being a promising technology which will make our lives a lot easier we cannot be oblivious to the fact IoT is not safe from online threat and attacks. Thus, along with the growth of IoT we also need to work on its aspects. Taking into account the limited resources that these devices have it is important that the security mechanisms should also be less complex and do not hinder the actual functionality of the device. In this paper, we propose an ECC based lightweight authentication for IoT devices which deploy RFID tags at the physical layer. ECC is a very efficient public key cryptography mechanism as it provides privacy and security with lesser computation overhead. We also present a security and performance analysis to verify the strength of our proposed approach.

Public cloud data storage services were considered as a potential alternative to store low-cost digital data in the short term. They are offered by different providers on the Internet. Some providers offer limited free plans for the users who are starting the service. However, data security concern arises when data stored are considered as a valuable asset. This study explores the usage of secret sharing scheme: Rabin's IDA and Shamir's SSA to implement a tool called dCloud for file protection stored in public cloud storage in a seamless way. It addresses data security by hiding its complexities when targeting ordinary non-technical users. The secret key is automatically generated by dCloud in a secure random way on Rabin's IDA. Shamir's SSA completes the process through dispersing the key into each of Rabin's IDA output files. Moreover, the Hash value of the original file is added to each of those output files to confirm the integrity of the file during reconstruction. Besides, the authentication key is used to communicate with all of the defined service providers during storage and reconstruction as well. It is stored into local secure key-store. By having a key to access the key-store, an ordinary non-technical user will be able to use dCloud to store and retrieve targeted file within defined public cloud storage services securely.

Recently, the home healthcare system has emerged as one of the most useful technology for e-healthcare. Contrary to classical recording methods of patient's medical data, which are, based on paper documents, nowadays all this sensitive data can be managed and forwarded through digital systems. These make possible for both patients and healthcare workers to access medical data or receive remote medical treatment using wireless interfaces whenever and wherever. However, simplifying access to these sensitive and private data can directly put patient's health and life in danger. In this paper, we propose a secure and lightweight biometric-based remote patient authentication scheme using elliptic curve encryption through which two mobile healthcare system communication parties could authenticate each other in public mobile healthcare environments. The security and performance analysis demonstrate that our proposal achieves better security than other concurrent schemes, with lower storage, communication and computation costs.

In Smart Grids (SGs), data aggregation process is essential in terms of limiting packet size, data transmission amount and data storage requirements. This paper presents a novel Domingo-Ferrer additive privacy based Secure Data Aggregation (SDA) scheme for Fog Computing based SGs (FCSG). The proposed protocol achieves end-to-end confidentiality while ensuring low communication and storage overhead. Data aggregation is performed at fog layer to reduce the amount of data to be processed and stored at cloud servers. As a result, the proposed protocol achieves better response time and less computational overhead compared to existing solutions. Moreover, due to hierarchical architecture of FCSG and additive homomorphic encryption consumer privacy is protected from third parties. Theoretical analysis evaluates the effects of packet size and number of packets on transmission overhead and the amount of data stored in cloud server. In parallel with the theoretical analysis, our performance evaluation results show that there is a significant improvement in terms of data transmission and storage efficiency. Moreover, security analysis proves that the proposed scheme successfully ensures the privacy of collected data.

Nowadays, the proliferation of smart, communication-enable devices is opening up many new opportunities of pervasive applications. A major requirement of pervasive applications is to be secured. The complexity to secure pervasive systems is to address a end-to-end security level: from the device to the services according to the entire life cycle of devices, applications and platform. In this article, we propose a solution combining both hardware and software elements to secure communications between devices and pervasive platform based on certificates issued from a Public Key Infrastructure. Our solution is implemented and validated with a real device extended by a secure element and our own Public Key Infrastructure.

Due to expansion of Internet and huge dataset, many organizations started to use cloud. Cloud Computing moves the application software and databases to the centralized large data centers, where the management of the data and services may not be fully trustworthy. Due to this cloud faces many threats. In this work, we study the problem of ensuring the integrity of data storage in Cloud Computing. To reduce the computational cost at user side during the integrity verification of their data, the notion of public verifiability has been proposed. Our approach is to create a new entity names Cloud Service Controller (CSC) which will help us to reduce the trust on the Third Party Auditor (TPA). We have strengthened the security model by using AES Encryption with SHA-S12 & tag generation. In this paper we get a brief introduction about the file upload phase, integrity of the file & Proof of Retrievability of the file.

As an information hinge of various trades and professions in the era of big data, cloud data center bears the responsibility to provide uninterrupted service. To cope with the impact of failure and interruption during the operation on the Quality of Service (QoS), it is important to guarantee the resilience of cloud data center. Thus, different resilience actions are conducted in its life circle, that is, resilience strategy. In order to measure the effect of resilience strategy on the system resilience, this paper propose a new approach to model and evaluate the resilience strategy for cloud data center focusing on its core part of service providing-IT architecture. A comprehensive resilience metric based on resilience loss is put forward considering the characteristic of cloud data center. Furthermore, mapping model between system resilience and resilience strategy is built up. Then, based on a hierarchical colored generalized stochastic petri net (HCGSPN) model depicting the procedure of the system processing the service requests, simulation is conducted to evaluate the resilience strategy through the metric calculation. With a case study of a company's cloud data center, the applicability and correctness of the approach is demonstrated.

Bitcoin provides freshness properties by forming a blockchain where each block is associated with its timestamp and the previous block. Due to these properties, the Bitcoin protocol is being used as a decentralized, trusted, and secure timestamping service. Although Bitcoin participants which create new blocks cannot modify their order, they can manipulate timestamps almost undetected. This undermines the Bitcoin protocol as a reliable timestamping service. In particular, a newcomer that synchronizes the entire blockchain has a little guarantee about timestamps of all blocks. In this paper, we present a simple yet powerful mechanism that increases the reliability of Bitcoin timestamps. Our protocol can provide evidence that a block was created within a certain time range. The protocol is efficient, backward compatible, and surprisingly, currently deployed SSL/TLS servers can act as reference time sources. The protocol has many applications and can be used for detecting various attacks against the Bitcoin protocol.

Peer-to-peer computing (P2P) refers to the famous technology that provides peers an equal spontaneous collaboration in the network by using appropriate information and communication systems without the need for a central server coordination. Today, the interconnection of several P2P networks has become a genuine solution for increasing system reliability, fault tolerance and resource availability. However, the existence of security threats in such networks, allows us to investigate the safety of users from P2P threats by studying the effects of competition between these interconnected networks. In this paper, we present an e-epidemic model to characterize the worm propagation in an interconnected peer-to-peer network. Here, we address this issue by introducing a model of network competition where an unprotected network is willing to partially weaken its own safety in order to more severely damage a more protected network. The unprotected network can infect all peers in the competitive networks after their non react against the passive worm propagation. Our model also evaluated the effect of an immunization strategies adopted by the protected network to resist against attacking networks. The launch time of immunization strategies in the protected network, the number of peers synapse connected to the both networks, and other effective parameters have also been investigated in this paper.

Web application technologies are growing rapidly with continuous innovation and improvements. This paper focuses on the popular Spring Boot [1] java-based framework for building web and enterprise applications and how it provides the flexibility for service-oriented architecture (SOA). One challenge with any Spring-based applications is its level of complexity with configurations. Spring Boot makes it easy to create and deploy stand-alone, production-grade Spring applications with very little Spring configuration. Example, if we consider Spring Model-View-Controller (MVC) framework [2], we need to configure dispatcher servlet, web jars, a view resolver, and component scan among other things. To solve this, Spring Boot provides several Auto Configuration options to setup the application with any needed dependencies. Another challenge is to identify the framework dependencies and associated library versions required to develop a web application. Spring Boot offers simpler dependency management by using a comprehensive, but flexible, framework and the associated libraries in one single dependency, which provides all the Spring related technology that you need for starter projects as compared to CRUD web applications. This framework provides a range of additional features that are common across many projects such as embedded server, security, metrics, health checks, and externalized configuration. Web applications are generally packaged as war and deployed to a web server, but Spring Boot application can be packaged either as war or jar file, which allows to run the application without the need to install and/or configure on the application server. In this paper, we discuss how Atmospheric Radiation Measurement (ARM) Data Center (ADC) at Oak Ridge National Laboratory, is using Spring Boot to create a SOA based REST [4] service API, that bridges the gap between frontend user interfaces and backend database. Using this REST service API, ARM scientists are now able to submit reports via a user form or a command line interface, which captures the same data quality or other important information about ARM data.

Cloud storage is an exclusive resource in cloud computing, which helps to store and share the data on cloud storage server. Clients upload the data and its hash information n server together on cloud storage. The file owner always concern about data security like privacy and unauthorized access to third party. The owner also wants to ensure the integrity data during communication process. To ensure integrity, we propose a framework based on third party auditor which checks the integrity and correctness of data during audit process. Our aim is to design custom hash for the file which is not only justifies the integrity but also version information about file.

Cloud computing is a standard architecture for providing computing services among servers and cloud user (CU) for preserving data from unauthorized users. Therefore, the user authentication is more reliable to ensure cloud services accessed only by a genuine user. To improve the authentication accuracy, Tiger Hash-based Kerberos Biometric Blowfish Authentication (TH-KBBA) Mechanism is introduced for accessing data from server. It comprises three steps, namely Registration, Authentication and Ticket Granting. In the Registration process, client enrolls user details and stores on cloud server (CS) using tiger hashing function. User ID and password is given by CS after registration. When client wants to access data from CS, authentication server (AS) verifies user identity by sending a message. When authenticity is verified, AS accepts user as authenticated user and convinces CS that user is authentic. For convincing process, AS generates a ticket and encrypted using Blowfish encryption. Encrypted ticket is sent back to user. Then, CU sends message to server containing users ID and encrypted ticket. Finally, the server decrypts ticket using blowfish decryption and verifies the user ID. If these two ID gets matched, the CS grants requested data to the user. Experimental evaluation of TH-KBBA mechanism and existing methods are carried out with different factors such as Authentication accuracy, authentications time and confidentiality rate with respect to a number of CUs and data.

It is technically challenging to conduct a security analysis of a dynamic network, due to the lack of methods and techniques to capture different security postures as the network changes. Graphical Security Models (e.g., Attack Graph) are used to assess the security of network systems, but it typically captures a snapshot of a network state to carry out the security analysis. To address this issue, we propose a new Graphical Security Model named Time-independent Hierarchical Attack Representation Model (Ti-HARM) that captures security of multiple network states by taking into account the time duration of each network state and the visibility of network components (e.g., hosts, edges) in each state. By incorporating the changes, we can analyse the security of dynamic networks taking into account all the threats appearing in different network states. Our experimental results show that the Ti-HARM can effectively capture and assess the security of dynamic networks which were not possible using existing graphical security models.

In this paper, we propose a new authentication method to prevent authentication vulnerability of Claim Token method of Membership Service provide in Private BlockChain. We chose Hyperledger Fabric v1.0 using JWT authentication method of membership service. TOTP, which generate OTP tokens and user authentication codes that generate additional time-based password on existing authentication servers, has been applied to enforce security and two-factor authentication method to provide more secure services.

Moving target defense (MTD) is becoming popular with the advancements in Software Defined Networking (SDN) technologies. With centralized management through SDN, changing the network attributes such as routes to escape from attacks is simple and fast. Yet, the available alternate routes are bounded by the network topology, and a persistent attacker that continuously perform the reconnaissance can extract the whole link-map of the network. To address this issue, we propose to use virtual shadow networks (VSNs) by applying Network Function Virtualization (NFV) abilities to the network in order to deceive attacker with the fake topology information and not reveal the actual network topology and characteristics. We design this approach under a formal framework for Internet Service Provider (ISP) networks and apply it to the recently emerged indirect DDoS attacks, namely Crossfire, for evaluation. The results show that attacker spends more time to figure out the network behavior while the costs on the defender and network operations are negligible until reaching a certain network size.

There are different traditional approaches to handling a large number of computers or workstations in a campus setting, ranging from imaging to virtualized environments. The common factor among the traditional approaches is to have a user workstation with a local hard drive (nonvolatile storage), scratchpad volatile memory, a CPU (Central Processing Unit) and connectivity to access resources on the network. This paper presents the use of block streaming, normally used for storage, to serve operating system and applications on-demand over the network to a workstation, also referred to as a client, a client computer, or a client workstation. In order to avoid per seat licensing, an Open Source solution is used, and in order to minimize the field maintenance and meet security privacy constraints, a workstation need not have a permanent storage such as a hard disk drive. A complete blue print, based on performance analyses, is provided to determine the type of network architecture, servers, workstations per server, and minimum workstation configuration, suitable for supporting such a solution. The results of implementing the proposed solution campus wide, supporting more than 450 workstations, are presented as well.

Robotics and the Internet of Things (IoT) are enveloping our society at an exponential rate due to lessening costs and better availability of hardware and software. Additionally, Cloud Robotics and Robot Operating System (ROS) can offset onboard processing power. However, strong and fundamental security practices have not been applied to fully protect these systems., partially negating the benefits of IoT. Researchers are therefore tasked with finding ways of securing communications and systems. Since security and convenience are oftentimes at odds, securing many heterogeneous components without compromising performance can be daunting. Protecting systems from attacks and ensuring that connections and instructions are from approved devices, all while maintaining the performance is imperative. This paper focuses on the development of security best practices and a mesh framework with an open-source, multipoint-to-multipoint virtual private network (VPN) that can tie Linux, Windows, IOS., and Android devices into one secure fabric, with heterogeneous mobile robotic platforms running ROSPY in a secure cloud robotics infrastructure.

The importance of peer-to-peer (P2P) network overlays produced enormous interest in the research community due to their robustness, scalability, and increase of data availability. P2P networks are overlays of logically connected hosts and other nodes including servers. P2P networks allow users to share their files without the need for any centralized servers. Since P2P networks are largely constructed of end-hosts, they are susceptible to abuse and malicious activity, such as sybil attacks. Impostors perform sybil attacks by assigning nodes multiple addresses, as opposed to a single address, with the goal of degrading network quality. Sybil nodes will spread malicious data and provide bogus responses to requests. To prevent sybil attacks from occurring, a novel defense mechanism is proposed. In the proposed scheme, the DHT key-space is divided and treated in a similar manner to radio frequency allocation incensing. An overlay of trusted nodes is used to detect and handle sybil nodes with the aid of source-destination pairs reporting on each other. The simulation results show that the proposed scheme detects sybil nodes in large sized networks with thousands of interactions.

With spatial crowdsourcing (SC), requesters outsource their spatiotemporal tasks (tasks associated with location and time) to a set of workers, who will perform the tasks by physically traveling to the tasks' locations. However, current solutions require the locations of the workers and/or the tasks to be disclosed to untrusted parties (SC server) for effective assignments of tasks to workers. In this paper we propose a framework for assigning tasks to workers in an online manner without compromising the location privacy of workers and tasks. We perturb the locations of both tasks and workers based on geo-indistinguishability and then devise techniques to quantify the probability of reachability between a task and a worker, given their perturbed locations. We investigate both analytical and empirical models for quantifying the worker-task pair reachability and propose task assignment strategies that strike a balance among various metrics such as the number of completed tasks, worker travel distance and system overhead. Extensive experiments on real-world datasets show that our proposed techniques result in minimal disclosure of task locations and no disclosure of worker locations without significantly sacrificing the total number of assigned tasks.

With spatial crowdsourcing (SC), requesters outsource their spatiotemporal tasks (tasks associated with location and time) to a set of workers, who will perform the tasks by physically traveling to the tasks' locations. However, current solutions require the locations of the workers and/or the tasks to be disclosed to untrusted parties (SC server) for effective assignments of tasks to workers. In this paper we propose a framework for assigning tasks to workers in an online manner without compromising the location privacy of workers and tasks. We perturb the locations of both tasks and workers based on geo-indistinguishability and then devise techniques to quantify the probability of reachability between a task and a worker, given their perturbed locations. We investigate both analytical and empirical models for quantifying the worker-task pair reachability and propose task assignment strategies that strike a balance among various metrics such as the number of completed tasks, worker travel distance and system overhead. Extensive experiments on real-world datasets show that our proposed techniques result in minimal disclosure of task locations and no disclosure of worker locations without significantly sacrificing the total number of assigned tasks.

The use of Electric Vehicle (EV) is growing rapidly due to its environmental benefits. However, the major problem of these vehicles is their limited battery, the lack of charging stations and the re-charge time. Introducing Information and Communication Technologies, in the field of EV, will improve energy efficiency, energy consumption predictions, availability of charging stations, etc. The Internet of Vehicles based only on Electric Vehicles (IoEV) is a complex system. It is composed of vehicles, humans, sensors, road infrastructure and charging stations. All these entities communicate using several communication technologies (ZigBee, 802.11p, cellular networks, etc). IoEV is therefore vulnerable to significant attacks such as DoS, false data injection, modification. Hence, security is a crucial factor for the development and the wide deployment of Internet of Electric Vehicles (IoEV). In this paper, we present an overview of security issues of the IoEV architecture and we highlight open issues that make the IoEV security a challenging research area in the future.

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.

A Cyber Physical Sensor System (CPSS) consists of a computing platform equipped with wireless access points, sensors, and actuators. In a Cyber Physical System, CPSS constantly collects data from a physical object that is under process and performs local real-time control activities based on the process algorithm. The collected data is then transmitted through the network layer to the enterprise command and control center or to the cloud computing services for further processing and analysis. This paper investigates the CPSS' most common cyber security threats and vulnerabilities and provides countermeasures. Furthermore, the paper addresses how the CPSS are attacked, what are the leading consequences of the attacks, and the possible remedies to prevent them. Detailed case studies are presented to help the readers understand the CPSS threats, vulnerabilities, and possible solutions.

Moving Target Defence (MTD) has been recently proposed and is an emerging proactive approach which provides an asynchronous defensive strategies. Unlike traditional security solutions that focused on removing vulnerabilities, MTD makes a system dynamic and unpredictable by continuously changing attack surface to confuse attackers. MTD can be utilized in cloud computing to address the cloud's security-related problems. There are many literature proposing MTD methods in various contexts, but it still lacks approaches to evaluate the effectiveness of proposed MTD method. In this paper, we proposed a combination of Shuffle and Diversity MTD techniques and investigate on the effects of deploying these techniques from two perspectives lying on two groups of security metrics (i) system risk: which is the cloud providers' perspective and (ii) attack cost and return on attack: which are attacker's point of view. Moreover, we utilize a scalable Graphical Security Model (GSM) to enhance the security analysis complexity. Finally, we show that combining MTD techniques can improve both aforementioned two groups of security metrics while individual technique cannot.

It is well known that distributed cyber attacks simultaneously launched from many hosts have caused the most serious problems in recent years including problems of privacy leakage and denial of services. Thus, how to detect those attacks at early stage has become an important and urgent topic in the cyber security community. For this purpose, recognizing C&C (Command & Control) communication between compromised bots and the C&C server becomes a crucially important issue, because C&C communication is in the preparation phase of distributed attacks. Although attack detection based on signature has been practically applied since long ago, it is well-known that it cannot efficiently deal with new kinds of attacks. In recent years, ML(Machine learning)-based detection methods have been studied widely. In those methods, feature selection is obviously very important to the detection performance. We once utilized up to 55 features to pick out C&C traffic in order to accomplish early detection of DDoS attacks. In this work, we try to answer the question that "Are all of those features really necessary?" We mainly investigate how the detection performance moves as the features are removed from those having lowest importance and we try to make it clear that what features should be payed attention for early detection of distributed attacks. We use honeypot data collected during the period from 2008 to 2013. SVM(Support Vector Machine) and PCA(Principal Component Analysis) are utilized for feature selection and SVM and RF(Random Forest) are for building the classifier. We find that the detection performance is generally getting better if more features are utilized. However, after the number of features has reached around 40, the detection performance will not change much even more features are used. It is also verified that, in some specific cases, more features do not always means a better detection performance. We also discuss 10 important features which have the biggest influence on classification.