Biblio

With fast growing research in the area of application partitioning for offloading, determining which devices to prioritize over the other for mobile code offloading is fundamental. Multiple methods can be adopted using both single-criterion and multiple-criteria strategies. Due to the characteristics of pervasive environments, whereby devices having different computing capability, different level of privacy and security and the mobility nature in such environment makes the decision-making process complex. To this end, this paper proposes a method using a combination of the method Analytic Hierarchy Process (AHP) to calculate weights criteria of participating devices. Next the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) is considered to sort in order of priority the participating devices, hence facilitating the decision to opt for which participating device first. An evaluation of the method is also presented.

Internet of Things (IoT) have offered great opportunities for the growth of smart objects in the last decade. Smart devices are deployed in many fields such as smart cities, healthcare and agriculture. One of the applications of IoT is Wireless Sensor Networks (WSN) that require inexpensive and space-economic design for remote sensing and communication capabilities. This, unfortunately, lead to their inherent security vulnerabilities. Lightweight cryptography schemes are designed to counter many attacks in low-powered devices such as the IoT and WSN. These schemes can provide support for data encryption and key management while maintaining some level of efficiency. Most of these block ciphers provide good security. However, due to the complex cryptographic scheme's efficiency and optimisation is an issue. In this work, we focus on a new lightweight encryption scheme called the Klein block cipher. The algorithms of Klein block cipher are analysed for performance and security optimisations. A new algorithm which consists of 3-layer substitute box is proposed to reduce the need for resource consumption but maintain the security.

In recent years, several trust management frame-works and models have been proposed for the Internet of Things (IoT). Focusing primarily on distributed trust management schemes; testing and validation of these models is still a challenging task. It requires the implementation of the proposed trust model for verification and validation of expected outcomes. Nevertheless, a stand-alone and standard IoT network simulator for testing of distributed trust management scheme is not yet available. In this paper, a .NET-based Distributed Trust Management Simulator for IoT Networks (DTMSim-IoT) is presented which enables the researcher to implement any static/dynamic trust management model to compute the trust value of a node. The trust computation will be calculated based on the direct-observation and trust value is updated after every transaction. Transaction history and logs of each event are maintained which can be viewed and exported as .csv file for future use. In addition to that, the simulator can also draw a graph based on the .csv file. Moreover, the simulator also offers to incorporate the feature of identification and mitigation of the On-Off Attack (OOA) in the IoT domain. Furthermore, after identifying any malicious activity by any node in the networks, the malevolent node is added to the malicious list and disseminated in the network to prevent potential On-Off attacks.

Cyber-Physical Systems (CPS) such as intelligent connected vehicles, smart farming and smart logistics are constantly generating tons of data and requiring real-time data processing capabilities. Therefore, Edge Computing which provisions computing resources close to the End Devices from the network edge is becoming the ideal platform for CPS. However, it also brings many issues and one of the most prominent challenges is how to ensure the development of trustworthy smart services given the dynamic and distributed nature of Edge Computing. To tackle this challenge, this paper proposes a novel Federated Learning based Edge Computing platform for CPS, named “FengHuoLun”. Specifically, based on FengHuoLun, we can: 1) implement smart services where machine learning models are trained in a trusted Federated Learning framework; 2) assure the trustworthiness of smart services where CPS behaviours are tested and monitored using the Federated Learning framework. As a work in progress, we have presented an overview of the FengHuoLun platform and also some preliminary studies on its key components, and finally discussed some important future research directions.

The Internet of Things is progressively turning into a pervasive computing service, needing enormous volumes of data storage and processing. However, due to the distinctive properties of resource constraints, self-organization, and short-range communication in Internet of Things (IoT), it always adopts to cloud for outsourced storage and computation. This integration of IoT with cloud has a row of unfamiliar security challenges for the data at rest. Cloud computing delivers highly scalable and flexible computing and storage resources on pay-per-use policy. Cloud computing services for computation and storage are getting increasingly popular and many organizations are now moving their data from in-house data centers to the Cloud Storage Providers (CSPs). Time varying workload and data intensive IoT applications are vulnerable to encounter challenges while using cloud computing services. Additionally, the encryption techniques and third-party auditors to maintain data integrity are still in their developing stage and therefore the data at rest is still a concern for IoT applications. In this paper, we perform an analysis study to investigate the challenges and strategies adapted by Cloud Computing to facilitate a safe transition of IoT applications to the Cloud.

IoT adds more flexibility in many areas of applications to makes it easy to monitor and manage data instantaneously. However, IoT has many challenges regarding its security and storage issues. Moreover, the third-party trusting agents of IoT devices do not support sufficient security level between the network peers. This paper proposes improving the trust, processing power, and storage capability of IoT in distributed system topology by adopting the blockchain approach. An application, IoT Trust Management (ITM), is proposed to manage the trust of the shared content through the blockchain network, e.g., supply chain. The essential key in ITM is the trust management of IoT devices data are done using peer to peer (P2P), i.e., no third-party. ITM is running on individual python nodes and interact with frontend applications creating decentralized applications (DApps). The IoT data shared and stored in a ledger, which has the IoT device published details and data. ITM provides a higher security level to the IoT data shared on the network, such as unparalleled security, speed, transparency, cost reduction, check data, and Adaptability.

This paper proposes a proximity-aware extensions to the current Bitcoin protocol, named Master Node Based Clustering (MNBC). The ultimate purpose of the proposed protocol is to evaluate the security and performance of grouping nodes based on physical proximity. In MNBC protocol, physical internet connectivity increases as well as the number of hops between nodes decreases through assigning nodes to be responsible for propagating based on physical internet proximity.

Blockchain is developing rapidly in various domains for its security. Nowadays, one of the most crucial fundamental concerns is internet security. Blockchain is a novel solution to enhance the security of network applications. However, there are no precise frameworks to secure the Internet of Vehicle (IoV) using Blockchain technology. In this paper, a blockchain-based smart internet of vehicle (BSIoV) framework has been proposed due to the cooperative, collaborative, transparent, and secure characteristics of Blockchain. The main contribution of the proposed work is to connect vehicle-related authorities together to fix a secure and transparent vehicle-to-everything (V2X) communication through the peer-to-peer network connection and provide secure services to the intelligent transport systems. A key management strategy has been included to identify a vehicle in this proposed system. The proposed framework can also provide a significant solution for the data security and safety of the connected vehicles in blockchain network.

The privacy protection and information security are two crucial issues for future advanced artificial intelligence devices, especially for cooperative system with rich core data exchange which may offer opportunities for attackers to fake interaction messages. To combat such threat, great efforts have been made by introducing trust mechanism in initiative or passive way. Furthermore, blockchain and distributed ledger technology provide a decentralized and peer-to-peer network, which has great potential application for multi-agent system, such as IoTs and robots. It eliminates third-party interference and data in the blockchain are stored in an encrypted way permanently and anti-destroys. In this paper, a methodology of blockchain is proposed and designed for advanced cooperative system with artificial intelligence to protect privacy and sensitive data exchange between multi-agents. The validation procedure is performed in laboratory by a three-level computing networks of Raspberry Pi 3B+, NVIDIA Jetson Tx2 and local computing server for a robot system with four manipulators and four binocular cameras in peer computing nodes by Go language.

Blockchain become a suitable platform for peer to peer energy trade as it facilitates secure interactions among parties with trust or a mutual trusted 3rd party. However, the scalability issue of blockchains is a problem for real-time energy trade to be completed within a small time duration. In this paper, we use offline channels for blockchains to circumvent scalability problems of blockchains for peer to peer energy trade with small trade duration. We develop algorithms to find stable coalitions for energy trade using blockchain offline channels. We prove that our solution is secure against adversarial prosumer behaviors, it supports real-time trade as the algorithm is guaranteed to find and record stable coalitions before a fixed time, and the coalition structure generated by the algorithm is efficient.

Mobile peer-to-peer network (MP2P) attracts increasing attentions due to the ubiquitous use of mobile communication and huge success of peer-to-peer (P2P) mode. However, open p2p mode makes nodes tend to be selfish, and the scarcity of resources in mobile nodes aggravates this problem, thus the nodes easily express a non-complete cooperative (NCC) attitude. Therefore, an identification of non-complete cooperative nodes and a corresponding trust routing scheme are proposed for MP2P in this paper. The concept of octant is firstly introduced to build a trust model which analyzes nodes from three dimensions, namely direct trust, internal state and recommendation reliability, and then the individual non-complete cooperative (INCC) nodes can be identified by the division of different octants. The direct trust monitors nodes' external behaviors, and the consideration of internal state and recommendation reliability contributes to differentiate the subjective and objective non-cooperation, and mitigate the attacks about direct trust values respectively. Thus, the trust model can identify various INCC nodes accurately. On the basis of identification of INCC nodes, cosine similarity method is applied to identify collusive non-complete cooperate (CNCC) nodes. Moreover, a trust routing scheme based on the identification of NCC nodes is presented to reasonably deal with different kinds of NCC nodes. Results from extensive simulation experiments demonstrate that this proposed identification and routing scheme have better performances, in terms of identification precision and packet delivery fraction than current schemes respectively.

In an era of technological revolution in which everything becomes smarter and connected, the blockchain can introduce a new model for energy transactions able to grant more simplicity, security and transparency for end-users. The blockchain technology is characterized by a distributed architecture without a trusted and centralized authority, and, therefore, it appears as the perfect solutions for managing exchanges between peers. In this paper, a market algorithm that can be easily transferred to a smart contract for maximizing the match between produced and consumed energy in a micro-grid is presented. The algorithm supports energy transactions between peers (both producers and consumers) and could be one of the main executables implemented using a blockchain platform. The case study presented in this paper shows how the end-users through the blockchain could select among the possible energy transactions those more suitable to offer specific ancillary services to the grid operator without involving the grid operator itself or a third-party aggregator.

A new ONU authentication method based on POTS key matching is proposed, which makes use of ONU's own FXS resources and connects with a pots phone by dialing the corresponding LOID service key and authentication code that will be sent to ONU. The key combined with LOID service key and authentication code received by ONU will be filtered and then the LOID authentication code is obtained, which is put to match with DigitMap preset into the database of ONU. The LOID authentication code will be transmitted to OLT so as to achieve the purpose of ONU authentication and authorization if the match result is successful.

Currently, data security issues are remaining as a major concern during digital communication. A large amount of crucial data is transmitted through the communication channel. There are many cryptographic algorithms available, which are used for providing data security during communication and storage process. However, the data needs to be decrypted for performing operations, which may lead to elevation of the privilege of data. The pin or passwords used for decryption of data can be easily identified using a brute force attack. This leads to losing the confidentiality of crucial data to an unauthorized user. In the proposed system, a combination of Homomorphic and Honey encryption is used to improve data confidentiality and user authentication problems. Thus, the system provides better data security for the issues related to outsourced databases.

Cost-effective and efficient sequencing technologies have resulted in massive genomic data availability. To compute on a large-scale genomic dataset, it is often required to outsource the dataset to the cloud. To protect data confidentiality, data owners encrypt sensitive data before outsourcing. Outsourcing enhances data owners to eliminate the storage management problem. Since genome data is large in volume, secure execution of researchers query is challenging. In this paper, we propose a method to securely perform count query on datasets containing genotype, phenotype, and numeric data. Our method modifies the prefix-tree proposed by Hasan et al. [1] to incorporate numerical data. The proposed method guarantees data privacy, output privacy, and query privacy. We preserve the security through encryption and garbled circuits. For a query of 100 single-nucleotide polymorphism (SNPs) sequence, we achieve query execution time approximately 3.5 minutes in a database of 1500 records. To the best of our knowledge, this is the first proposed secure framework that addresses heterogeneous biomedical data including numeric attributes.

The advance of smart eHealthcare and cloud computing techniques has propelled an increasing number of healthcare centers to outsource their healthcare data to the cloud. Meanwhile, in order to preserve the privacy of the sensitive information, healthcare centers tend to encrypt the data before outsourcing them to the cloud. Although the data encryption technique can preserve the privacy of the data, it inevitably hinders the query functionalities over the outsourced data. Among all practical query functionalities, the similarity range query is one of the most popular ones. However, to our best knowledge, many existing studies on the similarity range query over outsourced data still suffer from the efficiency issue in the query process. Therefore, in this paper, aiming at improving the query efficiency, we propose an efficient privacy-preserving similarity range query scheme based on the precomputed distance technique. In specific, we first introduce a pre-computed distance based similarity range query (PreDSQ) algorithm, which can improve the query efficiency by precomputing some distances. Then, we propose our privacy-preserving similarity query scheme by applying an asymmetric scalar-product-preserving encryption technique to preserve the privacy of the PreDSQ algorithm. Both security analysis and performance evaluation are conducted, and the results show that our proposed scheme is efficient and can well preserve the privacy of data records and query requests.

With the increasing popularity of geo-positioning technologies and mobile Internet, spatial keyword data services have attracted growing interest from both the industrial and academic communities in recent years. Meanwhile, a massive amount of data is increasingly being outsourced to cloud in the encrypted form for enjoying the advantages of cloud computing while without compromising data privacy. Most existing works primarily focus on the privacy-preserving schemes for either spatial or keyword queries, and they cannot be directly applied to solve the spatial keyword query problem over encrypted data. In this paper, we study the challenging problem of Privacy-preserving Boolean Range Query (PBRQ) over encrypted spatial databases. In particular, we propose two novel PBRQ schemes. Firstly, we present a scheme with linear search complexity based on the space-filling curve code and Symmetric-key Hidden Vector Encryption (SHVE). Then, we use tree structures to achieve faster-than-linear search complexity. Thorough security analysis shows that data security and query privacy can be guaranteed during the query process. Experimental results using real-world datasets show that the proposed schemes are efficient and feasible for practical applications, which is at least ×70 faster than existing techniques in the literature.

The popularity of cloud storage among data users is due to easy maintenance, and no initial infrastructure setup cost as compared to local storage. However, although the data users outsource their data to cloud storage (a third party) still, they concern about their physical data. To check whether the data stored in the cloud storage has been modified or not, public auditing of the data is required before its utilization. To audit over vast outsourced data, the availability of the auditor is an essential requirement as nowadays, data owners are using mobile devices. But unfortunately, a single auditor leads to a single point of failure and inefficient to preserve the security and correctness of outsourced data. So, we introduce a distributed public auditing scheme which is based on peer-to-peer (P2P) architecture. In this work, the auditors are organized using a distributed hash table (DHT) mechanism and audit the outsourced data with the help of a published hashed key of the data. The computation and communication overhead of our proposed scheme is compared with the existing schemes, and it found to be an effective solution for public auditing on outsourced data with no single point of failure.

With the rapid development of internet technology, informatization and digitalization have penetrated into every link of human social life. A large amount of sensitive data has been accumulated and is still being generated within the enterprise. These sensitive data runs through the daily operation of enterprises and is widely used in business analysis, development and testing, and even some outsourcing business scenarios, which are increasing the possibility of sensitive data leakage and tampering. In fact, due to the improper use of data and the lack of protective measures and other reasons, data leakage events have happened again and again. Therefore, by introducing the concept of fuzzy set and using the membership function method, this paper proposes a desensitization technology framework for industrial data and a data desensitization algorithm based on fuzzy set, and verifies the desensitization effect and protective action on sensitive data of this algorithm through the test data desensitization experiment.

With the proliferation of cloud services, cloud-based image retrieval services enable large-scale image outsourcing and ubiquitous image searching. While enjoying the benefits of the cloud-based image retrieval services, critical privacy concerns may arise in such services since they may contain sensitive personal information. In this paper, we propose an efficient and Privacy-Preserving Image Retrieval scheme with Key Switching Technique (PPIRS). PPIRS utilizes the inner product encryption for measuring Euclidean distances between image feature vectors and query vectors in a privacy-preserving manner. Due to the high dimension of the image feature vectors and the large scale of the image databases, traditional secure Euclidean distance comparison methods provide insufficient search efficiency. To prune the search space of image retrieval, PPIRS tailors key switching technique (KST) for reducing the dimension of the encrypted image feature vectors and further achieves low communication overhead. Meanwhile, by introducing locality sensitive hashing (LSH), PPIRS builds efficient searchable indexes for image retrieval by organizing similar images into a bucket. Security analysis shows that the privacy of both outsourced images and queries are guaranteed. Extensive experiments on a real-world dataset demonstrate that PPIRS achieves efficient image retrieval in terms of computational cost.

In light of the progress achieved by the technology sector in the areas of internet speed and cloud services development, and in addition to other advantages provided by the cloud such as reliability and easy access from anywhere and anytime, most data owners find an opportunity to take advantage of the cloud to store data. However, data owners find a challenge that was and is still facing them in the field of outsourcing, which is protecting sensitive data from leakage. Researchers found that partitioning data into partitions, based on data sensitivity, can be used to protect data from leakage and to increase performance by storing the partition, which contains sensitive data in an encrypted form. In this paper, we review the methods used in designing partitions and dividing data approaches. A hybrid data partitioning approach is proposed to improve these techniques. We consider the frequency attack types used to guess the sensitive data and the most important properties that must be available in order for the encryption to be strong against frequency attacks.

Now a day’s most of the services are related to cloud and becoming more popular in using the services to tenants. Most importantly and famous service of cloud is Database as a Service (DaaS). This cloud service provides various resources as managing, using and administration such as software, hardware and tenants’ networks. The data and executing of queries in database are managed by the administrator from cloud service provider (CSP). Due to lack of trust on third party service provider the security and authentication issues are always facing by the tenants which is motivated us to write this paper. This paper shows the brief description about cryptographic algorithms, various types and query authentication on data. In the end the conclusion of the paper by proposing a new scheme that carry through the security and authentication of querying results of outsourcing cloud data.

A non-predicted and transient malfunctioning of one or multiple unmanned aerial vehicles (UAVs) is something that may happen over a course of their deployment. Therefore, it is very important to have means to detect these events and take actions for ensuring a high level of reliability, security, and safety of the flight for the predefined mission. In this research, we propose algorithms aiming at the detection and isolation of any faulty UAV so that the performance of the UAVs application is kept at its highest level. To this end, we propose the use of Kullback-Leiler Divergence (KLD) and Artificial Neural Network (ANN) to build algorithms that detect and isolate any faulty UAV. The proposed methods are declined in these two directions: (1) we compute a difference between the internal and external data, use KLD to compute dissimilarities, and detect the UAV that transmits erroneous measurements. (2) Then, we identify the faulty UAV using an ANN model to classify the sensed data using the internal sensed data. The proposed approaches are validated using a real dataset, provided by the Air Lab Failure and Anomaly (ALFA) for UAV fault detection research, and show promising performance.

This paper demonstrates that it is possible to execute sophisticated and powerful fault injection attacks on microcontrollers using low-cost equipment and readily available components. Earlier work had implied that powerful lasers and high grade optics frequently used to execute such attacks were being underutilized and that attacks were equally effective when using low-power settings and imprecise focus. This work has exploited these earlier findings to develop a low-cost laser workstation capable of generating multiple discrete faults with timing accuracy capable of targeting consecutive instruction cycles. We have shown that the capabilities of this new device exceed those of the expensive laboratory equipment typically used in related work. We describe a simplified fault model to categorize the effects of induced errors on running code and use it, along with the new device, to reevaluate the efficacy of different defensive coding techniques. This has enabled us to demonstrate an efficient hybrid defense that outperforms the individual defenses on our chosen target. This approach enables device programmers to select an appropriate compromise between the extremes of undefended code and unusable overdefended code, to do so specifically for their chosen device and without the need for prohibitively expensive equipment. This work has particular relevance in the burgeoning IoT world where many small companies with limited budgets are deploying low-cost microprocessors in ever more security sensitive roles.

The industrial Internet of Things (IIoT) can facilitate industrial upgrading, intelligent manufacturing, and lean production. Industrial control system (ICS) is a vital support mechanism for many key infrastructures in the IIoT. However, natural defects in the ICS network security mechanism and the susceptibility of the programmable logic controller (PLC) program to malicious attack pose a threat to the safety of national infrastructure equipment. To improve the security of the underlying equipment in ICS, a model checking method based on timed automata is proposed in this work, which can effectively model the control process and accurately simulate the system state when incorporating time factors. Formal analysis of the ICS and PLC is then conducted to formulate malware detection rules which can constrain the normal behavior of the system. The model checking tool UPPAAL is then used to verify the properties by detecting whether there is an exception in the system and determine the behavior of malware through counter-examples. The chemical reaction control system in Tennessee-Eastman process is taken as an example to carry out modeling, characterization, and verification, and can effectively detect multiple patterns of malware and propose relevant security policy recommendations.