Biblio

Although quite a few results on resilient consensus of multi-agent systems with malicious agents and fixed topology have been reported in the literature, we lack any known results on such a problem for multi-agent systems with time-varying topologies. Herein, we study the resilient consensus problem of time-varying networked systems in the presence of misbehaving nodes. A novel concept of joint ( r, s) -robustness is firstly proposed to characterize the robustness of the time-varying topologies. It is further revealed that the resilient consensus of multi-agent systems under F-total malicious network can be reached by the Weighted Mean-Subsequence-Reduced algorithm if and only if the time-varying graph is jointly ( F+1, F+1) -robust. Numerical simulations are finally performed to verify the effectiveness of the analytical results.

To avoid the spam message, malicious and cyber bullies activities which are mostly done by the fake profile. These activities challenge the privacy policies of the social network communities. These fake profiles are responsible for spread false information on social communities. To identify the fake profile, duplicate, spam and bots account there is much research work done in this area. By using a machine-learning algorithm, most of the fake accounts detected successfully. This paper represents the review of Fake Profile Detection on Social Site by Using Machine Learning.

Fast Growth of (ICT) Information and Communication Technology results to Innovation of Cloud Computing and is considered as a key driver for technological innovations, as an IT innovations, cloud computing had added a new dimension to that importance by increasing usage to technology that motivates economic development at the national and global levels. Continues need of higher storage space (applications, files, videos, music and others) are some of the reasons for adoption and implementation, Users and Enterprises are gradually changing the way and manner in which Data and Information are been stored. Storing/Retrieving Data and Information traditionally using Standalone Computers are no longer sustainable due to high cost of Peripheral Devices, This further recommends organizational innovative adoption with regards to approaches on how to effectively reduced cost in businesses. Cloud Computing provides a lot of prospects to users/organizations; it also exposes security concerns which leads to low adoption, implementation and usage. Therefore, the study will examine standard ways of improving cloud computing adoption, implementation and usage by proposing and developing a security model using a design methodology that will ensure a secured Cloud Computing and also identify areas where future regularization could be operational.

The special feature of the thing-user centric communication is that thing-users can form a society autonomously and collaborate to solve problems. To share experiences and knowledge, thing-users form, join, and leave communities. The thing-user, who needs a help from other thing-users to accomplish a mission, searches thing-user communities and nominates thing-users of the discovered communities to organize a collaborative work group. Thing-user community should perform autonomously the social construction process and need principles and procedures for the community formation and collaboration within the thing-user communities. This paper defines thing-user communities and proposes an organizational structure for the thing-user community formation.

Most financial markets prohibit unfair trades as they reduce efficiency and diminish the integrity of the market. Spoofers place orders they have no intention of trading in order to manipulate market prices and profit illegally. Most financial markets prohibit such spoofing orders; however, further clarification is still needed regarding how many orders a spoofer needs to place in order to manipulate market prices and profit. In this study I built an artificial market model (an agent-based model for financial markets) to show how unbalanced buy and sell orders affect the expected returns, and I implemented the spoofer agent in the model. I then investigated how many orders the spoofer needs to place in order to manipulate market prices and profit illegally. The results indicate that showing more spoofing orders than waiting orders in the order book enables the spoofer to earn illegally, amplifies price fluctuation, and reduces the efficiency of the market.

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 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.

The internal structure of the flexible-DC transformer is complicated and the lack of a reliable vibration calculation model limits the application of the vibration analysis method in the fault diagnosis of the flexible-DC transformer. In response to this problem, this paper analyzes the correlation between the vibration signals of multiple measuring points and establishes a ``black box'' model of transformer vibration detection. Using the correlation analysis of multiple measuring points and BP neural network, a ``black box'' model that simulates the internal vibration transmission relationship of the transformer is established. The vibration signal of the multiple measuring points can be used to calculate the vibration signal of the target measuring point under specific working conditions. This can provide effective information for fault diagnosis and judgment of the running status of the flexible-DC transformer.

Radio frequency identification (RFID) is widespread and still necessary in many important applications. However, and in various significant cases, the use of this technology faces multiple security issues that must be addressed. This is mainly related to the use of RFID tags (transponders) which are electronic components communicating wirelessly, and hence they are vulnerable to multiple attacks through several means. In this work, an extensive fault analysis is performed on a tag architecture in order to evaluate its hardness. Tens of millions of single-bit upset (SBU) and multiple-bit upset (MBU) faults are emulated randomly on this tag architecture using an FPGA-based emulation platform. The emulated faults are classified under five groups according to faults effect on the tag behaviour. The obtained results show the faults effect variation in function of the number of MBU affected bits. The interpretation of this variation allows evaluating the tag robustness. The proposed approach represents an efficient mean that permits to study tag architectures at the design level and evaluating their robustness and vulnerability to fault attacks.

Fault attacks consist in changing the program behavior by injecting faults at run-time, either at hardware or at software level. Their goal is to change the correct progress of the algorithm and hence, either to allow gaining some privilege access or to allow retrieving some secret information based on an analysis of the deviation of the corrupted behavior with respect to the original one. Countermeasures have been proposed to protect embedded systems by adding spatial, temporal or information redundancy at hardware or software level. First we define Countermeasures Check Point (CCP) and CCPs-based countermeasures as an important subclass of countermeasures. Then we propose a methodology to generate an optimal protection scheme for CCPs-based countermeasure. Finally we evaluate our work on a benchmark of code examples with respect to several Control Flow Integrity (CFI) oriented existing protection schemes.

For classical fault analysis, a transient fault is required to be injected during runtime, e.g., only at a specific round. Instead, Persistent Fault Analysis (PFA) introduces a powerful class of fault attacks that allows for a fault to be present throughout the whole execution. One limitation of original PFA as introduced by Zhang et al. at CHES'18 is that the adversary needs know (or brute-force) the faulty values prior to the analysis. While this was addressed at a follow-up work at CHES'20, the solution is only applicable to a single faulty value. Instead, we use the potency of Statistical Fault Analysis (SFA) in the persistent fault setting, presenting Statistical Persistent Fault Analysis (SPFA) as a more general approach of PFA. As a result, any or even a multitude of unknown faults that cause an exploitable bias in the targeted round can be used to recover the cipher's secret key. Indeed, the undesired faults in the other rounds that occur due the persistent nature of the attack converge to a uniform distribution as required by SFA. We verify the effectiveness of our attack against LED and AES.

The advanced complex electronic systems increasingly demand safer and more secure hardware parts. Correspondingly, fault injection became a major verification milestone for both safety- and security-critical applications. However, fault injection campaigns for gate-level designs suffer from huge execution times. Therefore, designers need to apply early design evaluation techniques to reduce the execution time of fault injection campaigns. In this work, we propose a method to represent gate-level Single-Event Transient (SET) faults by multiple Single-Event Upset (SEU) faults at the Register-Transfer Level. Introduced approach is to identify true and false logic paths for each SET in the flip-flops' fan-in logic cones to obtain more accurate sets of flip-flops for multiple SEUs injections at RTL. Experimental results demonstrate the feasibility of the proposed method to successfully reduce the fault space and also its advantage with respect to state of the art. It was shown that the approach is able to reduce the fault space, and therefore the fault-injection effort, by up to tens to hundreds of times.