Biblio

Due to geographical locations of Indonesia, some technology such as hydro and solar photovoltaics are very attractive to be used and developed. Distribution Energy Resources (DER) is the appropriate schemes implemented to achieve optimal operation respecting the location and capacity of the plant. The Gorontalo sub-system network was chosen as a case study considering both of micro-hydro and PV as contributed to supply the grid. The needs of a smart electrical system are required to improve reliability, power quality, and adaptation to any circumstances during DER application. While the topology was changing over time, intermittent of DER output and bidirectional power flow can be overcome with smart grid systems. In this study, an automation algorithm has been conducted to aid the engineers in solving the protection problems caused by DER implementation. The Protection Security Assessment (PSA) method is used to evaluate the state of the protection system. Determine the relay settings using an adaptive rule-based method on expert systems. The application with a Graphical User Interface (GUI) has been developed to make user easier to get the specific relay settings and locations which are sensitive, fast, reliable, and selective.

The computation speed of computer systems is getting faster and the memory has been enhanced in performance and density through process scaling. However, due to the process scaling, DRAMs are recently suffering from numerous inherent faults. DRAM vendors suggest In-DRAM Error Correcting Code (IECC) to cope with the unreliable operation. However, the conventional IECC schemes have concerns about miscorrection and performance degradation. This paper proposes a pin-aligned In-DRAM ECC architecture using the expandability of a Reed-Solomon code (PAIR), that aligns ECC codewords with DQ pin lines (data passage of DRAM). PAIR is specialized in managing widely distributed inherent faults without the performance degradation, and its correction capability is sufficient to correct burst errors as well. The experimental results analyzed with the latest DRAM model show that the proposed architecture achieves up to 106 times higher reliability than XED with 14% performance improvement, and 10 times higher reliability than DUO with a similar performance, on average.

This paper introduces an efficient reactive routing protocol considering the mobility and the reliability of a node in Cognitive Radio Sensor Networks (CRSNs). The proposed protocol accommodates the dynamic behavior of the spectrum availability and selects a stable transmission path from a source node to the destination. Outlined as a weighted graph problem, the proposed protocol measures the weight for an edge the measuring the mobility patterns of the nodes and channel availability. Furthermore, the mobility pattern of a node is defined in the proposed routing protocol from the viewpoint of distance, speed, direction, and node's reliability. Besides, the spectrum awareness in the proposed protocol is measured over the number of shared common channels and the channel quality. It is anticipated that the proposed protocol shows efficient routing performance by selecting stable and secured paths from source to destination. Simulation is carried out to assess the performance of the protocol where it is witnessed that the proposed routing protocol outperforms existing ones.

This paper studies physical-layer security over slow fading channels, considering the impact of finite-blocklength secrecy coding. A comprehensive analysis and optimization framework is established to investigate the secrecy throughput (ST) of a legitimate user pair coexisting with an eavesdropper. Specifically, we devise both adaptive and non-adaptive optimization schemes to maximize the ST, where we derive optimal parameters including the transmission policy, blocklength, and code rates based on the instantaneous and statistical channel state information of the legitimate pair, respectively. Various important insights are provided. In particular, 1) increasing blocklength improves both reliability and secrecy with our transmission policy; 2) ST monotonically increases with blocklength; 3) ST initially increases and then decreases with secrecy rate, and there exists a critical secrecy rate that maximizes the ST. Numerical results are presented to verify theoretical findings.

The construction of polar code refers to selecting K "most reliable polarizing channels" in N polarizing channels to WN(1)transmit information bits. For non-systematic polar code, Arikan proposed a method to measure the channel reliability for BEC channel, which is called Bhattacharyya Parameter method. The calculated complexity of this method is O(N) . In this paper, we find the complementarity of Bhattacharyya Parameter. According to the complementarity, the code construction under a certain channel condition can be quickly deduced from the complementary channel condition.

The current trend of IoT user is toward the use of services and data externally due to voluminous processing, which demands resourceful machines. Instead of relying on the cloud of poor connectivity or a limited bandwidth, the IoT user prefers to use a cloudlet-based fog computing. However, the choice of cloudlet is solely dependent on its trust and reliability. In practice, even though a cloudlet possesses a required trusted platform module (TPM), we argue that the presence of a TPM is not enough to make the cloudlet trustworthy as the TPM supports only the primitive security of the bootstrap. Besides uncertainty in security, other uncertain conditions of the network (e.g. network bandwidth, latency and expectation time to complete a service request for cloud-based services) may also prevail for the cloudlets. Therefore, in order to evaluate the trust value of multiple cloudlets under uncertainty, this paper broadly proposes the empirical process for evaluation of trust. This will be followed by a measure of trust-based reputation of cloudlets through computational intelligence such as fuzzy logic and ant colony optimization (ACO). In the process, fuzzy logic-based inference and membership evaluation of trust are presented. In addition, ACO and its pheromone communication across different colonies are being modeled with multiple cloudlets. Finally, a measure of affinity or popular trust and reputation of the cloudlets is also proposed. Together with the context of application under multiple cloudlets, the computationally intelligent approaches have been investigated in terms of performance. Hence the contribution is subjected towards building a trusted cloudlet-based fog platform.

Network adversaries, such as malicious transit autonomous systems (ASes), have been shown to be capable of partitioning the Bitcoin's peer-to-peer network via routing-level attacks; e.g., a network adversary exploits a BGP vulnerability and performs a prefix hijacking attack (viz. Apostolaki et al. [3]). Due to the nature of BGP operation, such a hijacking is globally observable and thus enables immediate detection of the attack and the identification of the perpetrator. In this paper, we present a stealthier attack, which we call the EREBUS attack, that partitions the Bitcoin network without any routing manipulations, which makes the attack undetectable to control-plane and even to data-plane detectors. The novel aspect of EREBUS is that it makes the adversary AS a natural man-in-the-middle network of all the peer connections of one or more targeted Bitcoin nodes by patiently influencing the targeted nodes' peering decision. We show that affecting the peering decision of a Bitcoin node, which is believed to be infeasible after a series of bug patches against the earlier Eclipse attack [29], is possible for the network adversary that can use abundant network address resources (e.g., spoofing millions of IP addresses in many other ASes) reliably for an extended period of time at a negligible cost. The EREBUS attack is readily available for large ASes, such as Tier-1 and large Tier-2 ASes, against the vast majority of 10K public Bitcoin nodes with only about 520 bit/s of attack traffic rate per targeted Bitcoin node and a modest (e.g., 5-6 weeks) attack execution period. The EREBUS attack can be mounted by nation-state adversaries who would be willing to execute sophisticated attack strategies patiently to compromise cryptocurrencies (e.g., control the consensus, take down a cryptocurrency, censor transactions). As the attack exploits the topological advantage of being a network adversary but not the specific vulnerabilities of Bitcoin core, no quick patches seem to be available. We discuss that some naive solutions (e.g., whitelisting, rate-limiting) are ineffective and third-party proxy solutions may worsen the Bitcoin's centralization problem. We provide some suggested modifications to the Bitcoin core and show that they effectively make the EREBUS attack significantly harder; yet, their non-trivial changes to the Bitcoin's network operation (e.g., peering dynamics, propagation delays) should be examined thoroughly before their wide deployment.

Reliability analysis of concurrent data based on Botnet modeling is conducted in this paper. At present, the detection methods for botnets are mainly focused on two aspects. The first type requires the monitoring of high-privilege systems, which will bring certain security risks to the terminal. The second type is to identify botnets by identifying spam or spam, which is not targeted. By introducing multi-dimensional permutation entropy, the impact of permutation entropy on the permutation entropy is calculated based on the data communicated between zombies, describing the complexity of the network traffic time series, and the clustering variance method can effectively solve the difficulty of the detection. This paper is organized based on the data complex structure analysis. The experimental results show acceptable performance.

Individualization of anonymous identities using artificial intelligence - enables innovative human-computer interaction through the personalization of communication which is, at the same time, individual and anonymous. This paper presents possible approach for individualization of anonymous identities in real time. It uses computer vision and artificial intelligence to automatically detect and recognize person's age group, gender, human body measures, proportions and other specific personal characteristics. Collected data constitutes the so-called person's biometric footprint and are linked to a unique (but still anonymous) identity that is recorded in the computer system, along with other information that make up the profile of the person. Identity anonymization can be achieved by appropriate asymmetric encryption of the biometric footprint (with no additional personal information being stored) and integrity can be ensured using blockchain technology. Data collected in this manner is GDPR compliant.

Botnets are one of the major threats on the Internet. They are used for malicious activities to compromise the basic network security goals, namely Confidentiality, Integrity, and Availability. For reliable botnet detection and defense, deep learning-based approaches were recently proposed. In this paper, four different deep learning models, namely Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), hybrid CNN-LSTM, and Multi-layer Perception (MLP) are applied for botnet detection and simulation studies are carried out using the CTU-13 botnet traffic dataset. We use several performance metrics such as accuracy, sensitivity, specificity, precision, and F1 score to evaluate the performance of each model on classifying both known and unknown (zero-day) botnet traffic patterns. The results show that our deep learning models can accurately and reliably detect both known and unknown botnet traffic, and show better performance than other deep learning models.

Intrusion Detection System is a well-known term in the domain of Network and Information Security. It's one of the important components of the Network and Information Security infrastructure. Host Intrusion Detection System (HIDS) helps to detect unauthorized use, abnormal and malicious activities on the host, whereas Network Intrusion Detection System (NIDS) helps to detect attacks and intrusion on networks. Various researchers are actively working on different approaches to improving the IDS performance and many improvements have been achieved. However, development in many other technologies and newly emerging techniques always opens the doors of opportunity to add a sharp edge to IDS and to make it more robust and reliable. This paper proposes the development of Distributed Intrusion Detection System (DIDS) using emerging and promising technologies like Blockchain upon a stable platform like cloud infrastructure.

The transmission line tower is affected by the surface subsidence in the mined out area of coal mine, which will appear the phenomenon of subsidence, inclination and even tower collapse, threatening the operation safety of the transmission line tower in the mined out area. Therefore, a Safety and Damage Assessment Method of Transmission Line Tower in Goaf Based on Artificial Intelligence is proposed. Firstly, the geometric model of the coal seam in the goaf and the structural reliability model of the transmission line tower are constructed to evaluate the safety. Then, the random forest algorithm in artificial intelligence is used to evaluate the damage of the tower, so as to take protective measures in time. Finally, a finite element simulation model of tower foundation interaction is built, and its safety (force) and damage identification are experimentally analyzed. The results show that the proposed method can ensure high accuracy of damage assessment and reliable judgment of transmission line tower safety within the allowable error.

Device-Free Localization and Tracking (DFLT) acts as a key component for the contactless awareness applications such as elderly care and home security. However, the random phase errors in WiFi signal and weak target echoes submerged in background clutter signals are mainly obstacles for current DFLT systems. In this paper, we propose the design and implementation of MuTrack, a multiparameter based DFLT system using commodity WiFi devices with a single link. Firstly, we select an antenna with maximum reliability index as the reference antenna for signal sanitization in which the conjugate operation removes the random phase errors. Secondly, we design a multi-dimensional parameters estimator and then refine path parameters by optimizing the complete data of path components. Finally, the Hungarian Kalman Filter based tracking method is proposed to derive accurate locations from low-resolution parameter estimates. We extensively validate the proposed system in typical indoor environment and these experimental results show that MuTrack can achieve high tracking accuracy with the mean error of 0.82 m using only a single link.

IoT devices such as Google Home and Amazon Echo provide great convenience to our lives. Many of these IoT devices collect data including Personal Identifiable Information such as names, phone numbers, and addresses and thus IoT security is important. However, conducting security analysis on IoT devices is challenging due to the variety, the volume of the devices, and the special skills required for hardware and software analysis. In this research, we create and demonstrate a dynamic analysis security testing infrastructure for capturing network traffic from IoT devices. The network traffic is automatically mirrored to a server for live traffic monitoring and offline data analysis. Using the dynamic analysis security testing infrastructure, we conduct extensive security analysis on network traffic from Google Home and Amazon Echo. Our testing results indicate that Google Home enforces tighter security controls than Amazon Echo while both Google and Amazon devices provide the desired security level to protect user data in general. The dynamic analysis security testing infrastructure presented in the paper can be utilized to conduct similar security analysis on any IoT devices.

Software-defined networks (SDN), through their programmability, significantly increase network resilience by enabling dynamic reconfiguration of network topologies in response to faults and potentially malicious attacks detected in real-time. Another key trend in network softwarization is cloud-native software, which, together with SDN, will be an integral part of the core of future 5G networks. In SDN, the control plane forms the "brain" of the software-defined network and is typically implemented as a set of distributed controller replicas to avoid a single point of failure. Distributed consensus algorithms are used to ensure agreement among the replicas on key data even in the presence of faults. Security is also a critical concern in ensuring that attackers cannot compromise the SDN control plane; byzantine fault tolerance algorithms can provide protection against compromised controller replicas. However, while reliability/availability and security form key attributes of resilience, they are typically modeled separately in SDN, without consideration of the potential impacts of their interaction. In this paper we present an initial framework for a model that unifies reliability, availability, and security considerations in distributed consensus. We examine – via simulation of our model – some impacts of the interaction between accidental faults and malicious attacks on SDN and suggest potential mitigations unique to cloud-native software.

Covert communication, i.e., communication with a low probability of detection (LPD), has attracted a huge body of work. Recent studies have concluded that the maximal covert coding rate of the discrete memoryless channels and the additive white Gaussian noise (AWGN) channels is diminishing with the blocklength: the maximum information nats that can be transmitted covertly and reliably over such channels is only on the order of the square root of the blocklength. In this paper, we study covert communication over multiple-input multiple-output (MIMO) AWGN channels. We derive the order-optimal scaling law of the number of covert nats when the maximal covert coding rate of MIMO AWGN channels is diminishing with the blocklength. Furthermore, we provide a comparative discussion for the case in which secrecy and energy undetectability constraints are combined.

This research used an Autonomous Security Robot (ASR) scenario to examine public reactions to a robot that possesses the authority and capability to inflict harm on a human. Individual differences in terms of personality and Perfect Automation Schema (PAS) were examined as predictors of trust in the ASR. Participants (N=316) from Amazon Mechanical Turk (MTurk) rated their trust of the ASR and desire to use ASRs in public and military contexts following a 2-minute video depicting the robot interacting with three research confederates. The video showed the robot using force against one of the three confederates with a non-lethal device. Results demonstrated that individual differences factors were related to trust and desired use of the ASR. Agreeableness and both facets of the PAS (high expectations and all-or-none beliefs) demonstrated unique associations with trust using multiple regression techniques. Agreeableness, intellect, and high expectations were uniquely related to desired use for both public and military domains. This study showed that individual differences influence trust and one's desired use of ASRs, demonstrating that societal reactions to ASRs may be subject to variation among individuals.

FinBots are chatbots built on automated decision technology, aimed to facilitate accessible banking and to support customers in making financial decisions. Chatbots are increasing in prevalence, sometimes even equipped to mimic human social rules, expectations and norms, decreasing the necessity for human-to-human interaction. As banks and financial advisory platforms move towards creating bots that enhance the current state of consumer trust and adoption rates, we investigated the effects of chatbot vignettes with and without socio-emotional features on intention to use the chatbot for financial support purposes. We conducted a between-subject online experiment with N = 410 participants. Participants in the control group were provided with a vignette describing a secure and reliable chatbot called XRO23, whereas participants in the experimental group were presented with a vignette describing a secure and reliable chatbot that is more human-like and named Emma. We found that Vignette Emma did not increase participants' trust levels nor lowered their privacy concerns even though it increased perception of social presence. However, we found that intention to use the presented chatbot for financial support was positively influenced by perceived humanness and trust in the bot. Participants were also more willing to share financially-sensitive information such as account number, sort code and payments information to XRO23 compared to Emma - revealing a preference for a technical and mechanical FinBot in information sharing. Overall, this research contributes to our understanding of the intention to use chatbots with different features as financial technology, in particular that socio-emotional support may not be favoured when designed independently of financial function.

Fully homomorphic encryption (FHE) was one of the most prominent research topics of the last ten years. And it is considered as a major cryptographic tool in a secure and reliable cloud computing environment. The reason behind that because it allows computations over encrypted data, without decrypting the original message. This paper developed a new symmetric (FHE) algorithm based on Enhanced Matrix Operation for Randomization and Encryption (EMORE) algorithm using a chaotic system. The proposed algorithm was considered a noise-free algorithm. It generates the ciphertext in a floating-point number's format, overcomes the problem of plaintext ring and modular arithmetic operation in EMORE by the hardness of a chaotic system, and provides another level of security in terms of randomness properties, sensitivity to the initial condition, and large key size (\textbackslashtextgreater2100) of a chaotic system. Besides that, the proposed algorithm provides the confidentiality and privacy of outsourced data computing through homomorphism property of it. By using both numerical and statistical tests, these tests proved that the proposed algorithm has positive randomness properties and provide secure and reliable encryption (through encryption-decryption time, key sensitivity, keyspace, and correlation). Finally, the simulation results show that the execution time of the proposed algorithm is faster about 7.85 times than the EMORE algorithm.

Underwater communication is vital for a variety of defence and scientific purposes. Current optical and sonar based carriers can deliver high capacity data rates, but their range and reliability is hampered by heavy propagation loss. A vertical Molecular Communication via Buoyancy (MCvB) channel is experimentally investigated here, where the dominant propagation force is buoyancy. Sequential puffs representing modulated symbols are injected and after the initial loss of momentum, the signal is driven by buoyancy forces which apply to both upwards and downwards channels. Coupled with the complex interaction of turbulent and viscous diffusion, we experimentally demonstrate that sequential symbols exhibit a Gaussian velocity spatial distribution. Our experimental results use Particle Image Velocimetry (PIV) to trace molecular clusters and infer statistical characteristics of their velocity profile. We believe our experimental paper's results can be the basis for long range underwater vertical communication between a deep sea vehicle and a surface buoy, establishing a covert and reliable delay-tolerant data link. The statistical distribution found in this paper is akin to the antenna pattern and the knowledge can be used to improve physical security.

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 idea behind collective perception is to improve vehicles' awareness about their surroundings. Every vehicle shares information describing its perceived environment by means of V2X communication. Similar to other information shared using V2X communication, collective perception information is potentially safety relevant, which means there is a need to assess the reliability and quality of received information before further processing. Transmitted information may have been forged by attackers or contain inconsistencies e.g. caused by malfunctions. This paper introduces a novel approach for estimating a belief that a pair of entities, e.g. two remote vehicles or the host vehicle and a remote vehicle, within a Vehicular ad hoc Network (VANET) are both trustworthy. The method updates the belief based on the consistency of the data that both entities provide. The evaluation shows that the proposed method is able to identify forged information.

The concept of social networking is integrated into Internet of things (IoT) to socialize smart objects by mimicking human behaviors, leading to a new paradigm of Social Internet of Things (SIoT). A crucial problem that needs to be solved is how to establish reliable relationships autonomously among objects, i.e., building trust. This paper focuses on exploring an efficient context-aware trustworthiness inference framework to address this issue. Based on the sociological and psychological principles of trust generation between human beings, the proposed framework divides trust into two types: familiarity trust and similarity trust. The familiarity trust can be calculated by direct trust and recommendation trust, while the similarity trust can be calculated based on external similarity trust and internal similarity trust. We subsequently present concrete methods for the calculation of different trust elements. In particular, we design a kernel-based nonlinear multivariate grey prediction model to predict the direct trust of a specific object, which acts as the core module of the entire framework. Besides, considering the fuzziness and uncertainty in the concept of trust, we introduce the fuzzy logic method to synthesize these trust elements. The experimental results verify the validity of the core module and the resistance to attacks of this framework.

The center-piece of this work is a software measurement physical unclonable function (PUF). It measures processor chip ALU silicon biometrics in a manner similar to all PUFs. Additionally, it composes the silicon measurement with the data-dependent delay of a particular program instruction in a way that is difficult to decompose through a mathematical model. This approach ensures that each software instruction is measured if computed. The SW-PUF measurements bind the execution of software to a specific processor with a corresponding certificate. This makes the SW-PUF a promising candidate for applications requiring Trusted Computing. For instance, it could measure the integrity of an execution path by generating a signature that is unique to the specific program execution path and the processor chip. We present an area and energy-efficient scheme based on the SW-PUF to provide a more robust root of trust for measurement than the existing trusted platform module (TPM). To explore the feasibility of the proposed design, the SW-PUF has been implemented in HSPICE using 45 nm technology and evaluated on the FPGA platform.

In this paper, according to the electricity business process including collecting and transmitting power information and sending control instructions, a coupling model of control-communication flow is built which is composed of three main matrices: control-communication, communication-communication, communication-control incidence matrices. Furthermore, the effective path change between two communication nodes is analyzed and a calculation method of connectivity probability for information network is proposed when considering a breakdown in communication links. Then, based on Bayesian conditional probability theory, the effect of the communication interruption on the energy Internet is analyzed and the metric matrix of controllability is given under communication congestion. Several cases are given in the final of paper to verify the effectiveness of the proposed method for calculating controllability matrix by considering different link interruption scenarios. This probability index can be regarded as a quantitative measure of the controllability of the power service based on the communication transmission instructions, which can be used in the power business decision-making in order to improve the control reliability of the energy Internet.