Biblio

In the current generation, road accidents and security problems increase dramatically worldwide in our day to day life. In order to overcome this, Vehicular Ad-hoc Network (VANETs) is considered as a key element of future Intelligent Transportation Systems (ITS). With the advancement in vehicular communications, the attacks have also increased, and such architecture is still exposed to many weaknesses which led to numerous security threats that must be addressed before VANET technology is practically and safely adopted. Distributed Denial of Service (DDoS) attack, replay attacks and Sybil attacks are the significant security threats that affect the communication and privacy in VANET. An algorithm to detect and prevent various kinds of security attacks in VANET communication has been designed and proposed in this work. An analysis has also been done by applying four protocols on an existing scenario of real traffic simulator using OpenStreetMap and the best suitable protocol has been selected for further application. The evaluation has been done using SUMO, NS3 and Java simulation environment. Simulation results and extensive performance analysis shows that our proposed Algorithm performs well in detecting and preventing the attacks in VANET communication.

To achieve a substantial reliability and safety level, it is imperative to provide electronic computing systems with appropriate mechanisms to tackle soft errors. This paper proposes a low-cost system-level soft error mitigation technique, which allocates the critical application function to a pool of specific general-purpose processor registers. Both the critical function and the register pool are automatically selected by a developed profiling tool. The proposed technique was validated through more than 320K fault injections considering a Linux kernel, different benchmarks and two multicore ARM processors. Results show that our technique significantly reduces the code size and performance overheads while providing reliability improvement, w.r.t. the Triple Modular Redundancy (TMR) technique.

Cloud computing has been envisioned as a next generation information technology (IT) paradigm. The risk of losing data stored with any untrustworthy service provider is the key barrier to widespread uptake of cloud computing. This paper proposes an algebraic signature based remote data possession checking (RDPC) scheme to verify the integrity of the data stored in the cloud. This scheme integrates forward error-correcting codes to enhance the data possession guarantee, which can recover the data when a small amount of file has been deleted. The scheme allows verification without the need for the auditor to compare against the original data, which reduces the communication complexity dramatically. The storage complexity of cloud user is reduced to several bytes' information. Extensive security analysis and simulation show that the proposed scheme is highly provably secure. Finally, experiment results reveal that the computation performance is effective, and bounded by disk I/O.

Nowadays the use of the Internet is growing; E-voting system has been used by different countries because it reduces the cost and the time which used to consumed by using traditional voting. When the voter wants to access the E-voting system through the web application, there are requirements such as a web browser and a server. The voter uses the web browser to reach to a centralized database. The use of a centralized database for the voting system has some security issues such as Data modification through the third party in the network due to the use of the central database system as well as the result of the voting is not shown in real-time. However, this paper aims to provide an E-voting system with high security by using blockchain. Blockchain provides a decentralized model that makes the network Reliable, safe, flexible, and able to support real-time services.

Supervisory Control and Data Acquisition (SCADA) systems have been a frequent target of cyberattacks in Industrial Control Systems (ICS). As such systems are a frequent target of highly motivated attackers, researchers often resort to intrusion detection through machine learning techniques to detect new kinds of threats. However, current research initiatives, in general, pursue higher detection accuracies, neglecting the detection of new kind of threats and their proposal detection scope. This paper proposes a novel, reliable host-based intrusion detection for SCADA systems through the Operating System (OS) diversity. Our proposal evaluates, at the OS level, the SCADA communication over time and, opportunistically, detects, and chooses the most appropriate OS to be used in intrusion detection for reliability purposes. Experiments, performed through a variety of SCADA OSs front-end, shows that OS diversity provides higher intrusion detection scope, improving detection accuracy by up to 8 new attack categories. Besides, our proposal can opportunistically detect the most reliable OS that should be used for the current environment behavior, improving by up to 8%, on average, the system accuracy when compared to a single OS approach, in the best case.

The acceleration of extending popularity of Online Social Networks (OSNs) thanks to various services with which they provide people, is inevitable. This is why in OSNs security as a way to protect private data of users to be abused by unauthoritative people has a vital role to play. Trust evaluation is the security approach that has been utilized since the advent of OSNs. Graph-based approaches are among the most popular methods for trust evaluation. However, graph-based models need to employ limitations in the search process of finding trusted paths. This contributes to a reduction in trust accuracy. In this investigation, a learning-based model which with no limitation is able to find reliable users of any target user, is proposed. Experimental results depict 12% improvement in trust accuracy compares to models based on the graph-based approach.

In recent times, an increasing amount of intelligent electronic devices (IEDs) are being deployed to make power systems more reliable and economical. While these technologies are necessary for realizing a cyber-physical infrastructure for future smart power grids, they also introduce new vulnerabilities in the grid to different cyber-attacks. Traditional methods such as state vector estimation (SVE) are not capable of identifying cyber-attacks while the geometric information is also injected as an attack vector. In this paper, a machine learning based smart grid attack identification method is proposed. The proposed method is carried out by first collecting smart grid power flow data for machine learning training purposes which is later used to classify the attacks. The performance of both the proposed SVM method and the traditional SVE method are validated on IEEE 14, 30, 39, 57 and 118 bus systems, and the performance regarding the scale of the power system is evaluated. The results show that the SVM-based method performs better than the SVE-based in attack identification over a much wider scale of power systems.

Source location privacy (SLP) protection ensures security of assets in monitoring wireless sensor networks (WSNs). Also, low end-to-end delay (EED) and high packet delivery ratio (PDR) guarantee high packet delivery reliability. Therefore, it is important to ensure high levels of SLP protection, low EED, and high PDR in mission-critical monitoring applications. Thus, this study proposes a new angle-based agent node routing protocol (APr) which is capable of achieving high levels of SLP protection, low EED, and high PDR. The proposed APr protocol employs multiple routing strategies to enable a dynamic agent node selection process and creation of obfuscating routing paths. Analysis results reveal that the APr protocol achieves high packet delivery reliability to outperform existing intermediate node-based protocols such as the AdrR and tree-based protocols such as the TbR. Furthermore, the APr protocol achieves significantly high levels of SLP protection to outperform the AdrR protocol.

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.

Cybersecurity of the supervisory control and data acquisition (SCADA) system, which is the key component of the cyber-physical systems (CPS), is facing big challenges and will affect the reliability of the smart grid. System reliability can be influenced by various cyber threats. In this paper, the reliability of the electric power system considering different cybersecurity issues in the SCADA system is analyzed by using Semi-Markov Process (SMP) and mean time-to-compromise (MTTC). External and insider attacks against the SCADA system are investigated with the SMP models and the results are compared. The system reliability is evaluated by reliability indexes including loss of load probability (LOLP) and expected energy not supplied (EENS) through Monte Carlo Simulations (MCS). The lurking threats of the cyberattacks are also analyzed in the study. Case studies were conducted on the IEEE Reliability Test System (RTS-96). The results show that with the increase of the MTTCs of the cyberattacks, the LOLP values decrease. When insider attacks are considered, both the LOLP and EENS values dramatically increase owing to the decreased MTTCs. The results provide insights into the establishment of the electric power system reliability enhancement strategies.

Software Quality Testing has always been a crucial part of the software development process and lately, there has been a rise in the usage of testing applications. While a well-planned and performed test, regardless of its nature - automated or manual, is a key factor when deciding on the results of the test, it is often not enough to give a more deep and thorough view of the whole process. That can be achieved with properly selected software metrics that can be used for proper risk assessment and evaluation of the development.This paper considers the most commonly used metrics when measuring a performed test and examines metrics that can be applied in the development process.

Learning-based approaches often outperform hand-coded algorithmic solutions for many problems in robotics. However, learning long-horizon tasks on real robot hardware can be intractable, and transferring a learned policy from simulation to reality is still extremely challenging. We present a novel approach to model-free reinforcement learning that can leverage existing sub-optimal solutions as an algorithmic prior during training and deployment. During training, our gated fusion approach enables the prior to guide the initial stages of exploration, increasing sample-efficiency and enabling learning from sparse long-horizon reward signals. Importantly, the policy can learn to improve beyond the performance of the sub-optimal prior since the prior's influence is annealed gradually. During deployment, the policy's uncertainty provides a reliable strategy for transferring a simulation-trained policy to the real world by falling back to the prior controller in uncertain states. We show the efficacy of our Multiplicative Controller Fusion approach on the task of robot navigation and demonstrate safe transfer from simulation to the real world without any fine-tuning. The code for this project is made publicly available at https://sites.google.com/view/mcf-nav/home.

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.

Most programming languages support foreign language interoperation that allows developers to integrate multiple modules implemented in different languages into a single multilingual program. While utilizing various features from multiple languages expands expressivity, differences in language semantics require developers to understand the semantics of multiple languages and their inter-operation. Because current compilers do not support compile-time checking for interoperation, they do not help developers avoid in-teroperation bugs. Similarly, active research on static analysis and bug detection has been focusing on programs written in a single language. In this paper, we propose a novel approach to analyze multilingual programs statically. Unlike existing approaches that extend a static analyzer for a host language to support analysis of foreign function calls, our approach extracts semantic summaries from programs written in guest languages using a modular analysis technique, and performs a whole-program analysis with the extracted semantic summaries. To show practicality of our approach, we design and implement a static analyzer for multilingual programs, which analyzes JNI interoperation between Java and C. Our empirical evaluation shows that the analyzer is scalable in that it can construct call graphs for large programs that use JNI interoperation, and useful in that it found 74 genuine interoperation bugs in real-world Android JNI applications.

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.

Energy security program which becomes the part of energy management must ensure the high reliability of the electric power transmission system so that the customer can be served very well. However, there are several problems that can hinder reliability achievement such as the long duration of fault recovery. On the other side, the prediction of fault recovery duration becomes a very challenging task. Because there are still few machine learning-based solution offer this paper proposes a machine learning-based strategy by using Naive-Bayes Classifier (NBC) and Support Vector Machine (SVM) in predicting the fault recovery duration class. The dataset contains 3398 rows of non-temporary-fault type records, six input features (Substation, Asset Type, Fault Category, Outage Start Time, Outage Day, and Outage Month) and single target feature (Fault Recovery Duration). According to the performance test result, those two methods reach around 97-99% of accuracy, average sensitivity, and average specificity. In addition, one of the advantages obtained in field of fault recovery prediction is increasing the accuracy of likelihood level calculation of the long fault recovery time risk.

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.

With the construction of State Grid informatization, professional data such as operation inspection, marketing, and regulation have gradually shifted from offline to online. In recent years, cyberspace security incidents have occurred frequently, and national and group cybersecurity threats have emerged. As the next-generation communication system, quantum security has to satisfy the security requirements. Also, it is especially important to build the fusion application of energy network quantum private communication technology and conventional network, and to form a safe and reliable quantum-level communication technology solution suitable for the power grid. In this paper, from the perspective of the multi-terminal quantum key application, combined with a mature electricity consumption information collection system, a handheld meter reading solution based on quantum private communication technology is proposed to effectively integrate the two and achieve technological upgrading. First, from the technical theory and application fields, the current situation of quantum private communication technology and its feasibility of combining with classical facilities are introduced and analyzed. Then, the hardware security module and handheld meter reading terminal equipment are taken as typical examples to design and realize quantum key shared storage, business security process application model; finally, based on the overall environment of quantum key distribution, the architecture design of multi-terminal quantum key application verification is implemented to verify the quantum key business application process.

Throughout the Department of Defense, there are ongoing efforts to increase cybersecurity and improve data transfer in unmanned robotic systems (UxS). This paper explores the performance of the Robot Operating System (ROS) 2, which is built with the Data Distribution Service (DDS) standard as a middleware. Based on how quality of service (QoS) parameters are defined in the robotic middleware interface, it is possible to implement strict delivery requirements to different nodes on a dynamic nodal network with multiple unmanned systems connected. Through this research, different scenarios with varying QoS settings were implemented and compared to baseline values to help illustrate the impact of latency and throughput on data flow. DDS security settings were also enabled to help understand the cost of overhead and performance when secured data is compared to plaintext baseline values. Our experiments were performed using a basic ROS 2 network consisting of two nodes (one publisher and one subscriber). Our experiments showed a measurable latency and throughput change between different QoS profiles and security settings. We analyze the trends and tradeoffs associated with varying QoS and security settings. This paper provides performance data points that can be used to help future researchers and developers make informative choices when using ROS 2 for UxS.

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.

Occurrence of faults in power systems is unavoidable but their timely recognition and location enhances the reliability and security of supply; thereby resulting in economic gain to consumers and power utility alike. Distribution Network (DN) is made smarter by the introduction of sensors and computers into the system. In this paper, detection and classification of faults in DN using Artificial Neural Network (ANN) is emphasized. This is achieved through the employment of Back Propagation Algorithm (BPA) of the Feed Forward Neural Network (FFNN) using three phase voltages and currents as inputs. The simulations were carried out using the MATLAB® 2017a. ANN with various hidden layers were analyzed and the results authenticate the effectiveness of the method.

In recent years, the demand for high reliability in industrial wireless communication has been increasing. To meet the strict requirement, many researchers have studied various bit interleaving coding schemes for long packet transmission in industrial wireless networks. Current research shows that the use of bit interleaving structure can improve the performance of the communication system for long packet transmission, but to improve reliability of industrial wireless communications by combining the bit interleaving and channel coding for short packets still requires further analysis. With this aim, bit interleaving structure is applied to convolution code coding scheme for short packet transmission in this paper. We prove that the use of interleaver fail to improve the reliability of data transmission under the circumstance of short packet transmission.

Fix pattern-based patch generation is a promising direction in Automated Program Repair (APR). Notably, it has been demonstrated to produce more acceptable and correct patches than the patches obtained with mutation operators through genetic programming. The performance of pattern-based APR systems, however, depends on the fix ingredients mined from fix changes in development histories. Unfortunately, collecting a reliable set of bug fixes in repositories can be challenging. In this paper, we propose to investigate the possibility in an APR scenario of leveraging code changes that address violations by static bug detection tools. To that end, we build the AVATAR APR system, which exploits fix patterns of static analysis violations as ingredients for patch generation. Evaluated on the Defects4J benchmark, we show that, assuming a perfect localization of faults, AVATAR can generate correct patches to fix 34/39 bugs. We further find that AVATAR yields performance metrics that are comparable to that of the closely-related approaches in the literature. While AVATAR outperforms many of the state-of-the-art pattern-based APR systems, it is mostly complementary to current approaches. Overall, our study highlights the relevance of static bug finding tools as indirect contributors of fix ingredients for addressing code defects identified with functional test cases.

With the transformation of connected vehicles into the Internet of Vehicles (IoV), the time is now ripe for paving the way for the next generation of connected vehicles with novel applications and innovative security measures. The connected vehicles are experiencing prenominal growth in the auto industry, but are still studded with many security and privacy vulnerabilities. Today's IoV applications are part of cyber physical communication systems that collect useful information from thousands of smart sensors associated with the connected vehicles. The technology advancement has paved the way for connected vehicles to share significant information among drivers, auto manufacturers, auto insurance companies and operational and maintenance service providers for various applications. The critical issues in engineering the IoV applications are effective to use of the available spectrum and effective allocation of good channels an opportunistic manner to establish connectivity among vehicles, and the effective utilization of the infrastructure under various traffic conditions. Security and privacy in information sharing are the main concerns in a connected vehicle communication network. Blockchain technology facilitates secured communication among users in a connected vehicles network. Originally, blockchain technology was developed and employed with the cryptocurrency. Bitcoin, to provide increased trust, reliability, and security among users based on peer-to-peer networks for transaction sharing. In this paper, we propose to integrate blockchain technology into ad hoc vehicular networking so that the vehicles can share network resources with increased trust, reliability, and security using distributed access control system and can benefit a wider scope of scalable IoV applications scenarios for decision making. The proposed architecture is the faithful environment for information sharing among connected vehicles. Blockchain technology allows multiple copies of data storage at the distribution cloud. Distributed access control system is significantly more secure than a traditional centralized system. This paper also describes how important of ad hoc vehicular networking in human life, possibilities in real-world implementation and its future trends. The ad hoc vehicular networking may become one of the most trendy networking concepts in the future that has the perspective to bring out much ease human beneficial and secured applications.

Database outsourcing is one of the important utilities in cloud computing in which the Information Proprietor (IP) transfers the database administration to the Cloud Service Provider (CSP) in order to minimize the administration cost and preservation expenses of the database. Inspite of its immense profit, it undergoes few security issues such as privacy of deployed database and provability of search results. In the recent past, few of the studies have been carried out on provability of search results of Outsourced Database (ODB) that affords correctness and completeness of search results. But in the existing schemes, since there is flow of data between the Information Proprietor and the clients frequently, huge communication cost prevails at the Information Proprietor side. To address this challenge, in this paper we propose Efficient Auditing for Outsourced Database with Token Enforced Cloud Storage (EAODBT). The proposed scheme reduces the large communication cost prevailing at the Information Proprietor side and achieves correctness and completeness of search results even if the mischievous CSP knowingly sends a null set. Experimental analysis show that the proposed scheme has totally reduced the huge communication cost prevailing between Information Proprietor and clients, and simultaneously achieves the correctness and completeness of search results.