Miracle-dz/UniFAR-FTUs huggingface dataset:
Protecting Access Privacy for Bitcoin Lightweight Client Using Trusted Hardware

Through decentralized ledger, Bitcoin rebuilds trust and achieves payment verification. However, the verification of Bitcoin transactions requires nodes to download the whole blockchain, which is infeasible for resource-constraint devices. Traditional schemes tried to transfer most of the storage and computation tasks from a lightweight client to a full node. Nevertheless, these schemes would severely violate users’ privacy since they would leak transaction ID and public-key address when a lightweight client queries the full node. To address this, we propose a privacy-preserving approach to secure queries of simplified payment verification (SPV) clients. Specifically, we design a d-differentially private mechanism based on trusted hardware such that semi-honest adversaries cannot acquire the real access pattern during shards retrieval phase. Data-oblivious primitives are adopted to prevent the internal search pattern leakage owing to side-channel attacks. Our scheme is more secure than traditional SPV schemes, and it is proved to satisfy d-differential privacy definition. Our prototype implementation with the Bitcoin blockchain dataset shows its practicality.
A High Accuracy Integrated Bagging-Fuzzy-GBDT Prediction Algorithm for Heart Disease Diagnosis

Associated with high morbidity and mortality, heart disease has become a severe threat to peoples health throughout the world. The recent development of Internet of Things (IoT) and machine learning in e-healthcare have contributed to the monitoring, prediction and diagnosis of heart disease. Particularly, the heart disease prediction can effectively facilitate disease prevention, diagnosis and timely treatment. However, traditional prediction models are weak in accuracy and generalization. In this paper, we propose a high accuracy integrated prediction algorithm for heart disease diagnosis. The fuzzy logic and Bootstrap Aggregating (Bagging) algorithm based on Gradient Boosting Decision Tree (GBDT) algorithm are combined to process heart disease data and generate multiple weak classifiers. At first, we integrate the fuzzy logic with GBDT to reduce the complexity of data. Moreover, we develop the Fuzzy-GBDT model integrated Bagging algorithm to avoid the interference of sensitive points and achieve partial parallelism. The simulation results show the proposed Fuzzy-Bagging-GBDT algorithm improves the accuracy and recall of heart disease prediction compared with GBDT.
Application Threat Modeling and Automated VNF Selection for Mitigation using TOSCA

In the era of Internet of Things (IoT) the interconnectedness of devices, and thus the need to protect them against threats increased. The widely used threat modeling method STRIDE can be used to identify the system's vulnerabilities and to determine appropriate mitigation solutions. In connected environments, especially the network layer plays a critical role in achieving security. Based on the Network Functions Virtualization (NFV) concept, network functions can be virtualized and provisioned on standard IT hardware. Virtualized Network Functions (VNFs) increase the flexibility of the provisioning, and thus security network functions, such as firewalls, can be easily deployed. However, in a complex distributed system it is time-consuming, error-prone, and for application architects even not possible to identify and provision the required security functions. For the orchestration and management of applications the TOSCA modeling language can be used to describe the application's components and their relations in a deployment model. The standard was mainly developed for cloud applications but was extended to the network layer. In this paper, we present a TOSCA-based approach for threat modeling based on STRIDE that facilitates the automated VNF selection and injection into TOSCA deployment models. The feasibility of our approach is validated by an extension of the TOSCA modeling tool Winery.
An Efficient Replay Method to Prevent SURF-Based Face Detection

During the past couple of years, a considerable number of digital images are gathered by search engines, or uploaded by users in different social websites. These images could contain a variety of variables such as different faces, scenes, and unusual objects. The advancement of digital face recognition algorithms is sensational, recognition algorithms used by some of the social websites are even stronger than most secret service establishments. However, there is always a concern about how to protect the user’s privacy when they do not want to be recognized automatically by these services. In this research, a SURF-based face recognition algorithm along with a featurebased attack to mislead this type of scheme is proposed. The recognition algorithm use different machine learning technique along with second layer descriptor counting function which makes it more powerful. However the primary focus of this research is to provide a method which can mislead this type of recognition systems with a smallest possible degradation to the image representation. The experimental results show that the proposed plan is successful to fool the SURF algorithm and its recognition system by 70%.
Defense Scheme to Protect IoT from Cyber Attacks using AI Principles

Even in its infancy, the internet of things (IoT) has enticed most of the modern industrial areas like smart cities, automobiles, medical technology. Since IoT connects everything together, it is vulnerable to a variety of devastating intrusion attacks. Being the internet of different devices makes it easy for attackers to launch their attacks. Thus, to combat all these attacks, an attack analysis is presented in this article using the basic principles of Artificial Neural Networks. Internet packet traces are used to train to the supervised ANN (Multilevel Perceptron) and evaluated after the training to decline the DDoS Attacks. This research article mainly focuses on the categorization of traffic patterns into legitimate traffic and attack traffic patterns in IoT network. The ANN processes are evaluated and tested in a simulated IoT network. The experimental results show a greater accuracy in detection of various DDoS attacks.
An Offloading Strategy Based on Cloud and Edge Computing for Industrial Internet

With the proliferation in the number of devices involved in Industrial Internet, it is becoming more and more difficult to meet simultaneously the requirements of industrial applications in terms of latency and economy only using traditional cloud computing paradigm. Edge computing paradigm can make up for this problem to a certain extent with constrained resources. In this paper, an integrated architecture is proposed, which can allow industrial devices to offload tasks to cloud or edge servers. Subsequently, an offloading strategy called ASO is also proposed based on this integrated architecture. The strategy, aiming at minimizing the energy consumption generated by industrial devices and cloud computing costs under the deadline constraints of industrial applications, consists of three sub-algorithms. First, a subdeadline is assigned to each task using a directed acyclic graph of the industrial application through subdeadline allocation algorithm. Then, tasks are topologically sorted to ensure the order of execution through topology sorting algorithm. Finally, a computing server is assigned to each task through task offloading algorithm. Experimental results demonstrate that compared with several baseline and heuristic algorithms, the proposed strategy can effectively reduce the energy consumption of industrial devices and cloud computing costs.
Clustering-Aid Sparse Representation Classification

Sparse Representation Classification (SRC) has attracted much attention due to its robustness in the fields of biometrics, such as face recognition, handwriting recognition. However, traditional SRC methods have to deal with a large amount of training samples, and the performance may also affected by the high correlation among training samples. In order to solve these problems, this paper proposes clustering-aided SRC methods. The first method performs feature selection and clustering to the original training samples before SRC operation, where the preprocessing reduces the correlation among samples. To further improve the classification performance, the second method divides the training samples into several groups, where the classes with samples sharing many similarities are assigned to different groups. Then, we perform SRC separately for each group, and fuse the results together to determine the final class label. Experiments show that when compared with the conventional SRC, the first proposed method significantly reduces the computation complexity without comprising the classification accuracy, and the second proposed method outperforms the first one.
Locating Bug IDs and Development Logs in Open Source Software (OSS) projects: An Experience Report

The development logs of software projects, contained in Version Control (VC) systems can be severely incomplete when tracking bugs, especially in open source projects, resulting in a reduced traceability of defects. Other times, such logs can contain bug information that is not available in bug tracking system (BT system) repositories, and vice-versa: if the development logs and bug related-data (i.e., BT system data) were used together, researchers and practitioners often would have a larger set of bug IDs for a software project, and a better picture of a bug life cycle, its evolution and maintenance.Considering a sample of 10 OSS projects and their development logs and BT systems data, the two objectives of this paper are (i) to determine which of the keywords ‘Fix’, ‘Bug’ or the ‘#’ identifier provide better precision; and (ii) to analyse their respective precision and recall at locating the larger amount possible of bug IDs manually.Overall, our results suggest that the use of the ‘#’ identifier in conjunction with the bug ID digits (e.g., #1234) is more precise for locating bugs in development logs, than the use of the ‘Bug’ and ‘Fix’ keywords. Such keywords are indeed present in the development logs, but they are less useful when trying to connect the development actions with the bug traces in software project.
Characterizations and Architectural Implications of NVM's External DRAM Cache

To meet the memory capacity requirement of big-data applications, a promising architecture is that NVM (Non-Volatile Memory) is used as main memory and external DRAM (off-chip) is used as cache. Compared to LLC versus DRAM (referred to as L-DRAM), external DRAM cache versus NVM main memory (referred to as E-NVM) are significantly different in terms of capacity and latency, thus traditional design cannot be directly borrowed without reconsideration. In this paper, we explore the characteristics and architectural implications of E-NVM in diverse dimensions including cache line granularity, associativity, replacement policy and prefetching methods. Lots of hints have been found for the optimal design of this emerging architecture.
DMF: A Dynamic Multi-Path Forwarding Strategy for Information Centric Networks

Information-Centric Networking (ICN) is a promising future network architecture where content instead of its location becomes the core of the communication model. Content is independent from location, which enables in-network caching and sharing. The content delivery is based on the content name instead of its address, and it is connectionless. An efficient forwarding strategy is significant for taking maximum advantage of multiple simultaneous connectivities. In this paper, we propose a Dynamic Multi-path Forwarding (DMF) strategy for ICN. DMF adopts RTT as metric at the initial phase and adopts the available bandwidth as metric at the saturated phase to make full use of multiple available transport paths and improve the receiving rate of the consumer. Simulation results show that DMF has better performance compared with existing multipath forwarding strategies proposed for ICN.
A Comprehensive Protection Method for Securing the Organization's Network Against Cyberattacks

Firewalls, and intrusion detection that are based on rule or anomaly detection can be used to protect the system against cyberattacks. This would help in detecting known attacks as well as unknown or zero type of attacks. However, the accuracy of such system depends on the list of known attacks, and the type of algorithms been implemented for the anomaly detection, as well as the attributes being analyzed by the algorithms. Different researches have been proposed to minimize the false report generated by the Anomaly Intrusion Detection (AID). But they couldn't adjust the behavior of AID in real time to reduce the faulty results. In this paper a comprehensive protection method is used to secure organizations against cyberattacks. This method used a Honeypot Intrusion Detection (HID) along with Anomaly Detection Controller (ADC), and prevention firewall to test the attacks at different level when accessing the network. The results from HID and ADC are used to update a Prevention Firewall (PF). The PF verifies these results to detect the false positive and false negative as well to detecting real at-tacks. This will help in controlling the traffic by blocking the detected attacks and updating the ADC to correct its behavior in order to reduce the false positive and false negative. This will lead to a better performance when dealing with future traffic coming from the reported faulty sources that were blocked by the ADC.
Location Information Based Beam Training for UAV mmWave System

Millimeter-wave (mmWave) communication utilizes very large bandwidth to provide high throughput. Unmanned Aerial Vehicle (UAV) can serve as a base station with high mobility and flexibility to extend the communication coverage. In this paper, we study UAV mmWave systems and focus on the beam training for UAV mmWave communication. We formulate the spectral efficiency maximization problem and obtain both optimal codebook size and codebook pattern. We also propose a beam training codebook design based on user distribution, which can be obtained empirically. Simulation results show that the spectral efficiency and downlink capacity of our proposed beam training outperform the existing methods.
Minimum Cost Task Assignment for Vehicle-Based Crowdsensing with Deterministic and Nondeterministic Trajectory

Vehicle-based crowdsensing (VCS) is a special case in crowdsourcing, and task assignment is a basic and important problem. In this paper, we investigate the minimum cost task assignment (MCTA) problem for vehicle-based crowdsensing. The VCS platform hopes to recruit some vehicles to complete given spatial-temporal tasks with the minimum cost. As vehicle trajectories are dynamic, we divide the MCTA problem into two sub-problems, the deterministic trajectory (D-MCTA) problem, and nondeterministic trajectory (N-MCTA) problem. For D-MCTA problem, we prove that the D-MCTA problem is NP-hard, and we propose a greedy algorithm to solve this problem. For N-MCTA problem, firstly, we determine the probability of each vehicle's trajectory through the logistic regression method. Then, we exploit the semi-Markov method to calculate the probability of each vehicle executing the task. Moreover, a greedy algorithm is proposed and the theoretical analysis is given. Finally, extensive simulations have been conducted to show the performance of our proposed algorithms is superior to the other algorithms.
Real-Time Trajectory Similarity Processing Using Longest Common Subsequence

Driven by the fast development of mobile internet and mobile devices, huge volumes of trajectory data describing the spatial-temporal information of moving objects are currently generated. Trajectory similarity processing is playing a critical role to explore valuable information, such as the patterns of human activities and behaviors. However, most of the existing works optimize this operation in an off-line manner, which is unable to provide timely feedback for time-sensitive applications. In this paper, we present a new real-time processing framework for trajectory similarity measurement, with practical algorithms to reduce the computing resource cost and guarantee the correctness of the result. Our proposal includes a cost-effective framework to ensure the correctness of trajectory similarity processing, a stream partition algorithm to maximize the processing throughput, a lightweight Top-k computing mode to cut off unnecessary network cost. Empirical studies on real-world stream applications validate the usefulness of our proposals and prove the huge advantage of our approaches over state-of-the-art solutions in the literature.
A Survey of Energy Efficient IoT Network in Cloud Environment

Internet of Things has been playing an increasingly important role in aspects of life such as healthcare, agriculture, home services, industrial process, and transportation. This technology has been applied in the cloud to enhance performance and reduce costs. Although using cloud computing with IoT has improved the overall system still faces some issues related to security, accuracy, energy efficiency, and big data. Power consumption is one of the main problems considered by many researchers in the field of IoT in a cloud environment. In this paper, we review recent research on energy efficient schemes for IoT in different cloud environments. Moreover, we investigate the results according to previous methods. In addition, we discuss shortcomings, limitation and possible opportunities which can be investigated in the future to improve these models.
Spectrum Sharing for High Altitude Platform Networks

High altitude platform (HAP) communications have attracted extensive attentions in both the academic and industry. The deployment of HAPs remains to be an ongoing challenge. In this paper, we propose a spatial distribution model for the performance analysis of HAP networks. In this model the HAPs are distributed on a sphere and the distribution of HAPs follows a homogeneous Poisson point process (PPP) on the sphere. Then we study the spectrum sharing between the HAP network and the ground network. Stochastic geometry is applied to analyze the performance of communications. The probabilities of successful communication of HAP network user and ground network user are calculated. Then, the optimal altitude of the HAP is obtained where the capacity of HAP network is optimized. The theoretical analysis results of this paper may motivate the study of deployment and spectrum sharing of HAP networks.
Reinforcement Learning-Based User Scheduling and Resource Allocation for Massive MU-MIMO System

User Scheduling and resource allocation in massive multi-user multiple-input multiple-output (MU-MIMO) systems can be regarded as a multi-objective optimization problem from the perspective of spatial domain and time-frequency domain. An appropriate scheduling scheme is crucial for the multiple users in MU-MIMO system to efficiently exploit spectrum resources and improve system sum rate. This paper proposes a user scheduling and resource allocation scheme based on reinforcement learning (RL), which has the decision making ability to decide whether a candidate user should be scheduled on a given resource block (RB). Specifically, we modeled the user scheduling problem in MU-MIMO system as a Markov Decision Process (MDP), characterizing complex problems with sequential decisions. Simulation results demonstrate that the proposed RL based scheduling and resource allocation scheme yields observable performance gains compared with other scheduling algorithms.
Human Palm Geometry Modelling for Biometric Security Systems

Palm print modelling and recognition systems have been extensively studied. Palm shape or palm geometry has had lesser attention paid to its study because of the difficulties associated with shape definitions and modelling. This paper reports on experimental determination of human palm geometry equations. Experimental determination of human palm geometry was undertaken using measurements of hands of 14 subjects drawn from a mixture of racial and gender backgrounds. By also analysing scanned images of their hands, characteristic measurements of their palms were determined. Characteristic expressions describing the geometry of human hands are proposed. The equations are based on measurements of various parts of the hand cross a broad spectrum of female and male representatives of various ethnic groups. They describe the relationships between the lengths of the hands and their perimeters at the fingertips and the base of the fingers. The relationships lead to a unique expression called the hand geometry equation.
An Energy-aware IoT Node for Sustainable Urban Sensing

This demo presents an energy-aware system designed for long-term deployments in urban sensing. It consists of a hardware-software co-design that allows measurement and harvesting of energy on class-1 IoT nodes. We focus on the self-management of energy and its integration with the RIOT operating system. The measurement module is composed of commodity components and designed for easy, flexible integration with various hardware platforms.
A Robust Method to Locate License Plates under Diverse Conditions

Automatic License Plate Detection (ALPD) is a crucial step, which significantly affects the recognition rate and speed of the Intelligent Transport System (ITS). This paper presents a robust license plate detection method using an intelligent combination of Faster R-CNN and image processing operations. In the proposed method, initially, a vehicle is detected in the input colored RGB images using the Faster R-CNN. Next, the image with detected vehicle is fed to our developed License Plate Localization Module (LPLM) to search the possible existence of the license plate. The LPLM converts the detected vehicle image from RGB to the HSV domain and applies color segmentation along with morphological operations, and finally uses the dimensions analysis to locate the license plate. Simulations on the challenging PKU dataset reveal that the proposed technique outperforms recent state-of-the-art methods in terms of detection accuracy, precision, recall, and execution time.
Reducing Redundant Control Messaging for Fast Response in SDN

The control mechanism of SDN tends to be distributed when the network scale expands. As the number of controllers grows, how to efficiently respond to network events in a timely fashion becomes challenging. Specifically, distributed controllers should reach a consensus on network states before making a network update decision in legacy consensus algorithm, which causes unexpected latency. While eventual correctness algorithms relies on Gossip schemes for fast recovery from network failures, leading to highly redundant controller responses when handling one single network event. In this paper, we aim at developing efficient control algorithm that eliminates unnecessary controller processing, facilitating rapid response to network events. To this end, we design a joint controller selection and message forwarding scheme, which identifies a subset of controllers for control message dissemination. An optimization problem is formulated with the objective of minimizing the total control overhead, which is found to be reducible to the K-center problem. Simulation results show that our method reduces up to 61% of the control overhead compared with legacy benchmarks.