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Xiaojiang Du

Publications and source records attributed to Xiaojiang Du.

At least 37 records · Page 2Linked to original sources
arXiv2020

Performance Analysis and Optimization for the MAC Protocol in UAV-based IoT Network

Unmanned aerial vehicles (UAVs) have played an important role in air-ground integration network. Especially in Internet of Things (IoT) services, UAV equipped with communication equipments is widely adopted as a mobile base station (BS) for data collection from IoT devices on the ground. In this paper, we consider an air-ground network in which the UAV flies straightly to collect information from the IoT devices in a 2-D plane based on the CSMA/CA protocol. Due to UAV's continuous mobility, the communication durations of devices in different locations with UAV are not only time-limited, but also vary from each other. To analyze the throughput performance of uplink multiple access control (MAC) protocol, we propose a new analysis model to deal with the communications heterogeneity in the network. Firstly, we divide the devices in the coverage into different clusters according to their communication durations. Then, a quitting probability indicating the probability that a device quits the UAV's coverage at each time slot is clarified. A modified three-dimensional Markov chain model adopting the quitting probability and cluster division is developed for the performance analysis. Besides, we also propose a modified CSMA/CA protocol which fully considers the heterogeneity of the access time and adaptively allocates the time resource among the devices in different clusters. Finally, the effects of retry limit, initial contention window size, the density of the devices, UAVs speed and coverage area are discussed in the simulation section.

cs.IT↗
arXiv2020

Privacy-Preserving Machine Learning Training in Aggregation Scenarios

To develop Smart City, the growing popularity of Machine Learning (ML) that appreciates high-quality training datasets generated from diverse IoT devices raises natural questions about the privacy guarantees that can be provided in such settings. Privacy-preserving ML training in an aggregation scenario enables a model demander to securely train ML models with the sensitive IoT data gathered from personal IoT devices. Existing solutions are generally server-aided, cannot deal with the collusion threat between the servers or between the servers and data owners, and do not match the delicate environments of IoT. We propose a privacy-preserving ML training framework named Heda that consists of a library of building blocks based on partial homomorphic encryption (PHE) enabling constructing multiple privacy-preserving ML training protocols for the aggregation scenario without the assistance of untrusted servers and defending the security under collusion situations. Rigorous security analysis demonstrates the proposed protocols can protect the privacy of each participant in the honest-but-curious model and defend the security under most collusion situations. Extensive experiments validate the efficiency of Heda which achieves the privacy-preserving ML training without losing the model accuracy.

cs.CR↗
arXiv2020

POKs Based Secure and Energy-Efficient Access Control for Implantable Medical Devices

Implantable medical devices (IMDs), such as pacemakers, implanted cardiac defibrillators, and neurostimulators are medical devices implanted into patients' bodies for monitoring physiological signals and performing medical treatments. Many IMDs have built-in wireless communication modules to facilitate data collecting and device reprogramming by external programmers. The wireless communication brings significant conveniences for advanced applications such as real-time and remote monitoring but also introduces the risk of unauthorized wireless access. The absence of effective access control mechanisms exposes patients' life to cyber attacks. In this paper, we present a lightweight and universally applicable access control system for IMDs. By leveraging Physically Obfuscated Keys (POKs) as the hardware root of trust, provable security is achieved based on standard cryptographic primitives while attaining high energy efficiency. In addition, barrier-free IMD access under emergent situations is realized by utilizing the patient's biometrical information. We evaluate our proposed scheme through extensive security analysis and a prototype implementation, which demonstrates our work's superiority on security and energy efficiency.

cs.CR↗
arXiv2019

Towards Efficient Integration of Blockchain for IoT Security: The Case Study of IoT Remote Access

The booming Internet of Things (IoT) market has drawn tremendous interest from cyber attackers. The centralized cloud-based IoT service architecture has serious limitations in terms of security, availability, and scalability, and is subject to single points of failure (SPOF). Recently, accommodating IoT services on blockchains has become a trend for better security, privacy, and reliability. However, blockchain's shortcomings of high cost, low throughput, and long latency make it unsuitable for IoT applications. In this paper, we take a retrospection of existing blockchain-based IoT solutions and propose a framework for efficient blockchain and IoT integration. Following the framework, we design a novel blockchain-assisted decentralized IoT remote accessing system, RS-IoT, which has the advantage of defending IoT devices against zero-day attacks without relying on any trusted third-party. By introducing incentives and penalties enforced by smart contracts, our work enables "an economic approach" to thwarting the majority of attackers who aim to achieve monetary gains. Our work presents an example of how blockchain can be used to ensure the fairness of service trading in a decentralized environment and punish misbehaviors objectively. We show the security of RS-IoT via detailed security analyses. Finally, we demonstrate its scalability, efficiency, and usability through a proof-of-concept implementation on the Ethereum testnet blockchain.

cs.CR↗
arXiv2019

PFirewall: Semantics-Aware Customizable Data Flow Control for Home Automation Systems

Emerging Internet of Thing (IoT) platforms provide a convenient solution for integrating heterogeneous IoT devices and deploying home automation applications. However, serious privacy threats arise as device data now flow out to the IoT platforms, which may be subject to various attacks. We observe two privacy-unfriendly practices in emerging home automation systems: first, the majority of data flowed to the platform are superfluous in the sense that they do not trigger any home automation; second, home owners currently have nearly zero control over their data. We present PFirewall, a customizable data-flow control system to enhance user privacy. PFirewall analyzes the automation apps to extract their semantics, which are automatically transformed into data-minimization policies; these policies only send minimized data flows to the platform for app execution, such that the ability of attackers to infer user privacy is significantly impaired. In addition, PFirewall provides capabilities and interfaces for users to define and enforce customizable policies based on individual privacy preferences. PFirewall adopts an elegant man-in-the-middle design, transparently executing data minimization and user-defined policies to process raw data flows and mediating the processed data between IoT devices and the platform (via the hub), without requiring modifications of the platform or IoT devices. We implement PFirewall to work with two popular platforms: SmartThings and openHAB, and set up two real-world testbeds to evaluate its performance. The evaluation results show that PFirewall is very effective: it reduces IoT data sent to the platform by 97% and enforces user defined policies successfully.

cs.CR↗
arXiv2019

Market-Based Model in CR-WSN: A Q-Probabilistic Multi-agent Learning Approach

The ever-increasingly urban populations and their material demands have brought unprecedented burdens to cities. Smart cities leverage emerging technologies like the Internet of Things (IoT), Cognitive Radio Wireless Sensor Network (CR-WSN) to provide better QoE and QoS for all citizens. However, resource scarcity is an important challenge in CR-WSN. Generally, this problem is handled by auction theory or game theory. To make CR-WSN nodes intelligent and more autonomous in resource allocation, we propose a multi-agent reinforcement learning (MARL) algorithm to learn the optimal resource allocation strategy in the oligopoly market model. Firstly, we model a multi-agent scenario, in which the primary users (PUs) is the sellers and the secondary users (SUs) is the buyers. Then, we propose the Q-probabilistic multiagent learning (QPML) and apply it to allocate resources in the market. In the multi-agent interactive learning process, the PUs and SUs learn strategies to maximize their benefits and improve spectrum utilization. Experimental results show the efficiency of our QPML approach, which can also converge quickly.

cs.MA↗
arXiv2019

Self-Selective Correlation Ship Tracking Method for Smart Ocean System

In recent years, with the development of the marine industry, navigation environment becomes more complicated. Some artificial intelligence technologies, such as computer vision, can recognize, track and count the sailing ships to ensure the maritime security and facilitates the management for Smart Ocean System. Aiming at the scaling problem and boundary effect problem of traditional correlation filtering methods, we propose a self-selective correlation filtering method based on box regression (BRCF). The proposed method mainly include: 1) A self-selective model with negative samples mining method which effectively reduces the boundary effect in strengthening the classification ability of classifier at the same time; 2) A bounding box regression method combined with a key points matching method for the scale prediction, leading to a fast and efficient calculation. The experimental results show that the proposed method can effectively deal with the problem of ship size changes and background interference. The success rates and precisions were higher than Discriminative Scale Space Tracking (DSST) by over 8 percentage points on the marine traffic dataset of our laboratory. In terms of processing speed, the proposed method is higher than DSST by nearly 22 Frames Per Second (FPS).

cs.CV↗
arXiv2019

When Energy Trading meets Blockchain in Electrical Power System: The State of the Art

With the rapid growth of renewable energy resources, the energy trading began to shift from centralized to distributed manner. Blockchain, as a distributed public ledger technology, has been widely adopted to design new energy trading schemes. However, there are many challenging issues for blockchain-based energy trading, i.e., low efficiency, high transaction cost, security & privacy issues. To tackle with the above challenges, many solutions have been proposed. In this survey, the blockchain-based energy trading in electrical power system is thoroughly investigated. Firstly, the challenges in blockchain-based energy trading are identified. Then, the existing energy trading schemes are studied and classified into three categories based on their main focus: energy transaction, consensus mechanism, and system optimization. And each category is presented in detail. Although existing schemes can meet the specific energy trading requirements, there are still many unsolved problems. Finally, the discussion and future directions are given.

cs.CR↗
arXiv2019

Heterogeneous Coexistence of Cognitive Radio Networks in TV White Space

Wireless standards (e.g., IEEE 802.11af and 802.22) have been developed for enabling opportunistic access in TV white space (TVWS) using cognitive radio (CR) technology. When heterogeneous CR networks that are based on different wireless standards operate in the same TVWS, coexistence issues can potentially cause major problems. Enabling collaborative coexistence via direct coordination between heterogeneous CR networks is very challenging, due to incompatible MAC/PHY designs of coexisting networks, requirement of an over-the-air common control channel for inter-network communications, and time synchronization across devices from different networks. Moreover, such a coexistence scheme would require competing networks or service providers to exchange sensitive control information that may raise conflict of interest issues and customer privacy concerns. In this paper, we present an architecture for enabling collaborative coexistence of heterogeneous CR networks over TVWS, called Symbiotic Heterogeneous coexistence ARchitecturE (SHARE). Define "indirect coordination" first before using it. Because coexistence cannot avoid coordination By mimicking the symbiotic relationships between heterogeneous organisms in a stable ecosystem, SHARE establishes an indirect coordination mechanism between heterogeneous CR networks via a mediator system, which avoids the drawbacks of direct coordination. SHARE includes two spectrum sharing algorithms whose designs were inspired by well-known models and theories from theoretical ecology, viz, the interspecific competition model and the ideal free distribution model.

cs.NI↗
arXiv2019

Vulnerability Prediction Based on Weighted Software Network for Secure Software Building

To build a secure communications software, Vulnerability Prediction Models (VPMs) are used to predict vulnerable software modules in the software system before software security testing. At present many software security metrics have been proposed to design a VPM. In this paper, we predict vulnerable classes in a software system by establishing the system's weighted software network. The metrics are obtained from the nodes' attributes in the weighted software network. We design and implement a crawler tool to collect all public security vulnerabilities in Mozilla Firefox. Based on these data, the prediction model is trained and tested. The results show that the VPM based on weighted software network has a good performance in accuracy, precision, and recall. Compared to other studies, it shows that the performance of prediction has been improved greatly in Pr and Re.

cs.SE↗
arXiv2019

Super-Resolution of Brain MRI Images using Overcomplete Dictionaries and Nonlocal Similarity

Recently, the Magnetic Resonance Imaging (MRI) images have limited and unsatisfactory resolutions due to various constraints such as physical, technological and economic considerations. Super-resolution techniques can obtain high-resolution MRI images. The traditional methods obtained the resolution enhancement of brain MRI by interpolations, affecting the accuracy of the following diagnose process. The requirement for brain image quality is fast increasing. In this paper, we propose an image super-resolution (SR) method based on overcomplete dictionaries and inherent similarity of an image to recover the high-resolution (HR) image from a single low-resolution (LR) image. We explore the nonlocal similarity of the image to tentatively search for similar blocks in the whole image and present a joint reconstruction method based on compressive sensing (CS) and similarity constraints. The sparsity and self-similarity of the image blocks are taken as the constraints. The proposed method is summarized in the following steps. First, a dictionary classification method based on the measurement domain is presented. The image blocks are classified into smooth, texture and edge parts by analyzing their features in the measurement domain. Then, the corresponding dictionaries are trained using the classified image blocks. Equally important, in the reconstruction part, we use the CS reconstruction method to recover the HR brain MRI image, considering both nonlocal similarity and the sparsity of an image as the constraints. This method performs better both visually and quantitatively than some existing methods.

cs.CV↗
arXiv2019

A Low-overhead Kernel Object Monitoring Approach for Virtual Machine Introspection

Monitoring kernel object modification of virtual machine is widely used by virtual-machine-introspection-based security monitors to protect virtual machines in cloud computing, such as monitoring dentry objects to intercept file operations, etc. However, most of the current virtual machine monitors, such as KVM and Xen, only support page-level monitoring, because the Intel EPT technology can only monitor page privilege. If the out-of-virtual-machine security tools want to monitor some kernel objects, they need to intercept the operation of the whole memory page. Since there are some other objects stored in the monitored pages, the modification of them will also trigger the monitor. Therefore, page-level memory monitor usually introduces overhead to related kernel services of the target virtual machine. In this paper, we propose a low-overhead kernel object monitoring approach to reduce the overhead caused by page-level monitor. The core idea is to migrate the target kernel objects to a protected memory area and then to monitor the corresponding new memory pages. Since the new pages only contain the kernel objects to be monitored, other kernel objects will not trigger our monitor. Therefore, our monitor will not introduce runtime overhead to the related kernel service. The experimental results show that our system can monitor target kernel objects effectively only with very low overhead.

cs.CR↗
arXiv2019

Interest-Related Item Similarity Model Based on Multimodal Data for Top-N Recommendation

Nowadays, the recommendation systems are applied in the fields of e-commerce, video websites, social networking sites, etc., which bring great convenience to people's daily lives. The types of the information are diversified and abundant in recommendation systems, therefore the proportion of unstructured multimodal data like text, image and video is increasing. However, due to the representation gap between different modalities, it is intractable to effectively use unstructured multimodal data to improve the efficiency of recommendation systems. In this paper, we propose an end-to-end Multimodal Interest-Related Item Similarity model (Multimodal IRIS) to provide recommendations based on multimodal data source. Specifically, the Multimodal IRIS model consists of three modules, i.e., multimodal feature learning module, the Interest-Related Network (IRN) module and item similarity recommendation module. The multimodal feature learning module adds knowledge sharing unit among different modalities. Then IRN learn the interest relevance between target item and different historical items respectively. At last, the multimodal data feature learning, IRN and item similarity recommendation modules are unified into an integrated system to achieve performance enhancements and to accommodate the addition or absence of different modal data. Extensive experiments on real-world datasets show that, by dealing with the multimodal data which people may pay more attention to when selecting items, the proposed Multimodal IRIS significantly improves accuracy and interpretability on top-N recommendation task over the state-of-the-art methods.

cs.IR↗
arXiv2019

Achieving Trust-Based and Privacy-Preserving Customer Selection in Ubiquitous Computing

The recent proliferation of smart devices has given rise to ubiquitous computing, an emerging computing paradigm which allows anytime & anywhere computing possible. In such a ubiquitous computing environment, customers release different computing or sensing tasks, and people, also known as data processors, participate in these tasks and get paid for providing their idle computing and communication resources. Thus, how to select an appropriate and reliable customer while not disclosing processors' privacy has become an interesting problem. In this article, we present a trust-based and privacy-preserving customer selection scheme in ubiquitous computing, called TPCS, to enable potential processors select the customers with good reputation. The basic concept of TPCS is that each data processor holds a trust value, and the reputation score of the customer is calculated based on processors' trust values and feedbacks via a truth discovery process. To preserve processors' privacy, pseudonyms and Paillier cryptosystem are applied to conceal each processor's real identity. In addition, three authentication protocols are designed to ensure that only the valid data processors (i.e., the processors registering in the system, holding the truthful trust values, and joining the computing tasks) can pass the authentication. A comprehensive security analysis is conducted to prove that our proposed TPCS scheme is secure and can defend against several sophisticated attacks. Moreover, extensive simulations are conducted to demonstrate the correctness and effectiveness of the proposed scheme.

cs.CR↗
arXiv2019

Real Time Lateral Movement Detection based on Evidence Reasoning Network for Edge Computing Environment

Edge computing is providing higher class intelligent service and computing capabilities at the edge of the network. The aim is to ease the backhaul impacts and offer an improved user experience, however, the edge artificial intelligence exacerbates the security of the cloud computing environment due to the dissociation of data, access control and service stages. In order to prevent users from using the edge-cloud computing environment to carry out lateral movement attacks, we proposed a method named CloudSEC meaning real time lateral movement detection based on evidence reasoning network for the edge-cloud environment. The concept of vulnerability correlation is introduced. Based on the vulnerability knowledge and environmental information of the network system, the evidence reasoning network is constructed, and the lateral movement reasoning ability provided by the evidence reasoning network is used. CloudSEC realizes the reconfiguration of the efficient real-time attack process. The experiment shows that the results are complete and credible.

cs.CR↗
arXiv2019

SDN Controllers: Benchmarking & Performance Evaluation

Software Defined Networks offer flexible and intelligent network operations by splitting a traditional network into a centralized control plane and a programmable data plane. The intelligent control plane is responsible for providing flow paths to switches and optimizes network performance. The controller in the control plane is the fundamental element used for all operations of data plane management. Hence, the performance and capabilities of the controller itself are extremely important. Furthermore, the tools used to benchmark their performance must be accurate and effective in measuring different evaluation parameters. There are dozens of controller proposals available in existing literature. However, there is no quantitative comparative analysis for them. In this article, we present a comprehensive qualitative comparison of different SDN controllers, along with a quantitative analysis of their performance in different network scenarios. More specifically, we categorize and classify 34 controllers based on their capabilities, and present a qualitative comparison of their properties. We also discuss in-depth capabilities of benchmarking tools used for SDN controllers, along with best practices for quantitative controller evaluation. This work uses three benchmarking tools to compare nine controllers against multiple criteria. Finally, we discuss detailed research findings on the performance, benchmarking criteria, and evaluation testbeds for SDN controllers.

cs.NI↗
arXiv2019

A Privacy-Preserving Traffic Monitoring Scheme via Vehicular Crowdsourcing

The explosive growth of vehicle amount has given rise to a series of traffic problems, such as traffic congestion, road safety, and fuel waste. Collecting vehicles' speed information is an effective way to monitor the traffic condition and avoid vehicles being congested, which however may bring threats to vehicles' location and trajectory privacy. Motivated by the fact that traffic monitoring does not need to know each individual vehicle's speed and the average speed would be sufficient, we propose a privacy-preserving traffic monitoring (PPTM) scheme to aggregate vehicles' speeds at different locations. In PPTM, the roadside unit (RSU) collects vehicles' speed information at multiple road segments, and further cooperates with a service provider to calculate the average speed information for every road segment. To preserve vehicles' privacy, both homomorphic Paillier cryptosystem and super-increasing sequence are adopted. A comprehensive security analysis indicates that the proposed PPTM can preserve vehicles' identities, speeds, locations, and trajectories privacy from being disclosed. In addition, extensive simulations are conducted to validate the effectiveness and efficiency of the proposed PPTM scheme.

cs.CR↗