Classifying network abnormalities into faults and attacks in IoT-based cyber physical systems using machine learning

Cyber Physical Systems (CPS) integrate physical processes with electronic computing devices and digital communication channels. Their proper operation might be affected by two main sources of abnormality, security attacks and failures. The topics of fault diagnosis and security attack analysis in CPS have been studied extensively in a stand-alone manner. However, considering the co-existence of both sources of abnormality, faults and attacks, in a system and being able to differentiate among them, is an important and timely problem not yet addressed adequately. In this work, we study the internal communication environment of an Energy Aware Smart Home (EASH) system. More specifically, we formally define the problem of differentiating between component failures and network attacks in EASH, based on their effect on the communication behaviour. We formally show the correlation between such abnormality sources and provide a machine learning based framework for the differentiation problem. Our framework is evaluated using a simulation as well as a real-time testbed environment, demonstrating a promising accuracy in classification of over 85%. Based on the obtained experimental results, we also provide a detailed analysis on the considered classes and features used in the proposed approach, which can further improve the classification accuracy.     

Performance optimization for cloud computing systems in the microservice era: state-of-the-art and research opportunities

With the demand of agile development and management, cloud applications today are moving towards a more fine-grained microservice paradigm, where smaller and simpler functioning parts are combined for providing end-to-end services. In recent years, we have witnessed many research efforts that strive to optimize the performance of cloud computing system in this new era. This paper provides an overview of existing works on recent system performance optimization techniques and classify them based on their design focuses. We also identify open issues and challenges in this important research direction.     

Design of a multi_agent system for worm spreading_reduction

With the explosive growth of Internet applications, the threats of network worms against computer systems and network security are seriously increasing. Many recent researches concentrate on providing a propagation model and early warning. In fact, the defense against worms in a realistic environment is an open problem. In this work, we present WSRMAS (worm spreading_reduction multi_agent system) as a system that includes a worm defense mechanism to considerably reduce the rate at which hosts are infected. As WSRMAS needs a suitable infra-structure, its architecture was elaborated and an agent platform was designed and implemented to support WSRMAS functions. The proposed system was provided once with a centralized plan and second with a decentralized (distributed) plan. In both cases the system performance was evaluated. Also different communication capabilities using Knowledge Query Manipulation Language (KQML) were exploited to improve WSRMAS performance. The ratio between worm and anti-worm spreading was studied to investigate its influence on the defense efficiency. Taking into account that some machines may not deploy WSRMAS, consequently, the effectiveness of WSRMAS under different operational conditions has been studied.     

CONQUEST: A Coarse-Grained Algorithm for Constructing Summaries of Distributed Discrete Datasets

In this paper we present a coarse-grained parallel algorithm, CONQUEST, for constructing bounded-error summaries of high-dimensional binary attributed data in a distributed environment. Such summaries enable more expensive analysis techniques to be applied efficiently under constraints on computation, communication, and privacy with little loss in accuracy. While the discrete and high-dimensional nature of the dataset makes the problem difficult in its serial formulation, the loose-coupling of distributed servers hosting the data and the heterogeneity in network bandwidth present additional challenges. CONQUEST is based on a novel linear algebraic tool, PROXIMUS, which is shown to be highly effective on a serial platform. In contrast to traditional fine-grained parallel techniques that distribute the kernel operations, CONQUEST adopts a coarse-grained parallel formulation that relies on the principle of sampling to reduce communication overhead while maintaining high accuracy. Specifically, each individual site computes its local patterns independently. Various sites cooperate in dynamically orchestrated work groups to construct consensus patterns from these local patterns. Individual sites may then decide to continue their participation in the consensus or leave the group. Such parallel formulation implicitly resolves load-balancing and privacy issues while reducing communication volume significantly. Experimental results on an Intel Xeon cluster demonstrate that this strategy is capable of excellent performance in terms of compression time, ratio, and accuracy with respect to post-processing tasks.     

The smart surface network: A bus-based approach to dense sensing

We present the Smart Surface Network (SSN), a hardware and software platform designed for dense sensing. Sensor nodes connected to the SSN communicate using a serial bus integrated within a mountable physical surface. The hardware architecture and bus access and communication mechanisms are implemented in a self-stabilizing manner, providing robust handling of unannounced arrivals and departures of network devices. An associated API supports a peer-to-peer communication paradigm, providing access to the physical, data link, and application layers of the bus. In this paper, we describe the SSN hardware architecture and present the bus access and peer discovery algorithms. We also discuss the design of the API and describe experimental results characterizing the fairness of the bus algorithm, the efficiency of the peer discovery algorithm, and the performance of the SSN system under varying load conditions.     

Secure spread: an integrated architecture for secure group communication

Group communication systems are high-availability distributed systems providing reliable and ordered message delivery, as well as a membership service, to group-oriented applications. Many such systems are built using a distributed client-server architecture where a relatively small set of servers provide service to numerous clients. In this work, we show how group communication systems can be enhanced with security services without sacrificing robustness and performance. More specifically, we propose several integrated security architectures for distributed client-server group communication systems. In an integrated architecture, security services are implemented in servers, in contrast to a layered architecture, where the same services are implemented in clients. We discuss performance and accompanying trust issues of each proposed architecture and present experimental results that demonstrate the superior scalability of an integrated architecture.     

Hybrid concurrency control protocol for data sharing among heterogeneous blockchains

With the development of information technology and cloud computing, data sharing has become an important part of scientific research. In traditional data sharing, data is stored on a third-party storage platform, which causes the owner to lose control of the data. As a result, there are issues of intentional data leakage and tampering by third parties, and the private information contained in the data may lead to more significant issues. Furthermore, data is frequently maintained on multiple storage platforms, posing significant hurdles in terms of enlisting multiple parties to engage in data sharing while maintaining consistency. In this work, we propose a new architecture for applying blockchains to data sharing and achieve efficient and reliable data sharing among heterogeneous blockchains. We design a new data sharing transaction mechanism based on the system architecture to protect the security of the raw data and the processing process. We also design and implement a hybrid concurrency control protocol to overcome issues caused by the large differences in blockchain performance in our system and to improve the success rate of data sharing transactions. We took Ethereum and Hyperledger Fabric as examples to conduct cross-blockchain data sharing experiments. The results show that our system achieves data sharing across heterogeneous blockchains with reasonable performance and has high scalability.     

JADE implemented mobile multi-agent based,distributed information platform for pervasive health care monitoring

In this paper we aim to implement a highly distributed information infrastructure—MADIP by using Intelligent Agent paradigm, which is able to notify the responsible care-provider of abnormality automatically, offer distance medical advice, and perform continuous health monitoring for those who need it. To confront the issues of interoperability, scalability, and openness in heterogeneous e-health environments, a FIPA2000 standard compliant agent development platform—JADE (Java Agent DEvelopment Framework) was adopted for the design and implementation of the proposed intelligent multi-agent based MADIP system.     

An Asynchronous Protocol for Release Consistent Distributed Shared Memory Systems

Distributed shared memory (DSM) systems provide a simple programming paradigm for networks of workstations, which are gaining popularity due to their cost-effective high computing power. However, DSM systems usually exhibit poor performance due to the large communication delay between the nodes; and a lot of different memory consistency models have been proposed to mask the network delay. In this paper, we propose an asynchronous protocol for the release consistent memory model, which we call an Asynchronous Release Consistency (ARC) protocol. Unlike other protocols where the communication adheres to the synchronous request/receive paradigm, the ARC protocol is asynchronous, such that the necessary pages are broadcast before they are requested. Hence, the network delay can be reduced by proper prefetching of necessary pages. We have also compared the performance of the ARC protocol with the lazy release protocol by running standard benchmark programs; and the experimental results showed that the ARC protocol achieves a performance improvement of up to 29%.     

GSM移动通信系统无线信道拥塞问题研究

随着GSM移动通信的发展,用户对通信服务质量要求越来越高。GSM移动通信系统无线信道堵塞是其发展中面临一个严重问题,不仅严重影响网络服务质量,降低用户的体验,而且对移动网络运营商的声誉带来恶劣影响,因此解决无线信道拥塞成为运营商进行网络优化的重要工作。鉴于此,本文对GSM移动通信系统无线信道拥塞问题进行了探讨,以期对相关从业人员有所借鉴。     

Adaptive Weighted Flow Net Algorithm for Human Activity Recognition Using Depth Learned Features

Human Activity Recognition (HAR) from video data collections is the core application in vision tasks and has a variety of utilizations including object detection applications, video-based behavior monitoring, video classification, and indexing, patient monitoring, robotics, and behavior analysis. Although many techniques are available for HAR in video analysis tasks, most of them are not focusing on behavioral analysis. Hence, a new HAR system analysis the behavioral activity of a person based on the deep learning approach proposed in this work. The most essential aim of this work is to recognize the complex activities that are useful in many tasks that are based on object detection, modelling of individual frame characteristics, and communication among them. Moreover, this work focuses on finding out the human actions from various video resolutions, invariant human poses, and nearness of multi objects. First, we identify the key and essential frames of each activity using histogram differences. Secondly, Discrete Wavelet Transform (DWT) is used in this system to extract coefficients from the sequence of key-frames where the activity is localized in space. Finally, an Adaptive Weighted Flow Net (AWFN) algorithm is proposed in this work for effective video activity recognition. Moreover, the proposed algorithm has been evaluated by comparing it with the existing Visual Geometry Group (VGG-16) convolution neural networks for making performance comparisons. This work focuses on competent deep learning-based feature extraction to discriminate the activities for performing the classification accuracy. The proposed model has been evaluated with VGG-16 using a combination of regular UCF-101 activity datasets and also in very challenging Low-quality videos such as HMDB51. From these investigations, it is proved that the proposed AWFN approach gives higher detection accuracy of 96%. It is approximately 0.3% to 7.88% of higher accuracy than state-of-art methods.     

Developing an integrated platform to enable hardware-in-the-loop for synchronous VR,traffic simulation and sensor interactions

Hardware-in-the-loop (HIL) is a testing paradigm where physical sensors (e.g., monitoring sensors such as cameras and proximity sensors, and alarm sensors such as screaming traffic cones) are connected to a virtual test system that simulates reality (e.g., virtual work zone with simulated dangerous situations). This paradigm is well-suited for conducting user studies for work zone safety because virtual test systems can be implemented using virtual reality (VR), which allows for safe and realistic testing of a sensing system without putting workers in danger along with avoiding high upfront costs needed to generate physical research testbeds. However, when recreating physical work zones in VR, researchers face various challenges while representing traffic patterns in VR, such as the lack of bi-directional communication between traffic simulation platforms and user behaviors in VR, hardware compatibility and integration issues, and customization inflexibility during implementation. Researchers, who need to develop such platforms for research studies that involve high-risk exposure to participants, are in need of evaluating the options available for bringing together the components of such platforms. This study provides an overview of alternative ways to integrate components of platforms that enable hardware-in-the-loop for synchronous VR, traffic simulation, and sensor interactions to position researchers to make decisions based on the pros and cons of each alternative. This paper also presents the implementation of such an integrated platform that allows a two-way interface between traffic simulation and VR environments for work zone safety analysis. Outcomes of this work will lay out the steps in implementing the integrated and immersive platform to be used in work zone safety studies based on the guidance presented.     

A thermal-aware scheduling for multicore architectures

With the advance of technology, the power density (temperature) increases rapidly to threaten system performance, reliability, and even system safety. Development of a thermal management method to reduce thermal hotspots and distribute the temperature uniformly has become an important issue. Therefore, dynamic thermal management (DTM) has emerged as an effective technique to remedy these issues above. In this paper, we propose a proactive thermal management scheme on the Criticore platform developed by our team to avoid suffering high temperature of the system. The proposed approach can schedule threads to prevent the system from overheating with the aid of the thermal sensors and the Power Management Circuit (PMC) designed in the Criticore. Furthermore, a novel thread migration is also presented to increase the reliability of the system.     

SPEED: A parallel platform for solving and predicting the performance of PDEs on distributed systems

Distributed systems such as networks of workstations are becoming an increasingly viable alternative to traditional supercomputer systems for running complex scientific applications. A large number of these applications require solving sets of partial differential equations (PDEs). In this paper, we describe the implementation and performance of SPEED (Scalable Partial differential Equation Environment on Distributed systems), a parallel platform which provides an efficient solution for time-dependent PDEs. SPEED allows the inclusion of a wide range of parameters and programming aids. PVM is employed as the underlying message-passing system. The parallel implementation has been performed using two algorithms. The first algorithm is a two-phase scheme which uses the conventional technique of alternating phases of computation and communication. The second algorithm employs a pre-computation technique that allows overlapping of computation and communication. Both methods yield significant speedups. The pre-computation technique reduces the communication time between the workstations but incurs additional overhead in buffer management. Hence, if the saving in communication time is larger than the overhead, the pre-computation technique outperforms the two-phase algorithm. SPEED also provides a performance prediction methodology that can accurately predict the performance of a given application on the system before running the application. This methodology allows the user to tune various parameters in order to identify system bottlenecks and maximize the performance.     

基于BONeS的WCDMA性能分析仿真平台

随着计算机技术的迅速发展,离散事件仿真技术在分析通信网性能时得到越来越多的应用。基于Unix操作系统的BONeS DESIGNER软件,是一种设计、仿真和分析通信系统结构和协议的交互式软件,该文主要介绍了一种基于BONES而建立的WCDMA系统级仿真平台,并基于该平台研究了WCDMA系统的话音、WWW数据业务在ATM接入网传输情况下有关网络性能,对混合业务的时延、抖动、吞吐量等方面进行了仿真与分析。仿真结果表明,该系统级仿真平台对WCDMA系统的性能分析是有效的,有助于系统设计。     

Using collaborative computing technologies to enable the sharing and integration of simulation services for product design

Nowadays simulation is commonly used in engineering design for verifying design concepts before physical prototypes are produced. The simulation of complex products such as mechatronics in general involves a synergy of multiple traditional disciplinary areas and entails the collaborative work of a multidisciplinary team. A need thus arises for supporting the effective and efficient integration of subsystem models at simulation runtime and in a distributed environment. These models are generally created using different simulation tools and depend on the inputs from each other to perform numerical integration. As such, many issues need to be addressed, e.g. system modeling, the use of computing technologies, and the runtime interaction between models. In this paper, a service-oriented paradigm is presented which is underpinned by collaborative computing technologies to enable the provision of simulation models as services as well as the integration of these services for performing simulation tasks in product design. As well as the implementation of such a paradigm, a method for the interaction between models is in particular developed to achieve high accuracy for the simulation of design problems involving the solving of system equations. Preliminary evaluation work shows that the proposed paradigm underpinned by collaborative computing technologies is viable and have great potential in supporting collaborative simulation development in industry and the method for interaction control successfully achieves better accuracy compared with traditional methods.     

基于评论异常度的新浪微博谣言识别方法

以微博为代表的社交媒体在为公众提供信息共享平台的同时, 也为谣言提供了可乘之机.开展微博中谣言的识别和清理方法研究, 对维护社会的安全稳定有着重要的现实意义.本文针对新浪微博平台中谣言识别的问题, 提出了一种基于评论异常度的微博谣言识别方法.首先采用D-S理论实现微博评论异常度的计算方法; 然后利用评论异常度与微博的内容特征、传播特征、用户特征对微博进行抽象表示; 最后再利用SVM (Support vector machine)构建一个基于评论异常度的谣言识别模型, 实现对新浪微博中谣言微博的识别.实验表明, 本文提出的谣言识别模型对新浪微博中谣言识别具有较好的效果, 谣言微博识别的F1值达到了96.2 %, 相较于现有文献的最好结果提高了1.3 %.     

Computer‐aided macroergonomics for improved performance and safety

The U.S. presidential election of 2000 was characterized by alleged ergonomic problems associated with the design of voting ballots. However, this visible event actually exemplified the fact that many apparent ergonomic issues are, in fact, macroergonomic issues. That is, in such cases, there are larger systemic factors at work that interact with and contribute to human–machine interface issues. Further complicating system effectiveness is the notion that modern work systems are distributed and therefore require computer aiding to establish a communication pipeline through which decisions can flow and performance can be enhanced. A conceptual and theoretical framework is presented for understanding such computer‐aided work systems and a taxonomy and research examples for three types of computer‐aided work systems are also offered. It is concluded that the single operator–single machine paradigm is not obsolete and the focus remains ultimately on the ergonomic level, but relevant macroergonomic, sociotechnical system factors require consideration prior to optimizing this level for overall ergonomic success. © 2002 Wiley Periodicals, Inc.     

基于OPC技术的组态软件与Matlab的数据通信及其仿真平台研究

利用OPC（OLE for Process Control）技术,可以建立MATLAB与组态软件之间的通信。这样,就可以利用组态软件生成友好的交互式人机界面,而用MATLAB完成复杂的控制算法,弥补组态软件算法能力不强的弱点,发挥MATLAB的强大计算能力。通过对力控组态软件与MATLAB之间通信的研究,以达到对象模型和控制器的分离构建,为控制系统的理论研究和设计提供了有效的仿真平台。     

Spam filtering framework for multimodal mobile communication based on dendritic cell algorithm

With the continual growth of mobile devices, they become a universal portable platform for effective business and personal communication. They enable a plethora of textual communication modes including electronic mails, instant messaging, and short messaging services. A downside of such great technology is the alarming rate of spam messages that are not only annoying to end-users but raises security concerns as well. This paper presents an intelligent framework for filtering multimodal textual communication including emails and short messages. We explore a novel methodology for information fusion inspired by the human immune system and hybrid approaches of machines learning. We study a number of methods to extract and select more relevant features to reduce the complexity of the proposed model to suite mobile applications while preserving good performance. The proposed framework is intensively evaluated on a number of benchmark datasets with remarkable results achieved.