Showing papers on "The Internet published in 2019"

Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing

Abstract: With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet of Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. Driving by this trend, there is an urgent need to push the AI frontiers to the network edge so as to fully unleash the potential of the edge big data. To meet this demand, edge computing, an emerging paradigm that pushes computing tasks and services from the network core to the network edge, has been widely recognized as a promising solution. The resulted new interdiscipline, edge AI or edge intelligence (EI), is beginning to receive a tremendous amount of interest. However, research on EI is still in its infancy stage, and a dedicated venue for exchanging the recent advances of EI is highly desired by both the computer system and AI communities. To this end, we conduct a comprehensive survey of the recent research efforts on EI. Specifically, we first review the background and motivation for AI running at the network edge. We then provide an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning model toward training/inference at the network edge. Finally, we discuss future research opportunities on EI. We believe that this survey will elicit escalating attentions, stimulate fruitful discussions, and inspire further research ideas on EI.

How Many Ways Can We Define Online Learning? A Systematic Literature Review of Definitions of Online Learning (1988-2018).

Abstract: Online learning as a concept and as a keyword has consistently been a focus of education research for over two decades. In this paper, we present results from a systematic literature review for the...

Network Intrusion Detection for IoT Security Based on Learning Techniques

Abstract: Pervasive growth of Internet of Things (IoT) is visible across the globe. The 2016 Dyn cyberattack exposed the critical fault-lines among smart networks. Security of IoT has become a critical concern. The danger exposed by infested Internet-connected Things not only affects the security of IoT but also threatens the complete Internet eco-system which can possibly exploit the vulnerable Things (smart devices) deployed as botnets. Mirai malware compromised the video surveillance devices and paralyzed Internet via distributed denial of service attacks. In the recent past, security attack vectors have evolved bothways, in terms of complexity and diversity. Hence, to identify and prevent or detect novel attacks, it is important to analyze techniques in IoT context. This survey classifies the IoT security threats and challenges for IoT networks by evaluating existing defense techniques. Our main focus is on network intrusion detection systems (NIDSs); hence, this paper reviews existing NIDS implementation tools and datasets as well as free and open-source network sniffing software. Then, it surveys, analyzes, and compares state-of-the-art NIDS proposals in the IoT context in terms of architecture, detection methodologies, validation strategies, treated threats, and algorithm deployments. The review deals with both traditional and machine learning (ML) NIDS techniques and discusses future directions. In this survey, our focus is on IoT NIDS deployed via ML since learning algorithms have a good success rate in security and privacy. The survey provides a comprehensive review of NIDSs deploying different aspects of learning techniques for IoT, unlike other top surveys targeting the traditional systems. We believe that, this paper will be useful for academia and industry research, first, to identify IoT threats and challenges, second, to implement their own NIDS and finally to propose new smart techniques in IoT context considering IoT limitations. Moreover, the survey will enable security individuals differentiate IoT NIDS from traditional ones.

A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges

Abstract: In recent years, with the rapid development of current Internet and mobile communication technologies, the infrastructure, devices and resources in networking systems are becoming more complex and heterogeneous. In order to efficiently organize, manage, maintain and optimize networking systems, more intelligence needs to be deployed. However, due to the inherently distributed feature of traditional networks, machine learning techniques are hard to be applied and deployed to control and operate networks. Software defined networking (SDN) brings us new chances to provide intelligence inside the networks. The capabilities of SDN (e.g., logically centralized control, global view of the network, software-based traffic analysis, and dynamic updating of forwarding rules) make it easier to apply machine learning techniques. In this paper, we provide a comprehensive survey on the literature involving machine learning algorithms applied to SDN. First, the related works and background knowledge are introduced. Then, we present an overview of machine learning algorithms. In addition, we review how machine learning algorithms are applied in the realm of SDN, from the perspective of traffic classification, routing optimization, quality of service/quality of experience prediction, resource management and security. Finally, challenges and broader perspectives are discussed.

Data-driven science and engineering: machine learning, dynamical systems, and control

Abstract: What does it take to be a data scientist? According to various sources on the internet, one popular way is to learn Python and to take some massive open online courses at one of the relevant intern...

Interoperability in Internet of Things: Taxonomies and Open Challenges

Abstract: In the last few years, many smart objects found in the physical world are interconnected and communicate through the existing internet infrastructure which creates a global network infrastructure called the Internet of Things (IoT). Research has shown a substantial development of solutions for a wide range of devices and IoT platforms over the past 6-7 years. However, each solution provides its own IoT infrastructure, devices, APIs, and data formats leading to interoperability issues. Such interoperability issues are the consequence of many critical issues such as vendor lock-in, impossibility to develop IoT application exposing cross-platform, and/or cross-domain, difficulty in plugging non-interoperable IoT devices into different IoT platforms, and ultimately prevents the emergence of IoT technology at a large-scale. To enable seamless resource sharing between different IoT vendors, efforts by several academia, industry, and standardization bodies have emerged to help IoT interoperability, i.e., the ability for multiple IoT platforms from different vendors to work together. This paper performs a comprehensive survey on the state-of-the-art solutions for facilitating interoperability between different IoT platforms. Also, the key challenges in this topic is presented.

The first-level digital divide shifts from inequalities in physical access to inequalities in material access.

Abstract: For a long time, a common opinion among policy-makers was that the digital divide problem would be solved when a country’s Internet connection rate reaches saturation. However, scholars of the second-level digital divide have concluded that the divides in Internet skills and type of use continue to expand even after physical access is universal. This study—based on an online survey among a representative sample of the Dutch population—indicates that the first-level digital divide remains a problem in one of the richest and most technologically advanced countries in the world. By extending basic physical access combined with material access, the study finds that a diversity in access to devices and peripherals, device-related opportunities, and the ongoing expenses required to maintain the hardware, software, and subscriptions affect existing inequalities related to Internet skills, uses, and outcomes.

Machine learning based phishing detection from URLs

Abstract: Due to the rapid growth of the Internet, users change their preference from traditional shopping to the electronic commerce. Instead of bank/shop robbery, nowadays, criminals try to find their victims in the cyberspace with some specific tricks. By using the anonymous structure of the Internet, attackers set out new techniques, such as phishing, to deceive victims with the use of false websites to collect their sensitive information such as account IDs, usernames, passwords, etc. Understanding whether a web page is legitimate or phishing is a very challenging problem, due to its semantics-based attack structure, which mainly exploits the computer users’ vulnerabilities. Although software companies launch new anti-phishing products, which use blacklists, heuristics, visual and machine learning-based approaches, these products cannot prevent all of the phishing attacks. In this paper, a real-time anti-phishing system, which uses seven different classification algorithms and natural language processing (NLP) based features, is proposed. The system has the following distinguishing properties from other studies in the literature: language independence, use of a huge size of phishing and legitimate data, real-time execution, detection of new websites, independence from third-party services and use of feature-rich classifiers. For measuring the performance of the system, a new dataset is constructed, and the experimental results are tested on it. According to the experimental and comparative results from the implemented classification algorithms, Random Forest algorithm with only NLP based features gives the best performance with the 97.98% accuracy rate for detection of phishing URLs.

astroquery: An Astronomical Web-querying Package in Python

Abstract: Astroquery is a collection of tools for requesting data from databases hosted on remote servers with interfaces exposed on the Internet, including those with web pages but without formal application program interfaces. These tools are built on the Python requests package, which is used to make HTTP requests, and astropy, which provides most of the data parsing functionality. astroquery modules generally attempt to replicate the web page interface provided by a given service as closely as possible, making the transition from browser-based to command-line interaction easy. astroquery has received significant contributions from throughout the astronomical community, including several from telescope archives. astroquery enables the creation of fully reproducible workflows from data acquisition through publication. This paper describes the philosophy, basic structure, and development model of the astroquery package. The complete documentation for astroquery can be found at http://astroquery.readthedocs.io/.

Mobile Devices and Health.

Abstract: Mobile Devices and Health Mobile health involves sensors, mobile apps, social media, and location-tracking technology used in disease diagnosis, prevention, and management. This article provides an...

New IT Driven Service-Oriented Smart Manufacturing: Framework and Characteristics

Abstract: Recently, along with the wide application of new generation information technologies (New IT) in manufacturing, many countries issued their national advanced manufacturing development strategies, such as Industrial Internet, Industry 4.0, and Made in China 2025. One common aim of these strategies is to achieve smart manufacturing, which demands the interoperation, integration, and fusion of the physical world and the cyber world of manufacturing. As well, New IT [such as Internet of Things (IoT), cloud computing, big data, mobile Internet, and cyber-physical systems (CPS)] have played pivotal roles in promoting smart manufacturing. Data generated in the physical world can be sensed and transfered to the cyber world through IoT and the Internet, and be processed and analyzed by cloud computing, big data technologies to adjust the physical world. The physical world and the cyber world of manufacturing are integrated based on CPS. On the other hand, servitization has become a prominent trend in the manufacturing. Embracing the concept of “Manufacturing-as-a-Service,” manufacturing is provided as service for users. Because of the characteristics of interoperability and platform independence, services pave the way for large-scale smart applications and manufacturing collaboration. Combining New IT and services, this paper proposes a framework—New IT driven service-oriented smart manufacturing (SoSM). SoSM aims at facilitating the visions of smart manufacturing by making full use of New IT and services. Complementary to the framework of SoSM, the New IT driven typical characteristics of SoSM are also investigated and discussed, respectively.

An Edge-Based Architecture to Support Efficient Applications for Healthcare Industry 4.0

Abstract: Edge computing paradigm has attracted many interests in the last few years as a valid alternative to the standard cloud-based approaches to reduce the interaction timing and the huge amount of data coming from Internet of Things (IoT) devices toward the Internet. In the next future, Edge-based approaches will be essential to support time-dependent applications in the Industry 4.0 context; thus, the paper proposes BodyEdge , a novel architecture well suited for human-centric applications, in the context of the emerging healthcare industry. It consists of a tiny mobile client module and a performing edge gateway supporting multiradio and multitechnology communication to collect and locally process data coming from different scenarios; moreover, it also exploits the facilities made available from both private and public cloud platforms to guarantee a high flexibility, robustness, and adaptive service level. The advantages of the designed software platform have been evaluated in terms of reduced transmitted data and processing time through a real implementation on different hardware platforms. The conducted study also highlighted the network conditions (data load and processing delay) in which BodyEdge is a valid and inexpensive solution for healthcare application scenarios.

Blockchain-Based Internet of Vehicles: Distributed Network Architecture and Performance Analysis

Abstract: The rapid growth of Internet of Vehicles (IoV) has brought huge challenges for large data storage, intelligent management, and information security for the entire system. The traditional centralized management approach for IoV faces the difficulty in dealing with real-time response. The blockchain, as an effective technology for decentralized distributed storage and security management, has already showed great advantages in its application of Bitcoin. In this paper, we investigate how the blockchain technology could be extended to the application of vehicle networking, especially with the consideration of the distributed and secure storage of big data. We define several types of nodes such as vehicle and roadside for vehicle networks and form several sub-blockchain networks. In this paper, we present a model of the outward transmission of vehicle blockchain data, and then give detail theoretical analysis and numerical results. This paper has shown the potential to guide the application of blockchain for future vehicle networking.

Robust Malware Detection for Internet of (Battlefield) Things Devices Using Deep Eigenspace Learning

Abstract: Internet of Things (IoT) in military settings generally consists of a diverse range of Internet-connected devices and nodes (e.g., medical devices and wearable combat uniforms). These IoT devices and nodes are a valuable target for cyber criminals, particularly state-sponsored or nation state actors. A common attack vector is the use of malware. In this paper, we present a deep learning based method to detect Internet Of Battlefield Things (IoBT) malware via the device’s Operational Code (OpCode) sequence. We transmute OpCodes into a vector space and apply a deep Eigenspace learning approach to classify malicious and benign applications. We also demonstrate the robustness of our proposed approach in malware detection and its sustainability against junk code insertion attacks. Lastly, we make available our malware sample on Github, which hopefully will benefit future research efforts (e.g., to facilitate evaluation of future malware detection approaches).

Blockchain Technologies for the Internet of Things: Research Issues and Challenges

Abstract: This paper presents a comprehensive survey of the existing blockchain protocols for the Internet of Things (IoT) networks. We start by describing the blockchains and summarizing the existing surveys that deal with blockchain technologies. Then, we provide an overview of the application domains of blockchain technologies in IoT, e.g., Internet of Vehicles, Internet of Energy, Internet of Cloud, Edge computing, etc. Moreover, we provide a classification of threat models, which are considered by blockchain protocols in IoT networks, into five main categories, namely identity-based attacks, manipulation-based attacks, cryptanalytic attacks, reputation-based attacks, and service-based attacks. In addition, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward secure and privacy-preserving blockchain technologies with respect to the blockchain model, specific security goals, performance, limitations, computation complexity, and communication overhead. Based on the current survey, we highlight open research challenges and discuss possible future research directions in the blockchain technologies for IoT.

astroquery: An Astronomical Web-Querying Package in Python

Abstract: astroquery is a collection of tools for requesting data from databases hosted on remote servers with interfaces exposed on the internet, including those with web pages but without formal application program interfaces (APIs). These tools are built on the Python requests package, which is used to make HTTP requests, and astropy, which provides most of the data parsing functionality. astroquery modules generally attempt to replicate the web page interface provided by a given service as closely as possible, making the transition from browser-based to command-line interaction easy. astroquery has received significant contributions from throughout the astronomical community, including several significant contributions from telescope archives. astroquery enables the creation of fully reproducible workflows from data acquisition through publication. This paper describes the philosophy, basic structure, and development model of the astroquery package. The complete documentation for astroquery can be found at this http URL.

A Novel Attribute-Based Access Control Scheme Using Blockchain for IoT

Abstract: With the sharp increase in the number of intelligent devices, the Internet of Things (IoT) has gained more and more attention and rapid development in recent years. It effectively integrates the physical world with the Internet over existing network infrastructure to facilitate sharing data among intelligent devices. However, its complex and large-scale network structure brings new security risks and challenges to IoT systems. To ensure the security of data, traditional access control technologies are not suitable to be directly used for implementing access control in IoT systems because of their complicated access management and the lack of credibility due to centralization. In this paper, we proposed a novel attribute-based access control scheme for IoT systems, which simplifies greatly the access management. We use blockchain technology to record the distribution of attributes in order to avoid single point failure and data tampering. The access control process has also been optimized to meet the need for high efficiency and lightweight calculation for IoT devices. The security and performance analysis show that our scheme could effectively resist multiple attacks and be efficiently implemented in IoT systems.

A Secure Authentication Protocol for Internet of Vehicles

Abstract: An Internet of Vehicles (IoV) allows forming a self-organized network and broadcasting messages for the vehicles on roads. However, as the data are transmitted in an insecure network, it is essential to use an authentication mechanism to protect the privacy of vehicle users. Recently, Ying et al. proposed an authentication protocol for IoV and claimed that the protocol could resist various attacks. Unfortunately, we discovered that their protocol suffered from an offline identity guessing attack, location spoofing attack, and replay attack, and consumed a considerable amount of time for authentication. To resolve these shortcomings, we propose an improved protocol. In addition, we provide a formal proof to the proposed protocol to demonstrate that our protocol is indeed secure. Compared with previous methods, the proposed protocol performs better in terms of security and performance.

From internet access to internet skills: digital inequality among older adults

Abstract: Although much research examines the factors that affect technology adoption and use, less is known about how older adults as a group differ in their ability to use the Internet. The theory of digital inequality suggests that even once people have gone online, differences among them will persist in important ways such as their online skills. We analyze survey data about older American adults’ Internet skills to examine whether skills differ in this group and if they do, what explains differential online abilities. We find that there is considerable variation in Internet know-how and this relates to both socioeconomic status and autonomy of use. The results suggest that attempts to achieve a knowledgeable older adult population regarding Internet use must take into account these users’ socioeconomic background and available access points.

Internet of Mobile Things: Overview of LoRaWAN, DASH7, and NB-IoT in LPWANs Standards and Supported Mobility

Abstract: Low-power wide area networks (LPWANs) constitute a type of networks which is used to connect things to the Internet from a wide variety of sectors. These types of technologies provide the Internet of Things (IoT) devices with the ability to transmit few bytes of data for long ranges, taking into consideration minimum power consumption. In parallel, IoT applications will cover a wide range of human and life needs from smart environments (cities, home, transportation, etc.) to health and quality of life. Among these popular LPWANs technologies, we have identified the unlicensed frequency band (LoRa, DASH7, SigFox, Wi-SUN, etc.), and the licensed frequency band standards (NB-IoT, LTE Cat-M, EC-GSM-IoT, etc.). In general, both types of standards only consider fixed interconnected things, and less attention has been provided to the mobility of the things or devices. In this paper, we address the mobility of the things and the connectivity in each of the three LPWAN standards: LoRaWAN, DASH7, and NB-IoT. In particular, we show how the mobility of things can be achieved when transmitting and receiving data. Then, we provide a general and technical comparison for the three standards. Finally, we illustrate several application scenarios where the mobility is required, and we show how to select the most suited standard. We also discuss the research challenges and perspectives.

Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey

Abstract: Traffic analysis is a compound of strategies intended to find relationships, patterns, anomalies, and misconfigurations, among others things, in Internet traffic. In particular, traffic classification is a subgroup of strategies in this field that aims at identifying the application’s name or type of Internet traffic. Nowadays, traffic classification has become a challenging task due to the rise of new technologies, such as traffic encryption and encapsulation, which decrease the performance of classical traffic classification strategies. Machine learning (ML) gains interest as a new direction in this field, showing signs of future success, such as knowledge extraction from encrypted traffic, and more accurate Quality of Service management. ML is fast becoming a key tool to build traffic classification solutions in real network traffic scenarios; in this sense, the purpose of this investigation is to explore the elements that allow this technique to work in the traffic classification field. Therefore, a systematic review is introduced based on the steps to achieve traffic classification by using ML techniques. The main aim is to understand and to identify the procedures followed by the existing works to achieve their goals. As a result, this survey paper finds a set of trends derived from the analysis performed on this domain; in this manner, the authors expect to outline future directions for ML-based traffic classification.

Fog Computing for the Internet of Things: A Survey

Abstract: Research in the Internet of Things (IoT) conceives a world where everyday objects are connected to the Internet and exchange, store, process, and collect data from the surrounding environment. IoT devices are becoming essential for supporting the delivery of data to enable electronic services, but they are not sufficient in most cases to host application services directly due to their intrinsic resource constraints. Fog Computing (FC) can be a suitable paradigm to overcome these limitations, as it can coexist and cooperate with centralized Cloud systems and extends the latter toward the network edge. In this way, it is possible to distribute resources and services of computing, storage, and networking along the Cloud-to-Things continuum. As such, FC brings all the benefits of Cloud Computing (CC) closer to end (user) devices. This article presents a survey on the employment of FC to support IoT devices and services. The principles and literature characterizing FC are described, highlighting six IoT application domains that may benefit from the use of this paradigm. The extension of Cloud systems towards the network edge also creates new challenges and can have an impact on existing approaches employed in Cloud-based deployments. Research directions being adopted by the community are highlighted, with an indication of which of these are likely to have the greatest impact. An overview of existing FC software and hardware platforms for the IoT is also provided, along with the standardisation efforts in this area initiated by the OpenFog Consortium (OFC).

Query expansion techniques for information retrieval: A survey

Abstract: With the ever increasing size of the web, relevant information extraction on the Internet with a query formed by a few keywords has become a big challenge. Query Expansion (QE) plays a crucial role in improving searches on the Internet. Here, the user’s initial query is reformulated by adding additional meaningful terms with similar significance. QE – as part of information retrieval (IR) – has long attracted researchers’ attention. It has become very influential in the field of personalized social document, question answering, cross-language IR, information filtering and multimedia IR. Research in QE has gained further prominence because of IR dedicated conferences such as TREC (Text Information Retrieval Conference) and CLEF (Conference and Labs of the Evaluation Forum). This paper surveys QE techniques in IR from 1960 to 2017 with respect to core techniques, data sources used, weighting and ranking methodologies, user participation and applications – bringing out similarities and differences.

Crowdsourced Delivery—A Dynamic Pickup and Delivery Problem with Ad Hoc Drivers

Abstract: The trend toward shorter delivery lead times reduces operational efficiency and increases transportation costs for Internet retailers. However, mobile technology creates new opportunities to organi...