On promoting ad-hoc collaboration among messengers

The explosion growth in the market place for handheld wireless devices has enabled new opportunities for wireless applications. Currently, handheld devices are restricted to being clients that make requests to servers and receive responses over the network. But as mobile ad-hoc networks become the trend, such devices will need to become active participants that serve requests from other devices and convey data to other devices as well. In this paper we present our vision of the future role that handheld devices will play in a mobile ad-hoc network configuration. We present this vision as part of the MESSENGER project that develops data management mechanisms for UDDI registries of Web services using mobile users and their software agents, and then describe its extension for exchanging descriptions of Web services during ad-hoc collaboration sessions. User agents are in charge of interacting with peer users over an ad-hoc network, and collaborating on feeding UDDI registries with recent content. © 2006 IEEE

Security and Privacy for Green IoT-based Agriculture: Review, Blockchain solutions, and Challenges

open access articleThis paper presents research challenges on security and privacy issues in the field of green IoT-based agriculture. We start by describing a four-tier green IoT-based agriculture architecture and summarizing the existing surveys that deal with smart agriculture. Then, we provide a classification of threat models against green IoT-based agriculture into five categories, including, attacks against privacy, authentication, confidentiality, availability, and integrity properties. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward secure and privacy-preserving technologies for IoT applications and how they will be adapted for green IoT-based agriculture. In addition, we analyze the privacy-oriented blockchain-based solutions as well as consensus algorithms for IoT applications and how they will be adapted for green IoT-based agriculture. Based on the current survey, we highlight open research challenges and discuss possible future research directions in the security and privacy of green IoT-based agriculture

Enabling ad-hoc collaboration between mobile users in the MESSENGER project

Abstract This paper discusses how ad-hoc collaboration boosts the operation of a set of messengers. This discussion continues the research we earlier initiated in the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\mathcal{MESSENGER}$\end{document} project, which develops data management mechanisms for UDDI registries of Web services using mobile users and software agents. In the current operation mode of messengers, descriptions of Web services are first, collected from UDDI registries and later, submitted to other UDDI registries. This submission mode of Web services descriptions does not foster the tremendous opportunities that both wireless technologies and mobile devices offer. When mobile devices are “close” to each other, they can form a mobile ad-hoc network that permits the exchange of data between these devices without any pre-existing communication infrastructure. By authorizing messengers to engage in ad-hoc collaboration, collecting additional descriptions of Web services from other messengers can happen, too. This has several advantages, but at the same time poses several challenges, which in fact highlight the complexity of ad-hoc networks

Ad-hoc collaboration between messengers: Operations and incentives

This paper discusses how ad-hoc collaboration boosts the operation of a set of messengers. This discussion continues the research we earlier initiated in the MESSENGER project, which develops data management mechanisms for UDDI registries of Web services using mobile users and software agents. In the current operation mode of messengers, descriptions of Web services are first, collected from UDDI registries and later on, distributed to other UDDI registries. This distribution mode of Web services descriptions does not foster the tremendous opportunities that both wireless technologies and mobile devices offer. When mobile devices are in the vicinity of each other, they can form a mobile ad-hoc network, which enables the exchange of data between these devices without any preexisting communication infrastructure. By authorizing messengers to engage in collaboration, collecting additional descriptions of Web services from other messengers can happen, too. © 2006 IEEE

Authentication and Authorization for Mobile IoT Devices Using Biofeatures: Recent Advances and Future Trends

Biofeatures are fast becoming a key tool to authenticate the IoT devices; in this sense, the purpose of this investigation is to summarise the factors that hinder biometrics models’ development and deployment on a large scale, including human physiological (e.g., face, eyes, fingerprints-palm, or electrocardiogram) and behavioral features (e.g., signature, voice, gait, or keystroke). The different machine learning and data mining methods used by authentication and authorization schemes for mobile IoT devices are provided. Threat models and countermeasures used by biometrics-based authentication schemes for mobile IoT devices are also presented. More specifically, we analyze the state of the art of the existing biometric-based authentication schemes for IoT devices. Based on the current taxonomy, we conclude our paper with different types of challenges for future research efforts in biometrics-based authentication schemes for IoT devices

Authentication schemes for Smart Mobile Devices: Threat Models, Countermeasures, and Open Research Issues

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This paper presents a comprehensive investigation of authentication schemes for smart mobile devices. We start by providing an overview of existing survey articles published in the recent years that deal with security for mobile devices. Then, we give a classification of threat models in smart mobile devices in five categories, including, identity-based attacks, eavesdropping-based attacks, combined eavesdropping and identity-based attacks, manipulation-based attacks, and service-based attacks. This is followed by a description of multiple existing threat models. We also provide a classification of countermeasures into four types of categories, including, cryptographic functions, personal identification, classification algorithms, and channel characteristics. According to the characteristics of the countermeasure along with the authentication model iteself, we categorize the authentication schemes for smart mobile devices in four categories, namely, 1) biometric-based authentication schemes, 2) channel-based authentication schemes, 3) factors-based authentication schemes, and 4) ID-based authentication schemes. In addition, we provide a taxonomy and comparison of authentication schemes for smart mobile devices in form of tables. Finally, we identify open challenges and future research directions

Assessment of Machine Learning Techniques for Building an Efficient IDS

ntrusion Detection Systems (IDS) are the systems that detect and block any potential threats (e.g. DDoS attacks) in the network. In this project, we explore the performance of several machine learning techniques when used as parts of an IDS. We experiment with the CICIDS2017 dataset, one of the biggest and most complete IDS datasets in terms of having a realistic background traffic and incorporating a variety of cyber attacks. The techniques we present are applicable to any IDS dataset and can be used as a basis for deploying a real time IDS in complex environments

A Novel Two-Stage Deep Learning Model for Efficient Network Intrusion Detection

The network intrusion detection system is an important tool for protecting computer networks against threats and malicious attacks. Many techniques have recently been proposed; however, these techniques face significant challenges due to the continuous emergence of new threats that are not recognized by the existing detection systems. In this paper, we propose a novel two-stage deep learning model based on a stacked auto-encoder with a soft-max classifier for efficient network intrusion detection. The model comprises two decision stages: an initial stage responsible for classifying network traffic as normal or abnormal using a probability score value. This is then used in the final decision stage as an additional feature for detecting the normal state and other classes of attacks. The proposed model is able to learn useful feature representations from large amounts of unlabeled data and classifies them automatically and efficiently. To evaluate and test the effectiveness of the proposed model, several experiments are conducted on two public datasets: an older benchmark dataset, the KDD99, and a newer one, the UNSW-NB15. The comparative experimental results demonstrate that our proposed model significantly outperforms the existing models and methods and achieves high recognition rates, up to 99.996% and 89.134%, for the KDD99 and UNSW-NB15 datasets, respectively. We conclude that our model has the potential to serve as a future benchmark for deep learning and network security research communities