A Survey on Wireless Security: Technical Challenges, Recent Advances and Future Trends

This paper examines the security vulnerabilities and threats imposed by the inherent open nature of wireless communications and to devise efficient defense mechanisms for improving the wireless network security. We first summarize the security requirements of wireless networks, including their authenticity, confidentiality, integrity and availability issues. Next, a comprehensive overview of security attacks encountered in wireless networks is presented in view of the network protocol architecture, where the potential security threats are discussed at each protocol layer. We also provide a survey of the existing security protocols and algorithms that are adopted in the existing wireless network standards, such as the Bluetooth, Wi-Fi, WiMAX, and the long-term evolution (LTE) systems. Then, we discuss the state-of-the-art in physical-layer security, which is an emerging technique of securing the open communications environment against eavesdropping attacks at the physical layer. We also introduce the family of various jamming attacks and their counter-measures, including the constant jammer, intermittent jammer, reactive jammer, adaptive jammer and intelligent jammer. Additionally, we discuss the integration of physical-layer security into existing authentication and cryptography mechanisms for further securing wireless networks. Finally, some technical challenges which remain unresolved at the time of writing are summarized and the future trends in wireless security are discussed.Comment: 36 pages. Accepted to Appear in Proceedings of the IEEE, 201

Secure Identification in Social Wireless Networks

The applications based on social networking have brought revolution towards social life and are continuously gaining popularity among the Internet users. Due to the advanced computational resources offered by the innovative hardware and nominal subscriber charges of network operators, most of the online social networks are transforming into the mobile domain by offering exciting applications and games exclusively designed for users on the go. Moreover, the mobile devices are considered more personal as compared to their desktop rivals, so there is a tendency among the mobile users to store sensitive data like contacts, passwords, bank account details, updated calendar entries with key dates and personal notes on their devices. The Project Social Wireless Network Secure Identification (SWIN) is carried out at Swedish Institute of Computer Science (SICS) to explore the practicality of providing the secure mobile social networking portal with advanced security features to tackle potential security threats by extending the existing methods with more innovative security technologies. In addition to the extensive background study and the determination of marketable use-cases with their corresponding security requirements, this thesis proposes a secure identification design to satisfy the security dimensions for both online and offline peers. We have implemented an initial prototype using PHP Socket and OpenSSL library to simulate the secure identification procedure based on the proposed design. The design is in compliance with 3GPP‟s Generic Authentication Architecture (GAA) and our implementation has demonstrated the flexibility of the solution to be applied independently for the applications requiring secure identification. Finally, the thesis provides strong foundation for the advanced implementation on mobile platform in future

Cryptanalysis of the Improved Cellular Message Encryption Algorithm

This paper analyzes the Improved Cellular Message Encryption Algorithm (CMEA-I) which is an improved version of the Telecommunication Industry Association\u27s Cellular Message Encryption Algorithm (CMEA). We present a chosen-plaintext attack of CMEA-I which requires less than 850 plaintexts in its adaptive version. This demonstrates that the improvements made over CMEA are ineffective to thwart such attacks and confirms that the security of CMEA and its variants must be reconsidered from the beginning

A fog computing solution for context-based privacy leakage detection for android healthcare devices

Intelligent medical service system integrates wireless internet of things (WIoT), including medical sensors, wireless communications, and middleware techniques, so as to collect and analyze patients' data to examine their physical conditions by many personal health devices (PHDs) in real time. However, large amount of malicious codes on the Android system can compromise consumers' privacy, and further threat the hospital management or even the patients' health. Furthermore, this sensor-rich system keeps generating large amounts of data and saturates the middleware system. To address these challenges, we propose a fog computing security and privacy protection solution. Specifically, first, we design the security and privacy protection framework based on the fog computing to improve tele-health and tele-medicine infrastructure. Then, we propose a context-based privacy leakage detection method based on the combination of dynamic and static information. Experimental results show that the proposed method can achieve higher detection accuracy and lower energy consumption compared with other state-of-art methods.This work was supported by the National Natural Science Foundation of China (General Program) under Grant No.61572253, the 13th Five-Year Plan Equipment Pre-Research Projects Fund under Grant No.61402420101HK02001, and the Aviation Science Fund under Grant No. 2016ZC52030

CPA\u27s guide to the Internet

https://egrove.olemiss.edu/aicpa_guides/1966/thumbnail.jp

Survival guide for road warriors : essentials for the mobile CPA

https://egrove.olemiss.edu/aicpa_guides/1233/thumbnail.jp

E-Commerce: Where Do We Go from Here?

The dynamics of the Internet and e-commerce has spawned change in business and industry since the early 1990s. Many e-merchants have benefited from it, while others fell behind. B2B, B2C, and B2G have all matured and grew to be the backbone of today’s World Wide Web. As a result, a number of issues, problems, and reflections have come up that warrant analy sis. In this paper, we bring up key trends and developments in e-business, where ecommerce is going, and the implications for management, profitability, and potential growth. Mobile commerce and wireless technology is especially noted

CPA\u27s guide to the Internet

https://egrove.olemiss.edu/aicpa_guides/1967/thumbnail.jp

Mobile Big Data Analytics in Healthcare

Mobile and ubiquitous devices are everywhere around us generating considerable amount of data. The concept of mobile computing and analytics is expanding due to the fact that we are using mobile devices day in and out without even realizing it. These mobile devices use Wi-Fi, Bluetooth or mobile data to be intermittently connected to the world, generating, sending and receiving data on the move. Latest mobile applications incorporating graphics, video and audio are main causes of loading the mobile devices by consuming battery, memory and processing power. Mobile Big data analytics includes for instance, big health data, big location data, big social media data, and big heterogeneous data. Healthcare is undoubtedly one of the most data-intensive industries nowadays and the challenge is not only in acquiring, storing, processing and accessing data, but also in engendering useful insights out of it. These insights generated from health data may reduce health monitoring cost, enrich disease diagnosis, therapy, and care and even lead to human lives saving. The challenge in mobile data and Big data analytics is how to meet the growing performance demands of these activities while minimizing mobile resource consumption. This thesis proposes a scalable architecture for mobile big data analytics implementing three new algorithms (i.e. Mobile resources optimization, Mobile analytics customization and Mobile offloading), for the effective usage of resources in performing mobile data analytics. Mobile resources optimization algorithm monitors the resources and switches off unused network connections and application services whenever resources are limited. However, analytics customization algorithm attempts to save energy by customizing the analytics process while implementing some data-aware techniques. Finally, mobile offloading algorithm decides on the fly whether to process data locally or delegate it to a Cloud back-end server. The ultimate goal of this research is to provide healthcare decision makers with the advancements in mobile Big data analytics and support them in handling large and heterogeneous health datasets effectively on the move