For various reasons, the cloud computing paradigm is unable to meet certain requirements (e.g. low latency and jitter, context awareness, mobility support) that are crucial for several applications (e.g. vehicular networks, augmented reality). To fulfil these requirements, various paradigms, such as fog computing, mobile edge computing, and mobile cloud computing, have emerged in recent years. While these edge paradigms share several features, most of the existing research is compartmentalised; no synergies have been explored. This is especially true in the field of security, where most analyses focus only on one edge paradigm, while ignoring the others. The main goal of this study is to holistically analyse the security threats, challenges, and mechanisms inherent in all edge paradigms, while highlighting potential synergies and venues of collaboration. In our results, we will show that all edge paradigms should consider the advances in other paradigms.
Fog computing is an emerging computing paradigm that has come into consideration for the deployment of IoT applications amongst researchers and technology industries over the last few years. Fog is highly distributed and consists of a wide number of autonomous end devices, which contribute to the processing. However, the variety of devices offered across different users are not audited. Hence, the security of Fog devices is a major concern in the Fog computing environment. Furthermore, mitigating and preventing those security measures is a research issue. Therefore, to provide the necessary se…
The rapid growth of the Internet of Things (IoT), cloud computing, Fog computing, mobile edge computing and wireless grids has resulted in the widespread deployment of relatively immature technology. These technologies, which will primarily use 5G wireless communication networks, are becoming popular because they can be deployed quickly with little infrastructure and lends themselves to environments utilizing numerous internet connected devices (ICD). Because of the threat of exploitation, these networks have to be protected by a robust security architecture due to these technologies being pla…
To slow down the spread of COVID-19, governments around the world are trying to identify infected people and to contain the virus by enforcing isolation and quarantine. However, it is difficult to trace people who came into contact with an infected person, which causes widespread community transmission and mass infection. To address this problem, we develop an e-government Privacy Preserving Mobile and Fog computing framework entitled PPMF that can trace infected and suspected cases nationwide. We use personal mobile devices with contact tracing app and two types of stationary fog nodes, named…
Abstract--- With the rapid growth of the Internet of Things (IoT), current Cloud systems face various drawbacks such as lack of mobility support, location-awareness, geo-distribution, high latency, as well as cyber threats. Fog/Edge computing has been proposed for addressing some of the drawbacks, as it enables computing resources at the network's edges and it locally offers big-data analytics rather than transmitting them to the Cloud. The Fog is defined as a Cloud-like system having similar functions, including software-, platform- and infrastructure-as services. The deployment of Fog applic…
Due to the complex nature of mobile communication systems, most of the security efforts in its domain are isolated and scattered across underlying technologies. This has resulted in an obscure view of the overall security. In this work, we attempt to fix this problem by proposing a domain-specific threat modeling framework. By gleaning from a diverse and large body of security literature, we systematically organize the attacks on mobile communications into various tactics and techniques. Our framework is designed to model adversarial behavior in terms of its attack phases and to be used as a c…
The mobile device is one of the fasted growing technologies that is widely used in a diversifying sector. Mobile devices are used for everyday life, such as personal information exchange - chatting, email, shopping, and mobile banking, contributing to information security threats. Users' behavior can influence information security threats. More research is needed to understand users' threat avoidance behavior and motivation. Using Technology threat avoidance theory (TTAT), this study assessed factors that influenced mobile device users' threat avoidance motivations and behaviors as it relates …
As technology and technology companies have grown in power, ubiquity, and societal influence, some companies -- and notably some mobile apps -- have come to be perceived as privacy threats. Prior work has considered how various factors impact perceptions of threat, including social factors, political speech, and user-interface design. In this work, we investigate how user-visible context clues impact perceptions about whether a mobile application application poses a privacy threat. We conduct a user study with 2109 users in which we find that users depend on context clues -- such as presence o…
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 describe and 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. We also provide a classification of countermeasures into four types of categories, incl…
The mining process in blockchain requires solving a proof-of-work puzzle, which is resource expensive to implement in mobile devices due to the high computing power and energy needed. In this paper, we, for the first time, consider edge computing as an enabler for mobile blockchain. In particular, we study edge computing resource management and pricing to support mobile blockchain applications in which the mining process of miners can be offloaded to an edge computing service provider. We formulate a two-stage Stackelberg game to jointly maximize the profit of the edge computing service provid…
With the increasing development of advanced communication technologies, vehicles are becoming smarter and more connected. Due to the tremendous growth of various vehicular applications, a huge amount of data is generated through advanced on-board devices and is deemed critical to improve driving safety and enhance vehicular services. However, cloud based models often fall short in applications where latency and mobility are critical. In order to fully realize the potential of vehicular networks, the challenges of efficient communication and computation need to be addressed. In this direction, …
New Threats to SMS-Assisted Mobile Internet Services from 4G LTE: Lessons Learnt from Distributed Mobile-Initiated Attacks towards Facebook and Other Services
Mobile edge caching is a promising technology for the next-generation mobile networks to effectively offer service environment and cloud-storage capabilities at the edge of networks. By exploiting the storage and computing resources at the network edge, mobile edge caching can significantly reduce service latency, decrease network load, and improve user experience. On the other hand, edge caching is subject to a number of threats regarding privacy violation and security breach. In this article, we first introduce the architecture of mobile edge caching, and address the key problems regarding w…
Ericsson Research（爱立信研究院）；Ericsson Security Research（爱立信安全研究院）；KTH Royal Institute of Technology（瑞典皇家理工学院）
Machine Learning (ML) is an important enabler for optimizing, securing and managing mobile networks. This leads to increased collection and processing of data from network functions, which in turn may increase threats to sensitive end-user information. Consequently, mechanisms to reduce threats to end-user privacy are needed to take full advantage of ML. We seamlessly integrate Federated Learning (FL) into the 3GPP 5G Network Data Analytics (NWDA) architecture, and add a Multi-Party Computation (MPC) protocol for protecting the confidentiality of local updates. We evaluate the protocol and fin…
Federated learning (FL) is a machine learning technique that aims at training an algorithm across decentralized entities holding their local data private. Wireless mobile networks allow users to communicate with other fixed or mobile users. The road traffic network represents an infrastructure-based configuration of a wireless mobile network where the Connected and Automated Vehicles (CAV) represent the communicating entities. Applying FL in a wireless mobile network setting gives rise to a new threat in the mobile environment that is very different from the traditional fixed networks. The thr…
Abuse of zero-permission sensors on-board mobile and wearable devices to infer users' personal context and information is a well-known privacy threat that has received significant attention. Efforts towards protection mechanisms that prevent or limit the success of such threats, however, have been ad-hoc so far and have primarily focused on designing threat-specific customized defense mechanisms. Such approaches are not very practical, as evident from their limited adoption within major mobile/wearable operating systems. In the end, it is clear that all privacy threats that take advantage of u…
The burgeoning technology of Mobile Edge Computing is attracting the traditional LBS and LS to deploy due to its nature characters such as low latency and location awareness. Although this transplant will avoid the location privacy threat from the central cloud provider, there still exists the privacy concerns in the LS of MEC scenario. Location privacy threat arises during the procedure of the fingerprint localization, and the previous studies on location privacy are ineffective because of the different threat model and information semantic. To address the location privacy in MEC environment,…
Mobile and Internet of Things devices are generating enormous amounts of multi-modal data due to their exponential growth and accessibility. As a result, these data sources must be directly analyzed in real time at the network edge rather than relying on the cloud. Significant processing power at the network's edge has made it possible to gather data and make decisions prior to data being sent to the cloud. Moreover, security problems have significantly towered as a result of the rapid expansion of mobile devices, Internet of Things (IoT) devices, and various network points. It's much harder t…
Todays world is digital, global, and interconnected and mobile devices are at the heart of modern communications in business, politics, and civil society. However, cyber threats are an omnipresent reality in our hyper-connected world. The world economic forum ranks cyber threats consistently among the global top security risks. Attacks on mobile devices grow yearly in volume and magnitude causing severe damage. This paper offers a comprehensive overview of modern mobile attacks categorized into malware, phishing, communication, supply chain, physical, and authentication attacks, including a se…
The age of technology has created a huge market for smartphones and Apps usage and a new generation has been created based on knowledge sharing. Now knowledge has been made easily accessible by Apps but; are users even aware of the permissions that these Apps require and the privacy issues involved? The study was conducted on the basis of how users make use of Apps. It was conducted through the assessment of permissions required by various Apps through carefully selected third-party Apps and the devices settings and also a review of existing literature that has been conducted in fields within …
Mobile devices and technologies offer a tremendous amount of benefits to users, although it is also understood that it introduces a set of challenges when it comes to security, compliance, and risks. More and more healthcare organizations have been seeking to update their outdated technology, and have considered the adoption of mobile devices to meet these needs. However, introducing mobile devices and technology also introduces new risks and threats to the organization. As a test case, we examine Epic Rover, a mobile application that has been identified as a viable solution to manage the elec…
Mobile GUI Agents under Real-world Threats: Are We There Yet?
Institute for AI Industry Research (AIR), Tsinghua University（人工智能产业研究院（AIR），清华大学）；University of Electronic Science and Technology of China（电子科学与技术大学）；Peking University（北京大学）；MiLM Plus, Xiaomi Inc.（MiLM Plus，小米公司）
GhostEI-Bench: Do Mobile Agents Resilience to Environmental Injection in Dynamic On-Device Environments?
Shanghai Jiao Tong University, School of Computer Science（上海交通大学计算机科学学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The University of Hong Kong（香港大学）
The fifth generation of mobile networks (5G) promises a range of new capabilities including higher data rates and more connected users. To support the new capabilities and use cases the 5G Core Network (5GCN) will be dynamic and reconfigurable in nature to deal with demand. It is these improvements which also introduce issues for traditional security monitoring methods and techniques which need to adapt to the new network architecture. The increased data volumes and dynamic network architecture mean an approach is required to focus security monitoring resources where it is most needed and reac…
Compared to cryptography, steganography is a less discussed domain. However, there is a recent trend of exploiting various information hiding techniques to empower malware, for instance to bypass security frameworks of mobile devices or to exfiltrate sensitive data. This is mostly due to the need to counteract increasingly sophisticated security mechanisms, such as code analysis, runtime countermeasures, or real-time traffic inspection tools. In this perspective, this paper presents malware exploiting information hiding in a broad sense, i.e., it does not focus on classical covert channels, bu…
Machine Learning Driven Smishing Detection Framework for Mobile Security
CSIRO’s Data61（澳大利亚联邦科学与工业研究组织Data61）；National Institute of Technology, Kurukshetra（库鲁克谢特拉国家理工学院）；University of Adelaide（阿德莱德大学）；PGIMS, Haryana, India（印度哈里亚纳邦PGIMS）
The passage of FISMA and HIPPA Acts have mandated various security controls that ensure the privacy of patients data. Hospitals and health-care organizations are required by law to ensure that patients data is stored and disseminated in a secure fashion. The advent of Bring Your Own Devices (BYOD), mobile devices, instant messaging (such as WhatsApp) and cloud technology however, have brought forth new challenges. The advent of Internet of Things (IOT) have complicated the matters further as organizations are not fully cognizant to the all facets of threats to data privacy. Physicians and heal…
Hanoi University of Science and Technology（河内科技大学）；Nanyang Technological University（南洋理工大学）；University of Technology Sydney（悉尼科技大学）
With the rapid growth of mobile applications and cloud computing, mobile cloud computing has attracted great interest from both academia and industry. However, mobile cloud applications are facing security issues such as data integrity, users' confidentiality, and service availability. A preventive approach to such problems is to detect and isolate cyber threats before they can cause serious impacts to the mobile cloud computing system. In this paper, we propose a novel framework that leverages a deep learning approach to detect cyberattacks in mobile cloud environment. Through experimental re…
SuperEar: Eavesdropping on Mobile Voice Calls via Stealthy Acoustic Metamaterials
ACSS Lab, NorthWest University（北western大学ACSS实验室）；PINE Lab, NorthWest University（北western大学PINE实验室）；Lancaster University（兰卡斯特大学）；Xi'an University of Posts and Telecommunications（西安邮电大学）；University of Leeds（利兹大学）
