Classification of Cyber Security Threats on Mobile Devices and Applications

Almaiah, Mohammed Amin; Alzahrani, Ali; Almomani, Omar; Alhwaitat, Ahmad K.

doi:10.1007/978-3-030-74575-2_6

Cited by 47 publications

(25 citation statements)

References 49 publications

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“…The updated DeLone and McLean model system quality (usability, functionality, interface design, accessibility, ease of use, interactivity), information quality (content adequacy, content usefulness and content design) and service quality (availability, personalization, reliability, trust and responsiveness) [37] Service quality models E-S-Qual efficiency, compliance, availability and privacy [38] WebQual model informational fit-to-task, tailored communications, trust, response time, ease of understanding, intuitive operations, visual appeal, innovativeness, emotional appeal, consistent image, online completeness, and relative advantage [39] WebQual model usability, information and interaction [39] 2.1. Software Quality Models…”

Section: Software Quality Modelsmentioning

confidence: 99%

A Conceptual Framework for Determining Quality Requirements for Mobile Learning Applications Using Delphi Method

et al. 2022

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The development of mobile learning apps might fail due to poor selection of the suitable technical requirements for mobile devices. This will affect the quality of mobile learning applications and, thus, will increase the development cost of mobile learning apps. Due to the above issues, we need to determine the most appropriate technical quality requirements for the development of mobile learning apps that meet user requirements. To achieve that, we propose a comprehensive framework to capture the most suitable technical quality requirements for mobile learning apps. A Delphi method was used to collect, evaluate, and analyze the data for this study. As a result of our Delphi study, we have identified nineteen technical quality requirements, divided into six quality dimensions, for the development of mobile learning applications. The proposed framework is expected to be a guideline for mobile apps designers and developers to successfully develop mobile learning apps.

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Section: Software Quality Modelsmentioning

confidence: 99%

A Conceptual Framework for Determining Quality Requirements for Mobile Learning Applications Using Delphi Method

et al. 2022

Self Cite

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“…This is different from the traditional ICS environments that require different zoning and in-depth defense frameworks. ICS environments differ from the standard enterprise environments in many ways, and below are some of the key differences [ 47 , 48 , 49 , 50 ]. Risk level: significant impact on human lives and possibly the nation.…”

Section: Background and Related Studiesmentioning

confidence: 99%

“…The second level of FN’s latency is reduced because of IoMT device communication. As a result, users’ requirements are encountered in the proposed fog computing (FC) model [ 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ].…”

Section: Proposed Frameworkmentioning

confidence: 99%

A Lightweight Hybrid Deep Learning Privacy Preserving Model for FC-Based Industrial Internet of Medical Things

Almaiah

Ali

Hajjej

et al. 2022

Sensors

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The Industrial Internet of Things (IIoT) is gaining importance as most technologies and applications are integrated with the IIoT. Moreover, it consists of several tiny sensors to sense the environment and gather the information. These devices continuously monitor, collect, exchange, analyze, and transfer the captured data to nearby devices or servers using an open channel, i.e., internet. However, such centralized system based on IIoT provides more vulnerabilities to security and privacy in IIoT networks. In order to resolve these issues, we present a blockchain-based deep-learning framework that provides two levels of security and privacy. First a blockchain scheme is designed where each participating entities are registered, verified, and thereafter validated using smart contract based enhanced Proof of Work, to achieve the target of security and privacy. Second, a deep-learning scheme with a Variational AutoEncoder (VAE) technique for privacy and Bidirectional Long Short-Term Memory (BiLSTM) for intrusion detection is designed. The experimental results are based on the IoT-Botnet and ToN-IoT datasets that are publicly available. The proposed simulations results are compared with the benchmark models and it is validated that the proposed framework outperforms the existing system.

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“…This includes data leakage, social engineering attacks [ 1 ], jamming attacks [ 2 ], software virus security, cryptojacking attacks [ 3 ], wireless channel security, physical device violations, and user location privacy leakage. Researchers have conducted research on these security issues from security threat classification [ 4 ], security defense scheme design, security performance optimization, evaluation, etc. The research covers access domain security, network domain security, user domain security, and application domain security.…”

Section: Introductionmentioning

confidence: 99%

GL-Tree: A Hierarchical Tree Structure for Efficient Retrieval of Massive Geographic Locations

Liu

Zhang

Xin

2023

Sensors

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Location-based application services and location privacy protection solutions are often required for the storage, management, and efficient retrieval of large amounts of geolocation data for specific locations or location intervals. We design a hierarchical tree-like organization structure, GL-Tree, which enables the storage, management, and retrieval of massive location data and satisfies the user’s location-hiding requirements. We first use Geohash encoding to convert the two-dimensional geospatial coordinates of locations into one-dimensional strings and construct the GL-Tree based on the Geohash encoding principle. We gradually reduce the location intervals by extending the length of the Geohash code to achieve geospatial grid division and spatial approximation of user locations. The hierarchical tree structure of GL-Tree reflects the correspondence between Geohash codes and geographic intervals. Users and their location relationships are recorded in the leaf nodes at each level of the hierarchical GL-Tree. In top–down order, along the GL-Tree, efficient storage and retrieval of location sets for specified locations and specified intervals can be achieved. We conducted experimental tests on the Gowalla public dataset and compared the performance of the B+ tree, R tree, and GL-Tree in terms of time consumption in three aspects: tree construction, location insertion, and location retrieval, and the results show that GL-Tree has good performance in terms of time consumption.

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Classification of Cyber Security Threats on Mobile Devices and Applications

Cited by 47 publications

References 49 publications

A Conceptual Framework for Determining Quality Requirements for Mobile Learning Applications Using Delphi Method

A Conceptual Framework for Determining Quality Requirements for Mobile Learning Applications Using Delphi Method

A Lightweight Hybrid Deep Learning Privacy Preserving Model for FC-Based Industrial Internet of Medical Things

GL-Tree: A Hierarchical Tree Structure for Efficient Retrieval of Massive Geographic Locations

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