Mobile Cloud Environment Research Articles

A CP-ABE scheme based on multi-authority in hybrid clouds for mobile devices

With the rapid development of cloud computing, the ensuing security issues have become increasingly prominent. In this context, attribute-based encryption (ABE) has gradually attracted widespread attentions due to its unique attribute matching mechanism. At the same time, mobile devices have put forward higher requirements for the design and deployment of ABE schemes. Therefore, in this paper, we propose an original hybrid cloud multi-authority ciphertext-policy attribute-based encryption (HCMACP-ABE) scheme. Specifically, we utilize the LSSS (Linear Secret-Sharing Schemes) access structure to realize secure access control, while the private cloud is responsible for maintaining the user’s authorization list and verifying the user. Finally, we prove that our proposal achieves IND-CCA secure and is efficient in a mobile hybrid cloud environment.

Enabling Fog Complex Security Services in Mobile Cloud Environments

The security infrastructure in modern mobile cloud relays on a set of atomic security services like key distribution, and authentication services which are mostly deployed at the fog. A mobile node may provide a simple or an atomic security service. Complex security services are obtained by coordinating several atomic services at different nodes. In mobile fog environments, the availability of such services for the mobile nodes at the edge straggles challenging nature of the environment and then service availability is deeply affected. Complex security requirements require available several atomic services at the edge and fog levels. So, increasing atomic service availability is vital and enhances security features between the fog and the edge. In this work a proactive complex security service replication protocol for increasing availability of fog security services is proposed. The protocol is able to manage proactively replicas whenever they are requested. It is aware of the distributed resources at the edge nodes. Using an extensive simulation analysis, the protocol performance shows a better achieved availability and reasonable response time for complex security services in different configurations.

Design and development of a parallelized algorithm for face recognition in mobile cloud environment

<p>Face recognition is the biometric application to recognise the identity. Face recognition application holds a set of images which are called databases stored by the user at cloud database. Cloud computing environment, database can be stored in the cloud environment to achieve huge data storage area. The problem with these data storages are that because of that huge size processing on this storage takes too much of compiling time. This paper aims to develop face recognition in mobile cloud environment by exploiting data or task parallelism in existing face recognition algorithms. To design and develop parallel PCA based face recognition algorithm. The parallel PCA face recognition algorithm has been deployed in the cloud server for performing PCA by request of user. It matches the image on the cloud server and gives response back to the user in the fewer amounts of time and with reduced latency. The developed Parallel PCA face recognition algorithm has minimized the overall response time for the face recognition algorithm. The performance of the developed system is tested and analysed on real face images. To analyse the developed system, a centralized and distributed based server methods are developed and comparison is being carried out. The conclusion drawn that the distributed server improves the efficiency as well as the computing power as compared with centralized server system. The comparison of centralized and distributed based servers is carried out by observing the time taken while varying the number of images in the training dataset.</p>

Blockchain based Privacy Preserving User Authentication Protocol for Distributed Mobile Cloud Environment

The development in cloud computing platforms has resulted, hosting many day-to-day service applications in the cloud. To avail the services provided by different cloud service providers (CSPs), the mobile user has to register his/her identity with the CSPs. The mobile user (MU) has to remember multiple identities and credentials to access various CSPs. Many single sign-on schemes have been proposed in the literature to eliminate multiple registrations by mobile users to access CSPs. Most of these schemes rely on a trusted third party known as Registration Authority Center (RAC), which is a centralized entity to manage the identity information of all the mobile users registered with it. The centralized RAC has two operational problems, i.e., RAC has full control over the data it possesses, resulting in the possibility of the data breach and increased risk of single-point-of-failure. In this paper, we propose a blockchain based privacy preserving user authentication protocol for distributed mobile cloud environment, which solves these two traditional problems with centralized registration centers. In proposed protocol, the registration of MU and CSP are performed through public blockchain network for MU to access CSPs and the authentication was performed between MU and CSP through public blockchain. The public blockchain network stores MU and CSPs identity information. Public blockchain network provides integrity to the data stored in it and secures the system from single-point-of-failure. In addition, security analysis and performance analysis were also performed for proposed protocol and it showed that the proposed protocol is secure from all-known attacks with better performance efficiency.

Detection of malicious Android applications using Ontology-based intelligent model in mobile cloud environment

Mobile Cloud Computing (MCC) is a computing model that makes mobile devices resourceful by executing mobile applications (apps) in the cloud and storing data in cloud servers. MCC faces several security threats in both the Cloud and Mobile environments. Among several threats, malicious apps are the most threatening ones, because they can perform various malicious activities in both environments. The traditional malware detection methods may not detect new types of malware or rapidly changing malware behavior. So, there is a need to develop an accurate model for detecting malicious apps in the MCC environment. Scalability and Knowledge Reusability are challenging issues in existing detection methods. To overcome these issues, the proposed model uses an effective Ontology-based intelligent model based on app permissions to detect malware apps. This model extracts the relationship between the static features from the apps and builds an Apps Feature Ontology (AFO). A concept vector set for apps is created using the items obtained from the AFO. The most discriminant features are selected using optimization algorithms like Particle Swarm Optimization, Social Spider Algorithm (SSA), and Gravitational Search Algorithm to reduce the dimension of the concept vector set. Various classifiers are applied to the reduced set. The efficiency of the proposed approach was evaluated on datasets obtained from the AndroZoo repository and VirusShare. The experimental results reveal that the proposed model can correctly detect malware using the Random Forest (RF) classifier with SSA and achieve higher detection accuracy with the lesser fall-out and less detection speed than existing Android malware detection techniques. Specifically, RF with SSA obtained higher accuracy, F1-score, and reduction in the fall-out of 94.11%, 93%, and 3%, respectively.

The Enhanced Forensic Examination and Analysis for Mobile Cloud Platform by Applying Data Mining Methods

Investigating the mobile cloud environment is a challenging task due to the characteristics of voluminous data, dispersion of data, virtualization, and diverse data. Recent research works focus on applying the latest forensic methodologies to the mobile cloud investigation. This paper proposes an enhanced forensic examination and analysis model for the mobile cloud environment that incorporates timeline analysis, hash filtering, data carving, and data transformation sub-phases to improve the performance of the cloud evidence identification and overall forensic decision-making. It analyzes the timeline of events and filters the case-specific files based on the hash values and metadata using the data mining methods. The proposed forensic model performs the in-place carving on the filtered data to guide the investigation and integrates the heterogeneous file types and distributed pieces of evidence with the assistance of the data mining. Finally, the proposed approach employs LSTM based model that significantly improves the forensic decision making.

Improving Data Services of Mobile Cloud Storage with Support for Large Data Objects using OpenStack Swift

Providing data services support for large file upload and download is increasingly vital for mobile cloud storage. There is an increase in mobile users whose data access trends show more access and large file sharing. It is a challenging task for Mobile Application Developers to handle upload and retrieve large files to/from a mobile app because of difficulties with latency, bandwidth, speed, errors, and disruptions to service in a wireless mobile environment. Some scenarios require these large files to be used offline, sometimes to be updated by a single user, and sometimes be shared among all other users. The Wireless mobile environment must consider mobile user’s constraints, such as frequent disconnections and low bandwidth, which affect the ability to handle data and transactions management. The primary objective of this study is to propose a cloud-based Mobile Sync service (sometimes referred as Mobile backend as a Service) with OpenStack Swift object storage to manage large objects efficiently using two main techniques of segmentation and object chunking with compression in a mobile cloud environment. This work further contributed to a prototype implementation of the proposed framework and provides Application Programming Interface (API) consisting of Create, Read, and Delete queries and chunking operations and a lightweight sync protocol that can manage large file synchronization and access. The experimental findings with object-chunking tested size settings show that the proposed Mobile Sync framework can accommodate large files ranging from 100MB to 1GB and provides a decrease in upload/download synchronization times of 63.203% / 92.987%percent as compared to other frameworks.

A Fine-Grained and Lightweight Data Access Control Model for Mobile Cloud Computing

With rapidly increasing adoption of cloud computing and the advancement of today mobile computing, it is inevitable that mobile devices are used to receive and send the data through the mobile cloud platform. This increases the convenience and flexibility of data access over the cloud computing since data users are able to access the shared data anytime, anywhere via mobile devices. However, using mobile devices in accessing shared data in a cloud where the sensitive data is encrypted is not practical because mobile devices have limited computing resources in dealing with heavy cryptographic operations. In this article, we propose a lightweight collaborative ciphertext policy attribute role-based encryption (LW-C-CP-ARBE) scheme to support a fine-grained and lightweight access control for mobile cloud environment. We apply CP-ABE approach as a core cryptographic access control and introduce a new proxy re-encryption (PRE) protocol to reduce data re-encryption and decryption cost for the mobile users. To this end, the overhead in running the cryptographic operation at the end-user device is small. In addition, we develop secure access policy sharing and re-encryption protocol to enable users having write privilege to update the data and request the proxy to perform data re-encryption. Finally, we present the evaluation and experiments to demonstrate the efficiency and practicality of our system.

Optimized mobile cloud resource discovery architecture based on dynamic cognitive and intelligent technique

In current world, the Mobile Cloud Computing (MCC) will undergo a dramatic upgrade with integrating of mobile cloud resources in small regions to provide a better MCC service. This paper considers the MCC resource availability problem in integrated mobile cloud environment to optimization resources availability with a better effectiveness, accuracy, reliable, low latency and low complexity. The DCICRD architecture is proposed in this paper optimizes the resource discover process with better resource availability. The proposed DCICRD architecture runs various operations such Resource demand prediction to find the required level of resources, Cloudlet Resource Discovery process which discovers resources based on the requirement predicted using two states expand and shrink. The expand state is used to discover resources on-demand in resource scarcity situation and the shrink state is called when abundant resource available to the required level. Further, Resource reliability check is performed to identify the reliable resource with energy level and signal strength above the threshold level. Finally, Local Resource Information Management Table is used to store the local resource provider information and Central Resource Information Management Table is used to store all local resource information for handover of resource provider. The implementation and evaluation is conducted by comparing the HARD architecture. The comparison result shows that proposed DCICRD architecture is better than HARD architecture by optimizing the resource discovery process and produce reliable resources on demand as per the required level with a better effectiveness using expand and shrink state, accuracy by making resource available for required level, reliable by sending message for frequent checking of Resource with required energy level and signal strength, low latency by making resource available locally and reduce complexity by performing only on-demand resource discovery operation after initial startup. Thus, the proposed DCICRD architecture is better when compared to HARD architecture.

Feature indexing and search optimization for enhancing the forensic analysis of mobile cloud environment

ABSTRACT The increased utilization of Mobile Cloud Computing (MCC) technology creates an opportunity for cybercrimes. Modeling the suitable methods for mobile cloud forensic examination and analysis is essential to improve the investigation performance. This paper incorporates data mining and optimization methods to enforce precise handling of the mobile cloud evidence in examination and analysis to improve the investigation performance. It enhances the analysis of the mobile cloud forensics with the incorporation of the evidence indexing, cross-referencing, and keyword searching as the sub-processes. The proposed Forensic Examination and analysis methodology using the Data mining and Optimization (FEDO) approach examines the key features of the evidence and indexes the pieces of evidence with key features to facilitate the investigation over the massive cloud evidence. By analyzing the temporal and geo-information, it applies cross-referencing to alleviate the evidence toward the case-specific evidence. The proposed methodology improves the searching capability of the investigation through the Linearly Decreasing Weight (LDW) strategy based Particle Swarm Optimization (PSO) algorithm. Thus, the experimental results demonstrate that the proposed forensic methodology yields better investigation performance in terms of accuracy of evidence detection.

Computational intelligence intrusion detection techniques in mobile cloud computing environments: Review, taxonomy, and open research issues

With the increasing utilization of the Internet and its provided services, an increase in cyber-attacks to exploit the information occurs. A technology to store and maintain user's information that is mostly used for its simplicity and low-cost services is cloud computing (CC). Also, a new model of computing that is noteworthy today is mobile cloud computing (MCC) that is used to reduce the limitations of mobile devices by allowing them to offload certain computations to the remote cloud. The cloud environment may consist of critical or essential information of an organization; therefore, to prevent this environment from possible attacks a security solution is needed. An intrusion detection system (IDS) is a solution to these security issues. An IDS is a hardware or software device that can examine all inside and outside network activities and recognize doubtful patterns that may demonstrate a network attack and automatically alert the network (or system) administrator. Because of the ability of an IDS to detect known/unknown (inside/outside) attacks, it is an excellent choice for securing cloud computing. Various methods are used in an intrusion detection system to recognize attacks more accurately. Unlike survey papers presented so far, this paper aims to present a comprehensive survey of intrusion detection systems that use computational intelligence (CI) methods in a (mobile) cloud environment. We firstly provide an overview of CC and MCC paradigms and service models, also reviewing security threats in these contexts. Previous literature is critically surveyed, highlighting the advantages and limitations of previous work. Then we define a taxonomy for IDS and classify CI-based techniques into single and hybrid methods. Finally, we highlight open issues and future directions for research on this topic.

Client Aware Scalable Cloudlet to Augment Edge Computing with Mobile Cloud Migration Service

<p class="0abstract">Computing with mobile is still in its infancy due to its limitations of computational power, battery lifetime and storage capacity. These limitations hinder the growth of mobile computing, which in-turn affects the growth of computationally intensive applications developed for the mobile devices. So in-order to help execute complex applications within the mobile device, mobile cloud computing (MCC) emerged as a feasible solution. The job of offloading the task to the cloud data center for storage and execution from the mobile seems to gain popularity, however, issues related to network bandwidth, loss of mobile data connectivity, and connection setup does not augment well to extend the benefits offered by MCC. Cloudlet servers filled this gab by assisting the mobile cloud environment as an edge device, offering compute power to the connected devices with high speed wireless LAN connectivity. Implementation constraints of cloudlet faces severe challenges in-terms of its storage, network sharing, and VM provisioning. Moreover, the number of connected devices of the cloudlet and its load conditions vary drastically leading to unexpected bottleneck, in which case the availability to server becomes an issue. Therefore, a scalable cloudlet, Client Aware Scalable Cloudlet (CASC) is proposed with linear regression analysis, predicting the knowledge of expected load conditions for provisioning new virtual machines and to perform resource migration.</p>

Mobile Cloud Forensic Readiness Process Model for Cloud-Based Mobile Applications

Nowadays, mobile cloud applications have attracted millions of smartphone users due to the proliferation of cyber technologies and a wide range of applications. Mobile cloud forensic investigation methodologies need tremendous growth due to the increasing crime incidents. The forensic readiness model plays a significant role in the forensic investigation framework by ensuring the ease of investigation to the forensic investigator. The existing forensic readiness supports either the mobile device or cloud environment, which lacks to prepare the information for the investigation. This article presents a mobile cloud forensic readiness process model to identify the factors and prepare the information that effectively supports forensic investigations. The proposed model involves requirements for the mobile cloud forensics from multiple perspectives with the aim of developing the forensic-ready system. As a result, the proposed forensic readiness model enables the mobile cloud forensic to improve the accuracy of investigation as well as reduces the investigation time significantly.

Energy Efficient f or Distributed Wireless Data Transmission in Mobile Cloud Computing

In Mobile cloud computing, Energy Efficiency is the Key factors to perform every task with less Energy. The Smart phone users are increasing day by day the data transfer speed is also increase and the energy consumption is also increase. In this paper to maintain the energy efficient for distributed wireless data transmission in Mobile cloud computing is called Distributed Wireless Data Transmission (DWDT). By this method, energy efficient is to be handled by using best intermediate path of data transmission in Mobile Cloud Computing with Energy Efficient. With this methodology the mobile cloud services can be reliability and energy efficiency in wireless data transmission in mobile cloud environment.

An energy efficient service composition mechanism using a hybrid meta-heuristic algorithm in a mobile cloud environment

By increasing mobile devices in technology and human life, using a runtime and mobile services has gotten more complex along with the composition of a large number of atomic services. Different services are provided by mobile cloud components to represent the non-functional properties as Quality of Service (QoS), which is applied by a set of standards. On the other hand, the growth of the energy-source heterogeneity in mobile clouds is an emerging challenge according to the energy saving problem in mobile nodes. In order to mobile cloud service composition as an NP-Hard problem, an efficient selection method should be taken by problem using optimal energy-aware methods that can extend the deployment and interoperability of mobile cloud components. Also, an energy-aware service composition mechanism is required to preserve high energy saving scenarios for mobile cloud components. In this paper, an energy-aware mechanism is applied to optimize mobile cloud service composition using a hybrid Shuffled Frog Leaping Algorithm and Genetic Algorithm (SFGA). Experimental results capture that the proposed mechanism improves the feasibility of the service composition with minimum energy consumption, response time, and cost for mobile cloud components against some current algorithms.

Blockchain of Carbon Trading for UN Sustainable Development Goals

Carbon credits should reduce the environmental pollution and carbon emission of the Earth in the future. The market for carbon credits will become a critical issue from 2021, and carbon credits will be applied to systems where individuals can trade. In order for these carbon credits to be traded between individuals, however, a corresponding exchange of carbon credits is needed. Policies, strategies, and technologies are also necessary to measure the trading of carbon credits. This paper aims at making transactions more reliable by applying blockchain technology to measure carbon emission rights. It uses blockchain to verify carbon emissions rights among the UN-SDGs’ (United Nations Sustainable Development Goals’) 17 tasks. In addition, it introduces the necessary dApp. In fact, we can protect against carbon emissions anomalies by using big data and artificial intelligence in mobile cloud environments. Thus, this paper proposes a blockchain-based carbon emission rights verification system to learn proven data further by using the governance system analysis and blockchain mainnet engine to solve these problems.

SECURING MOBILE ADHOC NETWORKS AND CLOUD ENVIRONMENT

Securing mobile adhoc networks and cloud environment in opposition to denial of service attack by examine and predict the network traffic. DDoS attacks are most important threats next to the accessibility of cloud services. Prevention mechanisms to protect next to DDoS attacks are not forever efficient on their own. Unite dissimilar method (load balancing, throttling and Honey pots) to build hybrid defense method, in meticulous with dissimilar cloud computing layers, is extremely recommended. In this paper, a variety of DDoS attacks have been presented. We as well highlighted the defense methods to counter attack dissimilar types DDoS attacks in the cloud environment. This paper proposes SVM-based algorithm to anomaly intrusion detection. A multiclass SVM algorithm with parameter optimized by PSO (MSVM-PSO) is accessible to find out a classifier to detect multiclass attacks. This paper will extend the proposed techniques to new computing environments Mobile Ad-Hoc Networks to detect anomalous physical or virtual nodes.

Security and privacy attacks during data communication in Software Defined Mobile Clouds

There has been an enormous growth in networking and data communication between the wireless devices. Cloud computation has taken a giant leap into the mobile domain to cater to the on-going communication demands. The latest programming techniques have also paved the way for incorporating Software Defined Networking techniques in Mobile Cloud environment. The growth of networking infrastructure with many such technological innovations, has led to the parallel increase in security and privacy of data at different levels of communication. The communication links and interfaces have become targeted platform for various security attacks causing denial of services. This survey comprehends an in-depth analysis of the possible data privacy attacks and threats in the proposed Software Defined Mobile Cloud. The possibilities of the attacks which are enlisted focus on various ways of spoofing and flooding of data packets, mis-interpretation and aggregation of the flow rules for communication, packet dropping and traffic flow enrouting at different cross-layered architectural levels built over the heterogeneous networks. An overview of the mechanisms to encounter the various vulnerabilities in the suggested framework is recommended. This article further identifies research directions and challenges to incorporate the possible techniques and preventive solutions, to overcome the malicious attacks in the software defined mobile cloud.

Efficient resourceful mobile cloud architecture (mRARSA) for resource demanding applications

For mobile clients, sufficient resources with the assurance of efficient performance and energy efficiency are the core concerns. This article mainly considers this need and proposes a resourceful architecture, called mRARSA that addresses the critical need in a mobile cloud environment. This architecture consists of cloud resources, mobile devices, and a set of functional components. The performance efficiency evaluates implementing the proposed context-aware multi-criteria decision offloading algorithm. This algorithm considers both device context (network parameters) and application content (task size) at run time when offloading an executable code to allocate the cloud resources. The appropriate resources select based on offloading decisions and via the wireless communication channels. The architecture’s remarkable component is the signal strength analyzer that determines the signal quality (e.g.-60 dBm) and contributes to performance efficiency. The proposed prototype model has implemented several times to monitor the performance efficiency, mobility, performance at communication barriers, and the outcomes of resource-demanding application’s execution. Results indicate performance improvement, such as the algorithm appropriately decides the cloud resources based on device network context, application content, mobility, and the signal strength quality and range. Moreover, the results also show significant improvement in achieving performance and energy efficiency. Sufficient resources and performance efficiency are the most significant features that distinguish this framework from the other existing frameworks.

FairEdge: A Fairness-Oriented Task Offloading Scheme for Iot Applications in Mobile Cloudlet Networks

Mobile cloud computing has emerged as a promising paradigm to facilitate computation-intensive and delay-sensitive mobile applications. Computation offloading services at the edge mobile cloud environment are provided by small-scale cloud infrastructures such as cloudlets. While offloading tasks to in-proximity cloudlets enjoys benefits of lower latency and smaller energy consumption, new issues related to the cloudlets are rising. For instance, unbalanced task distribution and huge load gaps among heterogeneous mobile cloudlets are becoming more challenging, concerning the network dynamics and distributed task offloading. In this paper, we propose ‘FairEdge’, a Fairness-oriented computation offloading scheme to enable balanced task distribution for mobile Edge cloudlet networks. By integrating the balls-and-bins theory with fairness index, our solution promotes effective load balancing with limited information at low computation cost. The evaluation results from extensive simulations and experiments with real-world datasets show that, FairEdge outperforms conventional task offloading methods, and it can achieve a network fairness up to 0.85 and reduce the unbalanced task offload by 50%.