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Cloud computing is the emerging technology providing IT as a utility through internet. The benefits of cloud computing are but not limited to service based, scalable, elastic, shared pool of resources, metered by use. Due to mentioned benefits the concept of cloud computing fits very well with the concept of m-learning which differs from other forms of e-learning, covers a wide range of possibilities opened up by the convergence of new mobile technologies, wireless communication structure and distance learning development. The concept of cloud computing like any other concept has not only benefits but also introduces myriad of security issues, such as transparency between cloud user and provider, lack of standards, security concerns related to identity, Service Level Agreements (SLA) inadequacy etc. Providing secure, transparent, and reliable services in cloud computing environment is an important issue. This paper introduces a secured three layered architecture with an advance Intrusion Detection System (advIDS), which overcomes different vulnerabilities on cloud deployed applications. This proposed architecture can reduce the impact of different attacks by providing timely alerts, rejecting the unauthorized access over services, and recording the new threat profiles for future verification. The goal of this research is to provide more control over data and applications to the cloud user, which are now mainly controlled by Cloud Service Provider (CSP).
Data is ever increasing in the computing world. Due to advancement of cloud technology the dynamics of volumes of data and its capacity has increased within a short period of time and will keep increasing further. Providing transparency, privacy, and security to the cloud users is becoming more and more challenging along with the volume of data and use of cloud services. We propose a new approach to address the above mentioned challenge by recording the user events in the cloud ecosystem into log files and applying MAR principle namely 1) Monitoring 2) Analyzing and 3) Reporting.
Computing Aggregates on Autonomous, Self-organizing Multi-Agent System: Application "Smart Grid"
(2017)
Decentralized data aggregation plays an important role in estimating the state of the smart grid, allowing the determination of meaningful system-wide measures (such as the current power generation, consumption, etc.) to balance the power in the grid environment. Data aggregation is often practicable if the aggregation is performed effectively. However, many existing approaches are lacking in terms of fault-tolerance. We present an approach to construct a robust self-organizing overlay by exploiting the heterogeneous characteristics of the nodes and interlinking the most reliable nodes to form an stable unstructured overlay. The network structure can recover from random state perturbations in finite time and tolerates substantial message loss. Our approach is inspired from biological and sociological self-organizing mechanisms.
Logging information is more precious as it contains the execution of a system; it is produced by millions of events from simple application logins to random system errors. Most of the security related problems in the cloud ecosystem like intruder attacks, data loss, and denial of service, etc. could be avoided if Cloud Service Provider (CSP) or Cloud User (CU) analyses the logging information. In this paper we introduced few challenges, which are place of monitoring, security, and ownership of the logging information between CSP and CU.
Also we proposed a logging architecture to analyze the behaviour of the cloud ecosystem, to avoid data breaches and other security related issues at the CSP space. So that we believe our proposed architecture can provide maximum trust between CU and CSP.