With the advancement in digital forensics, digital forensics has been evolved in Cloud computing. A common process of digital forensics mainly includes five steps: defining problem scenario, collection of the related data, investigation of the crime scenes, analysis of evidences and case documentation. The conduction of digital forensics in cloud results in several challenges, security, and privacy issues. In this paper, several digital forensics approaches in the context of IoT and cloud have been presented. The review focused on zone-based approach for IoT digital forensics where the forensics process is divided into three zones. Digital forensics in cloud provides the facilities of large data storage, computational capabilities and identification of criminal activities required for investigating forensics. We have presented a brief study on several issues and challenges raised in each phase of Cloud forensics process. The solution approaches as well as advancement prospects of cloud forensics have been described in the light of Blockchain technology. These studies will broaden the way to new researchers for better understanding and devising new ideas for combating the challenges. 

All new mobile radio communication systems undergo a cautious cellular network planning and re-planning process in order to resourcefully utilize the allotted frequency band and also ensure that the geographical area of focus is adequately fortified with integrated base stations transmitters. To this end, efficient radio propagation model prediction and tuning is of huge importance, as it assists radio network engineers to effectively assess and plan the cellular network signal coverage area. In this research work, an adaptive least absolute deviation approach is proposed and verified to fine-tune the parameters of Ericsson propagation model. The adaptive tuning technique have been verified experimentally with field propagation loss data acquired over three different suburban locations of a recently deployed LTE radio cellular network in Waterlines area of Port Harcourt City. In terms of the mean absolute percentage error and coefficient of efficiency, the outcomes of the proposed adaptive tuning approach show a higher degree of prediction performance accuracy on the measured loss data compared to the commonly applied least squares regression tuning technique.

External communications clients seek higher communication levels, better speech quality and higher network limitations due to limited radio recurrence spectrum connectivity, bandwidth, channel efficiency, physical zones and transmitting problems induced by factors such as blurring (Fading) and multi-way bending (Distortion). Fading is a significant impedance of the remote communication tube. Within this article, we find different procedures for alleviating the fading issue in the remote system. The simple solution to the fading problem will be to have the fading edge of the transmitter. In any scenario, this is not a fruitful agreement in any way whatsoever. One exchange agreement is beneficial to take a stance on the truthful behaviour of the deteriorating outlet. There follows the basic concept of good variety; where at least two inputs from the recipient are required to receive uncorrelated signals.

Artificial Neural Networks have been widely used in security and privacy domains for alleviating the issues of malicious attacks. Several embedded codes like Visual Basic for Application Macros are reasonably powerful scripts that can help to automate iterative processes in word processing documents. It has been observed that, unethical hackers exploit these embedded scripts for their malicious intents. Since most of the Microsoft Word users are unaware of such malicious attacks because they are layman end users and mistakenly considers less suspicious contents. And therefore, these hackers prefer to use Microsoft Office documents as most vulnerable items for or Attack vectors. As a general approach, non-executable files are assumed to be less vulnerable than executable files. This implies that these document files could provide an easy and convenient exploitable pathway that can allow hackers to execute their intended malicious actions on the victim’s machine. This research paper presents an automatic detection of malicious embedded codes in general and Microsoft Office documents as a specific case for experimental analysis. This research paper considered only malicious behavior of the embedded codes i.e. checks the status of inclusion or exclusion of the executable code. The malicious datasets are developed to create a knowledgebase where documents are pre-processed. Thereafter the data sets are disassembled using reverse engineering and then malicious features are extracted from the documents. In this research paper, nineteen different malicious keys were extracted. Later, feature reduction technique was applied. Based upon actions; these malicious keys were reduced to eight behaviors. Finally, a machine is trained using artificial neural network with eight input features; extracted from individual disassembled scripts. Afterwards, output nodes that represent malicious or benign behavior classify the existence of attack i.e. exists or does not exists. Based on the training model, a total of seven hundred ninety-two samples of documents were tested. Finally, the research has achieved an average accuracy of 92.2% in the identification of maliciousness of embedded codes in Microsoft Office documents as a case. This result shows that the proposed system has high accuracy in detecting malicious Embedded in word processing documents.