In the notion of communication system resource provision specifically, beam-forming is a concept of proficient utilization of the power of transmission. Network densification and massive MIMO allows us to control the power efficiency and can be effectively distributed among different users by reducing cost. We presented a practical scenario for the performance of massive MIMO and multi-small cell system to analyze the overall performance of the system. Our work is based on the resource allocation with optimal structural constraints to maintain the cost effectiveness while considering economic implications. The base stations located far away from the users receive attenuated signals and give rise to path loss, whereas the problems of inter cell interference also arise due to transmission from a base station to others cells. The performance of the cellular system can be enhanced with the combination of massive Mimo and small cells, where we simulate and also provide an analysis on practical system with optimal and low complexity beam-forming. The proposed scenario illustrates a structure with an optimal linear transmit beamforming regarding an efficient number of parameters to not lose optimality, which is extendable to designate any specific cellular network in consideration. Our approach exploited schemes with low complexity that are facilitating in complete solution formation, and tested them in various and all possible cases and scenarios.

Sensitive Data requires encryption before uploading to a public cloud. Access control based on Attribute-Based Encryption (ABE) is an effective technique to ensure data shared security and privacy in the public cloud. Cipher-Text Policy of ABE may suffer from scalability and performance issues as they do not permit for addition or removal of computing nodes at run time. Furthermore, another problem is existing approaches suffer from single-point-of-failure (SPoF). Therefore, we introduce a scalable multi-agent system architecture based on CP-ABE to ensure data sharing on public cloud storage and reliability in our proposed work. We proposed a cloud host as an inter-mediator between the user and the authorized agents without violating the system’s privacy and security. We have also proposed a novel methodology to protect the cloud from malware by exploiting the state’s efficient power of the art Gemini approach. Gemini is an efficient methodology for binary code-based graph embedding similarity detection. Our proposed study overcomes the deficiencies of scalability and efficiency along with providing the mechanism for malware detection in the cloud. It covers three aspects: scalability, the efficiency with multi-agents, and malware detection capability. Our contributed work is scalable, efficient for cloud data sharing, and protects from malware. Results reveal that our work provides better performance with preserving the security, privacy, and fine granularity features of CP-ABE and malware screening using regress analysis by the graph embedding technique.

Online social networks (OSNs) have reduced global boundaries, with Twitter enabling perspective sharing. Bot profile‐propagated false information misuse raises serious concerns. Considering this issue, we present our research on classifying Twitter accounts as “human” or “bot” using deep neural networks and transfer learning. Our proposed approach, TL‐PBot, stands for bot profile detection using transfer learning. The TL‐PBot framework utilizes Twitter account metadata such as follower count. Our TL‐PBot also incorporates text data from the Twitter description field as a feature. Word representation of the text data is achieved using Global Vectors (GloVe), a pre‐trained model. By employing user profile‐based features, we significantly reduce the overhead of feature engineering. The hybrid nature of the model enables it to effectively handle mixed‐type features, including text, binary, and numerical data. We design the network using long‐short‐term memory (LSTM) units. DNN model layers were trained, and the weights of the pre‐trained model layers were frozen to apply the transfer learning, resulting in reduced training time and improved bot profile detection accuracy. The performance of the proposed TL‐PBot is evaluated using publicly available datasets. The proposed approach is trained and tested on the same datasets and further evaluated on the validation datasets that were not used in the training phase, which is also a novelty in our approach. Comparative analysis with state‐of‐the‐art approaches demonstrates that the TL‐PBot approach achieves a higher accuracy of 98.07%, while excelling in precision of 99%, recall of 98%, f measure of 98.32%, and AUC of 0.99. Employing the transfer learning strategy resulted in an accelerated detection rate of 5.04 milliseconds, attesting to the effectiveness of this approach in enhancing computational efficiency.

Software testing lies in the validation which ensures that the implementation satisfies the client's requirements. There are several techniques on software testing some are implementation based while others are model based. We have proposed the Petri net model based testing technique for testing the software. Although UML is the de-facto standard for the software development however most of the UML diagrams supports static behavior while Petri nets supports dynamic behavior i.e. we can represent concurrent behavior of objects in case of object oriented paradigm. In our case we have also selected object oriented paradigm as it is well established paradigm and provides ease in development and it is more realistic. We have proposed a regression based testing technique using Petri nets. Regression testing technique is applied on the Delta version i.e. changed version of the software. Our technique has efficient mechanism of reducing the test suite for the delta version on the basis of analysis of the baseline version. Petri net models are used in this paper for analyzing the baseline and delta version.

The Android malware has various features and capabilities. Various malware has distinctive characteristics. Ransomware threatens financial loss and system lockdown. This paper proposes a threat-assessing approach using the Grey Wolf Optimizer (GWO) to train and tune the Adaptive Neuro-Fuzzy Inference System (ANFIS) to categorize Android malware accurately. GWO improves efficiency and efficacy in ANFIS training and learning for Android malware feature selection and classification. Our approach categorizes Android malware as a high, moderate, or low hazard. The proposed approach qualitatively assesses risk based on critical features and threats. Our threat-assessing mechanism’s scale categorizes Android malware. The proposed approach resolves the issue of overlapping features in different types of malware. Comparative results with other classifiers show that the ensemble of GWO is effective in the training and learning process of ANFIS and thus achieves 95% F-score, 94% specificity, and 94% accuracy. The ensemble makes fast learning possible and improves classification accuracy.