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Big data analysis for accurate predictions requires adherence to systematic procedures. This study shows an entire data analysis phase from the data collection to model evaluation using the Long Short-Term memory(LSTM) for cryptocurrency price prediction. Three different coin prices are directly collected from the CoinMarketCap in nearly real-time by applying the web scraping technique. The LSTM model trained with this data varying random seed or static seed parameters to find optimal conditions, leading to better accuracy of the LSTM model. Our model evaluated their accuracy in terms of MAE, RMSE, and SMAPE indicators. As a result of this experiment, most of the best candidate parameters are classified at the fixed seed trail in terms of the RMSE for Bit coin, Ethereum, and Lite Coin.

This study shows the effectiveness of anomaly-based IDS using long short-term memory(LSTM) based on the newly developed dataset called UNSW-NB15 while considering root mean square error and mean absolute error as evaluation metrics for accuracy. For each attack, 80% and 90% of samples were used as LSTM inputs and trained this model while increasing epoch values. Furthermore, this model has predicted attack points by applying test data and produced possible attack points for each attack at the 3rd time frame against the actual attack point. However, in the case of an Exploit attack, the consecutive overlapping attacks happen, there was ambiguity in the interpretation of the numerical values calculated by the LSTM. We presented a methodology for training data with binary values using LSTM and evaluation with RMSE metrics throughout this study.

This research aims to propose a model to classify text messages that extracted from the smart phone using forensic software and several machine learning algorithms. The data analysis procedure subdivided into physical extraction, relevant partitions, logical extraction, digital forensic analysis, and text classification. In the text classification step, the final result derived by applying sentiment analysis and k-means clustering algorithm under the control of python application. Through this model, we were able to classify most of the messages correctly as either being positive or negative.

As various wireless devices share the same frequencies in the unlicensed 2.4-GHz industrial scientific medical band, frequency sharing has become a challenging issue in the heterogeneous network. Many Wi-Fi applications increase network traffic and lead to the significant performance loss of other protocol devices including ZigBee for critical missions (e.g., medical devices) in the same band. In this paper, we propose a coexistence solution of the guide busy tone (GBT), providing reliable communications to the ZigBee network under Wi-Fi interference, and present fairness criteria in the tradeoff relation between Wi-Fi and ZigBee with GBT. The proposed GBT design, consisting of a GBT signaler and a busy tone canceller, reserves a channel for ZigBee through the full-duplex technique under heavy Wi-Fi traffic. Our experimental evaluation shows that the packet delivery ratio of the ZigBee network can be improved up to nearly 100% under the saturated Wi-Fi traffic by using GBT, which is scalable for the multinode case as well.

As many security solutions integrated with various technologies have been proposed against eavesdropping attacks, technical advances for adversaries can also pose a serious security threat. This paper considers a problem of smart eavesdropping attacks on multiple-input-multiple-output wiretap channels for a legitimate transceiver. We present a smart eavesdropper model and a cooperative jamming solution between transceivers that can control the jamming signal power to achieve the optimal secrecy performance. In particular, for practical applications, our proposed solution considers the residual self-interference from the full-duplex receiver and the limited cancellation capability of the smart eavesdropper. We derive the secrecy outage probability in general and smart eavesdropper cases, and show numerical results and secrecy regions for evaluation. As a result, our proposed solutions can improve the secrecy performance significantly by exploiting the full-duplex receiver and the cooperative jamming strategies with the sophisticated power control according to power expenditure.

This research proposes the methodology to extract common configuration parameter set by applying multiple benchmarking applications include TeraSort, TestDFSIO, and MrBench on the Hadoop distributed file system. The parameter search space conceptually conducted named Ω(x) to hold status of all parameter values and its evaluation results for every stage to eventually reduce benchmarking cost. In the process of determining parameter set for each stage, one parameter and its associated values selected which is reduced system performance in terms of overall execution time difference that are measured by multiple applications on a Hadoop cluster. The experimental results demonstrate the proposed extended greedy manner provide a feasible benchmark model for the multiple MapReduce tasks. This model classified several candidate parameter value sets that can be reduced the overall execution time by 27% of the values against Hadoop default settings. Moreover, we propose e-heuristic greedy with alternative parameter selection model to evaluate second candidate parameter value which will lead global optimum by returning back to the previous stage if local minimum is not found at the current stage compare to the previous ones.

Performance optimization for mapreduce computing in Hadoop platform is a tedious yet challenging problem due to the complexity of system organization with an extensive list of configuration parameters to be considered. In order to address and resolve this problem, various parameter optimization algorithms are proposed in this research from a naive exhaustive method to a random and a couple of heuristically-based greedy methods to vie with the exponentially nature of the search process for the possible best parameter setting. Extensive benchmark-based experiments have been conducted to validate the performance viability of the mapreduce computations by the benchmark programs such as TestDFSIO, TeraSort, to mention a couple. The experimental results demonstrate the proposed heuristically-based algorithms in greedy manner provide a promising answer to the problem of the research how to optimize the systems configuration parameter set at a computationally viable and feasible cost.

Security has become an increasingly important issue in wireless communications for the IoT (Internet of Things) environments, to which physical layer approaches can contribute by differentiating desired transceiver and wiretap channels for security of confidential data. In this paper, we propose an optimal power allocation strategy for practical physical-layer security, based on friendly jamming with cancellation for anti-eavesdropping. In particular, secrecy outage probability is evaluated in scenarios involving a pair of transmitter-receiver and a passive eavesdropper near the receiver. We derive the optimal power allocation strategy and the required cancellation capability to enhance secrecy performance. Numerical results verify the tradeoff between jamming power ratio and cancellation capability. Furthermore, our proposed scheme can achieve the improved secrecy rate regardless of availability of eavesdropper channel information.