No abstract

No abstract

The growth of edge computing, the Internet of Things (IoT), and cloud computing have been accompanied by new security issues evolving in the information security infrastructure. Recent studies suggest that the cost of insider attacks is higher than the external threats, making it an essential aspect of information security for organizations. Efficient insider threat detection requires stateof-the-art Artificial Intelligence models and utility. Although significant have been made to detect insider threats for more than a decade, there are many limitations, including a lack of real data, low accuracy, and a relatively low false alarm, which are major concerns needing further investigation. In this paper, an attempt to fulfill these gaps by detecting insider threats with the novelties of the present investigation first developed two deep learning hybrid LSTM models integrated with Google's Word2vec LSTM (Long Short-Term Memory) GLoVe (Global Vectors for Word Representation) LSTM. Secondly, the performance of two hybrid DL models was compared with the state-of-the-art ML models such as XGBoost, Ada-Boost, RF (Random Forest), KNN (K-Nearest Neighbor) and LR (Logistics Regression). Thirdly, the present investigation bridges the gaps of using a real dataset, high accuracy, and significantly lower false alarm rate. It was found that ML-based models outperformed the DL-based ones. The results were evaluated based on earlier studies and deemed efficient at detecting insider threats using the real dataset.

CDMA is interference limited multiple access system. Power control is an effective way to reduce co-channel interference. Consequently, it can improve the system capacity. CDMA employs fast closed-loop power control in uplink in which signal-to-interference ratio (SIR) is estimated. Transmitting power is adjusted by comparing estimated SIR with desired SIR. In this paper a modified fixed step power control algorithm has been proposed, which can improve the performance of a CDMA system. The proposed algorithm produced a faster convergence and low outage probability compared to other power control algorithms discussed and analyzed to maintain the desired SIR target.

Contribution of renewable energy in overall power generation is eagerly welcomed by all nations to mitigate the carbon emission. Solar Photovoltaic based power generation is a rapid progressing technology. Although drop in efficiency due to rise in Photovoltaic (PV) module temperature, is yet a significant loss which is highly site dependent. The most common approach does not include natural wind cooling effect while others are not commonly applied to estimate the module temperature during performance evaluation, which leads to error in forecasting, large area requirement for same power generation, more money investment as well as large payback period. Temperature and natural wind cooling highly affects the PV module performance, thus it becomes important to study and evaluate the performance of PV module in local conditions. In this work an attempt is made to observe the effect of natural cooling on PV module performance. The case study includes the performance ratio for simulation and experimental conditions considering artificial cooling. On another hand performance ratio is also evaluated for simulation and experimental conditions considering natural cooling. This study evaluates various errors, invested cost, annual units, annual recovery, payback time and return on investment to emphasize on local site dependent performance. An improved performance for various performance parameters is observed considering the natural cooling effect.