This paper examines the possible association between financial performance of the firm and economic indicators, corporate governance, ownership structure, capital structure, and risk management. It is also one of the very few examples, which attempts to test various determinant of firm performance in context of developing market (Pakistan). The present study examines the performance of firms in terms of profitability and its association with multiple determinants for 60 Pakistani corporate firms listed in Karachi stock exchange for the period of 2007 to 2011 and attempts to explain the observed behavior with the help of fixed effect model. The results consistently support the potential association between firm's financial performance and economic indicators, corporate governance, ownership structure, and capital structure although the intensity of relationship differs across different measures of performance. We find evidence in support of the hypotheses that a positive association exists between corporate governance, and risk management and performance while mixed results are observed for other variables.
In this paper, we introduce a sequential autoencoder framework using long short term memory (LSTM) neural network for computer network intrusion detection. We exploit the dimensionality reduction and feature extraction property of the autoencoder framework to efficiently carry out the reconstruction process. Furthermore, we use the LSTM networks to handle the sequential nature of the computer network data. We assign a threshold value based on cross-validation in order to classify whether the incoming network data sequence is anomalous or not. Moreover, the proposed framework can work on both fixed and variable length data sequence and works efficiently for unforeseen and unpredictable network attacks. We then also use the unsupervised version of the LSTM, GRU, Bi-LSTM and Neural Networks. Through a comprehensive set of experiments, we demonstrate that our proposed sequential intrusion detection framework performs well and is dynamic, robust and scalable.
In this paper, we execute anomaly detection over the computer networks using various machine learning algorithms. We then combine these algorithms to boost the overall performance. We implement three different types of classifiers, i.e, neural networks, decision trees and logistic regression. We then boost the overall performance of the intrusion detection algorithm using ensemble learning. In ensemble learning, we employ weighted majority voting scheme based on the individual classifier performance. We demonstrate a significant increase in the accuracy through a set of experiments KDD Cup 99 data set for computer network intrusion detection.
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