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Research in financial domain has shown that sentiment aspects of stock news have a profound impact on volume trades, volatility, stock prices and firm earnings. In-depth analysis of stock news is now sourced from financial reviews by various social networking and marketing sites to help improve decision making. Nonetheless, such reviews are in the form of unstructured text, which requires natural language processing (NLP) in order to extract the sentiments. Accordingly, in this study we investigate the use of NLP tasks in effort to improve the performance of sentiment classification in evaluating the information content of financial news as an instrument in investment decision support system. At present, feature extraction approach is mainly based on the occurrence frequency of words. Therefore low-frequency linguistic features that could be critical in sentiment classification are typically ignored. In this research, we attempt to improve current sentiment analysis approaches for financial news classification by focusing on low-frequency but informative linguistic expressions. Our proposed combination of low and high-frequency linguistic expressions contributes a novel set of features for sentiment classification. The experimental results show that an optimal Ngram feature selection (combination of optimal unigram and bigram features) enhances sentiment classification accuracy as compared to other types of feature sets. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) has become one of the fastest-growing technologies and has been broadly applied in various fields. IoT networks contain millions of devices with the capability of interacting with each other and providing functionalities that were never available to us before. These IoT networks are designed to provide friendly and intelligent operations through big data analysis of information generated or collected from an abundance of devices in real time. However, the diversity of IoT devices makes the IoT networks’ environments more complex and more vulnerable to various web attacks compared to traditional computer networks. In this article, we propose a novel ensemble deep learning based web attack detection system (EDL-WADS) to alleviate the serious issues that IoT networks faces. Specifically, we have designed three deep learning models to first detect web attacks separately. We then use an ensemble classifier to make the final decision according to the results obtained from the three deep learning models. In order to evaluate the proposed WADS, we have performed experiments on a public dataset as well as a real-word dataset running in a distributed environment. Experimental results show that the proposed system can detect web attacks accurately with low false positive and negative rates. [ABSTRACT FROM AUTHOR]

Redundant and irrelevant features in data have caused a long-term problem in network traffic classification. These features not only slow down the process of classification but also prevent a classifier from making accurate decisions, especially when coping with big data. In this paper, we propose a mutual information based algorithm that analytically selects the optimal feature for classification. This mutual information based feature selection algorithm can handle linearly and nonlinearly dependent data features. Its effectiveness is evaluated in the cases of network intrusion detection. An Intrusion Detection System (IDS), named Least Square Support Vector Machine based IDS (LSSVM-IDS), is built using the features selected by our proposed feature selection algorithm. The performance of LSSVM-IDS is evaluated using three intrusion detection evaluation datasets, namely KDD Cup 99, NSL-KDD and Kyoto 2006+ dataset. The evaluation results show that our feature selection algorithm contributes more critical features for LSSVM-IDS to achieve better accuracy and lower computational cost compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Detection of Denial-of-Service (DoS) attacks has attracted researchers since 1990s. A variety of detection systems has been proposed to achieve this task. Unlike the existing approaches based on machine learning and statistical analysis, the proposed system treats traffic records as images and detection of DoS attacks as a computer vision problem. A multivariate correlation analysis approach is introduced to accurately depict network traffic records and to convert the records into their respective images. The images of network traffic records are used as the observed objects of our proposed DoS attack detection system, which is developed based on a widely used dissimilarity measure, namely Earth Mover’s Distance (EMD). EMD takes cross-bin matching into account and provides a more accurate evaluation on the dissimilarity between distributions than some other well-known dissimilarity measures, such as Minkowski-form distance Lp and X^2 statistics. These unique merits facilitate our proposed system with effective detection capabilities. To evaluate the proposed EMD-based detection system, ten-fold cross-validations are conducted using KDD Cup 99 dataset and ISCX 2012 IDS Evaluation dataset. The results presented in the system evaluation section illustrate that our detection system can detect unknown DoS attacks and achieves 99.95 percent detection accuracy on KDD Cup 99 dataset and 90.12 percent detection accuracy on ISCX 2012 IDS evaluation dataset with processing capability of approximately 59,000 traffic records per second. [ABSTRACT FROM PUBLISHER]

The proliferation of malware has exhibited a substantial surge in both quantity and diversity, posing significant threats to the Internet and indispensable network applications. The accurate and effective classification makes a pivotal role in defending against malware. Numerous approaches employ supervised learning techniques, specifically Convolutional Neural Networks (CNNs), to train feature extractors. However, acquiring a substantial quantity of labeled samples incurs significant expenses, and relying solely on CNNs as feature extractors may result in restricted local receptive fields, consequently compromising the preservation of crucial features. In order to address these constraints, we propose an effective malware classification approach, denoted as MalSort, which leverages the masked self-supervised framework with Swin Transformer. Initially, each instance of malware is transformed into a color image. Furthermore, the Swin Transformer self-supervised framework is utilized to extract multi-scale key feature vectors from a randomly masked partial color image, while the prediction module is employed to predict the masked image. Ultimately, the pre-trained encoder is fine-tuned using the malware dataset to effectively carry out a malware classification task. Our MalSort exhibits a reduced reliance on labeled data samples during the training phase, thereby obviating the necessity for extensive amounts of labeled data. Consequently, the MalSort conserves hardware resources and improve its training efficiency. The experimental results indicate that the MalSort outperforms existing models by achieving a classification accuracy of 97.85%, a recall of 97.63%, a precision of 97.85%, and an F1-score of 97.85% on the BIG2015 dataset. Similarly, on the Malimg dataset, the model achieves percentages of 98.28%, 98.18%, 98.19%, and 98.28% for classification accuracy, recall, precision, and F1-score, respectively. • An asymmetric structure extracts features to handle labeled samples and feature loss. • The fine-tune incorporates class re-balancing to handle imbalance and overfitting. • A designed lightweight module improves convergence and decreases resource dependence. [ABSTRACT FROM AUTHOR]