The impact of corporate social responsibility and risk management on financial performance: The case of Vietnamese textile firm

The objective of this study is to assess the impact of corporate social responsibility (CSR) and risk management (RM) on financial performance (FP), and evaluate the moderate role of firm size in the relationship between risk management and financial performance. The study was conducted on a re-search sample of 389 Vietnamese textile firms. The results show that corporate social responsibility (CSR) was an optimal measure to minimize risks and improves financial performance. The good CSR policy reduces corporate risk and improves financial performance. Other way, the bad CSR policy increases corporate risk and impacts negatively on financial performance. In addition, the moderate role of firm size in the relationship between risk management and financial performance is statistically significant

Numerical study and experimental investigation of an electrohydrodynamic device for inertial sensing

We present a multi-physics simulation associated with experimental investigation for an electrohydrodynamic gyroscope based on ion wind corona discharge. The present device consisting of multiple point-ring electrodes generates a synthetic jet flow of ions for inertial sensing applications. Meanwhile the residual charge of jet is neutralized by an external ring electrode to guarantee the ion wind stable while circulating inside the device's channels. The working principle including the generation and then circulation of jet flow within the present device is firstly demonstrated by a numerical simulation and the feasibility and stability of the device are then successfully investigated by experimental work. Results show owing to the ion wind corona discharge based approach associated with new configuration, the present device is robust and consumes low energy

Merosesquiterpenes from marine sponge Smenospongia cerebriformis

Using various chromatography methods, three merosesquiterpenes belonging to sesquiterpene quinone type, neodactyloquinone (1), dactyloquinone D (2), and dactyloquinone C (3) together with two indole derivatives indole-3-aldehyde (4) and indole-3-cacboxylic methyl ester (5) were isolated from the methanol extract of the Vietnamese marine sponge Smenospongia cerebriformis. Their structures were determined by 1D-, 2D-NMR spectra, HR-ESI-MS and in comparison with those reported in the literature. Keywords. Smenospongia cerebriformis, merosesquiterpene, sesquiterpene quinone, indole derivative

Botnet Detection Based On Machine Learning Techniques Using DNS Query Data

In recent years, botnets have become one of the major threats to information security because they have been constantly evolving in both size and sophistication. A number of botnet detection measures, such as honeynet-based and Intrusion Detection System (IDS)-based, have been proposed. However, IDS-based solutions that use signatures seem to be ineffective because recent botnets are equipped with sophisticated code update and evasion techniques. A number of studies have shown that abnormal botnet detection methods are more effective than signature-based methods because anomaly-based botnet detection methods do not require pre-built botnet signatures and hence they have the capability to detect new or unknown botnets. In this direction, this paper proposes a botnet detection model based on machine learning using Domain Name Service query data and evaluates its effectiveness using popular machine learning techniques. Experimental results show that machine learning algorithms can be used effectively in botnet detection and the random forest algorithm produces the best overall detection accuracy of over 90%

Detecting Website Defacements Based on Machine Learning Techniques and Attack Signatures

Defacement attacks have long been considered one of prime threats to websites and web applications of companies, enterprises, and government organizations. Defacement attacks can bring serious consequences to owners of websites, including immediate interruption of website operations and damage of the owner reputation, which may result in huge financial losses. Many solutions have been researched and deployed for monitoring and detection of website defacement attacks, such as those based on checksum comparison, diff comparison, DOM tree analysis, and complicated algorithms. However, some solutions only work on static websites and others demand extensive computing resources. This paper proposes a hybrid defacement detection model based on the combination of the machine learning-based detection and the signature-based detection. The machine learning-based detection first constructs a detection profile using training data of both normal and defaced web pages. Then, it uses the profile to classify monitored web pages into either normal or attacked. The machine learning-based component can effectively detect defacements for both static pages and dynamic pages. On the other hand, the signature-based detection is used to boost the model’s processing performance for common types of defacements. Extensive experiments show that our model produces an overall accuracy of more than 99.26% and a false positive rate of about 0.27%. Moreover, our model is suitable for implementation of a real-time website defacement monitoring system because it does not demand extensive computing resources

A Review on Hot-IP Finding Methods and Its Application in Early DDoS Target Detection

On the high-speed connections of the Internet or computer networks, the IP (Internet Protocol) packet traffic passing through the network is extremely high, and that makes it difficult for network monitoring and attack detection applications. This paper reviews methods to find the high-occurrence-frequency elements in the data stream and applies the most efficient methods to find Hot-IPs that are high-frequency IP addresses of IP packets passing through the network. Fast finding of Hot-IPs in the IP packet stream can be effectively used in early detection of DDoS (Distributed Denial of Service) attack targets and spreading sources of network worms. Research results show that the Count-Min method gives the best overall performance for Hot-IP detection thanks to its low computational complexity, low space requirement and fast processing speed. We also propose an early detection model of DDoS attack targets based on Hot-IP finding, which can be deployed on the target network routers