Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm

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

Studying Health Outcomes in Farmworker Populations Exposed to Pesticides

A major goal of studying farmworkers is to better understand how their work environment, including exposure to pesticides, affects their health. Although a number of health conditions have been associated with pesticide exposure, clear linkages have yet to be made between exposure and health effects except in cases of acute pesticide exposure. In this article, we review the most common health end points that have been studied and describe the epidemiologic challenges encountered in studying these health effects of pesticides among farmworkers, including the difficulties in accessing the population and challenges associated with obtaining health end point data. The assessment of neurobehavioral health effects serves as one of the most common and best examples of an approach used to study health outcomes in farmworkers and other populations exposed to pesticides. We review the current limitations in neurobehavioral assessment and strategies to improve these analytical methods. Emerging techniques to improve our assessment of health effects associated with pesticide exposure are reviewed. These techniques, which in most cases have not been applied to farmworker populations, hold promise in our ability to study and understand the relationship between pesticide exposure and a variety of health effects in this population

Intrusion detection method based on nonlinear correlation measure

Cyber crimes and malicious network activities have posed serious threats to the entire internet and its users. This issue is becoming more critical, as network-based services, are more widespread and closely related to our daily life. Thus, it has raised a serious concern in individual internet users, industry and research community. A significant amount of work has been conducted to develop intelligent anomaly-based intrusion detection systems (IDSs) to address this issue. However, one technical challenge, namely reducing false alarm, has been along with the development of anomaly-based IDSs since 1990s. In this paper, we provide a solution to this challenge. A nonlinear correlation coefficient-based (NCC) similarity measure is proposed to help extract both linear and nonlinear correlations between network traffic records. This extracted correlative information is used in our proposed IDS to detect malicious network behaviours. The effectiveness of the proposed NCC-based measure and the proposed IDS are evaluated using NSL-KDD dataset. The evaluation results demonstrate that the proposed NCC-based measure not only helps reduce false alarm rate, but also helps discriminate normal and abnormal behaviours efficiently

Enrichment pattern of leachable trace metals in roadside soils of Miri City, Eastern Malaysia

This article presents the results on distribution and enrichment pattern of acid-leachable trace metals (ALTMs) from roadside soil of Miri city, Sarawak, East Malaysia. The city is one of the fastest developing in the Malaysian region with huge petroleum resources. ALTMs Fe, Mn, Cr, Cu, Ni, Co, Pb, Zn and Cd along with organic carbon and carbonates (CaCO3) were analyzed in 37 soil sediments collected from roadside. The enrichment of ALTMs [especially Cu (0.4-13.1 µg g-1), Zn (9.3-70.7 µg g-1), Pb (13.8-99.1 µg g-1)] in the roadside soils indicate that these metals are mainly derived from sources related to traffic exhausts, forest fires and oil refineries. The comparative study and enrichment pattern of elements indicates that Mn, Cu, Zn and Pb are enriched multi-fold than the unpolluted soil and Ni, Pb, Cd in some samples compared to Sediment Quality Guidelines like Lowest Effect Level (LEL) and Effects Range Low (ERL) in the region which is mainly due to the recent industrial developments in the region. © 2014 Springer-Verlag Berlin Heidelberg