Anomaly Intrusion Detection based on Concept Drift

Nowadays, security on the internet is a vital issue and therefore, intrusion detection is one of the major research problems for networks that defend external attacks. Intrusion detection is a new approach for providing security in existing computers and data networks. An Intrusion Detection System is a software application that monitors the system for malicious activities and unauthorized access to the system. An easy accessibility condition causes computer networks vulnerable against the attack and several threats from attackers. Intrusion Detection System is used to analyze a network of interconnected systems for avoiding uncommon intrusion or chaos. The intrusion detection problem is becoming a challenging task due to the increase in computer networks since the increased connectivity of computer systems gives access to all and makes it easier for hackers to avoid their traces and identification. The goal of intrusion detection is to identify unauthorized use, misuse and abuse of computer systems. This project focuses on algorithms: (i) Concept Drift based ensemble Incremental Learning approach for anomaly intrusion detection, and (ii) Diversity and Transfer-based Ensemble Learning. These are highly ranked anomaly detection models. We study and compare both learning models. The Network Security Laboratory-Knowledge Discovery and Data Mining (NSL-KDD99) dataset have been used for training and to detect the misuse activities

A biosensor assay for the detection of Mycobacterium avium subsp. paratuberculosis in fecal samples

A simple, membrane-strip-based lateral-flow (LF) biosensor assay and a high-throughput microtiter plate assay have been combined with a reverse transcriptase polymerase chain reaction (RT-PCR) for the detection of a small number (ten) of viable Mycobacterium (M.) avium subsp. paratuberculosis (MAP) cells in fecal samples. The assays are based on the identification of the RNA of the IS900 element of MAP. For the assay, RNA was extracted from fecal samples spiked with a known quantity of (101 to 106) MAP cells and amplified using RT-PCR and identified by the LF biosensor and the microtiter plate assay. While the LF biosensor assay requires only 30 min of assay time, the overall process took 10 h for the detection of 10 viable cells. The assays are based on an oligonucleotide sandwich hybridization assay format and use either a membrane flow through system with an immobilized DNA probe that hybridizes with the target sequence or a microtiter plate well. Signal amplification is provided when the target sequence hybridizes to a second DNA probe that has been coupled to liposomes encapsulating the dye, sulforhodamine B. The dye in the liposomes provides a signal that can be read visually, quantified with a hand-held reflectometer, or with a fluorescence reader. Specificity analysis of the assays revealed no cross reactivity with other mycobacteria, such as M. avium complex, M. ulcerans, M. marium, M. kansasii, M. abscessus, M. asiaticum, M. phlei, M. fortuitum, M. scrofulaceum, M. intracellulare, M. smegmatis, and M. bovis. The overall assay for the detection of live MAP organisms is comparatively less expensive and quick, especially in comparison to standard MAP detection using a culture method requiring 6-8 weeks of incubation time, and is significantly less expensive than real-time PCR

GO-PEAS: a scalable yet accurate grid-based outlier detection method using novel pruning searching techniques

In this paper, we propose a scalable yet accurate grid-based outlier detection method called GO-PEAS (stands for Grid-based Outlier detection with Pruning Searching techniques). Innovative techniques are incorporated into GO-PEAS to greatly improve its speed performance, making it more scalable for large data sources. These techniques offer efficient pruning of unnecessary data space to substantially enhance the detection speed performance of GO-PEAS. Furthermore, the detection accuracy of GO-PEAS is guaranteed to be consistent with its baseline version that does not use the enhancement techniques. Experimental evaluation results have demonstrated the improved scalability and good effectiveness of GO-PEAS

A Re-Evaluation of Past to Present-Day Use of the Blissful Neuronal Nutraceutical “Cannabis”

The chronological events associated with cannabis utilization are viewed perceptively as matters/issues that happened from the period “before Christ” (BC) or “anno domini” (AD, “in the year of our Lord”) to the present time. Cannabis is one of the oldest natural products known to humanity worldwide and has been used by various civilizations, cultures, and religions before the birth of Christ. Interestingly, it is used to date and has the potential for future usage for centuries to come. Currently, the major religions around the world are Christianity, Islamism, Hinduism, Buddhism, Sikhism, Taoism, Judaism, Confucianism, Bahá'í, Shinto, Jainism, and Zoroastrianism, with their precepts regarding the use of cannabis products. Similarly, the most noted ancient civilizations, including the Incan Civilization, Aztec Civilization, Roman Civilization, Ancient Greek Civilization, Chinese Civilization, Mayan Civilization, Ancient Egyptian Civilization, Indus Valley Civilization, and Mesopotamian Civilization, have reported cannabis use. This review discusses cannabis in various civilizations and religions from the past to the present day.</jats:p