Online Mining Intrusion Patterns from IDS Alerts

The intrusion detection system (IDS) which is used widely in enterprises, has produced a large number of logs named alerts, from which the intrusion patterns can be mined. These patterns can be used to construct the intrusion scenarios or discover the final objectives of the malicious actors, and even assist the forensic works of network crimes. In this paper, a novel algorithm for the intrusion pattern mining is proposed which aimsto solve the difficult problems of the intrusion action sequence such as the loss of important intrusion actions, the disorder of the action sequence and the random noise actions. These common problems often occur in the real production environment which cause serious performance decrease in the analyzing system. The proposed algorithm is based on the online analysis of the intrusion action sequences extracted from IDS alerts, through calculating the influences of a particular action on the subsequent actions, the real intrusion patterns are discovered. The experimental results show that the method is effective in discovering pattern from the complex intrusion action sequences.

Discovery of Knowledge Using Association Rules in Wireless Sensor Epocs-a Survey

Data mining and knowledge discovery in huge data streams have recently involved in more applications used for decision making. Currently in wireless sensor networks, various mining techniques are used to discover knowledge on sensor data. Applying mining algorithm in wireless sensor data faces many challenges such as continuous arrival of sensor data, fast and huge data arrival, changes of mining results over time, online mining, data transformation, changing network topology, resource constraints and have emerged into various research problems. In Wireless Sensor Database, this paper presents a review on various approaches of association rule mining algorithms using various techniques forming sensor association rules generating frequent patterns to find upcoming sensor events or sensor fault detection or to estimate the missing sensor readings.

Using online mining techniques to inform formative evaluations: An analysis of YouTube video comments about chronic pain

Despite the growing body of research analysing information posted on social media, very few studies have focused on how ‘naturally occurring data’ could inform formative evaluations in health research. This article argues that exploratory data-mining techniques such as descending hierarchical classification, cluster and correspondence analysis could usefully be employed either as stand-alone or mixed methods in the design of needs assessments on health-related issues. To this end, the article reports on the application of text mining techniques to analyse YouTube video comments on chronic pain. The article finds that online forums such as YouTube are packed with information difficult to obtain through traditional research techniques where social desirability and fear of judgement may influence what people are willing to say. It argues that insights gained from social media research could provide important substantive information for health practitioners.

Association Rule Mining Based on Multidimensional Pattern Relations

Most incremental mining and online mining algorithms concentrate on finding association rules or patterns consistent with entire current sets of data. Users cannot easily obtain results from only interesting portion of data. This may prevent the usage of mining from online decision support for multidimensional data. To provide adhoc, query driven, and online mining support, we first propose a relation called the multidimensional pattern relation to structurally and systematically store context and mining information for later analysis.