Neighborhood Random Classification

International audienceEnsemble methods (EMs) have become increasingly popular in data mining because of their efficiency. These methods(EMs) generate a set of classifiers using one or several machine learning algorithms (MLAs) and aggregate them into a single classifier (Meta-Classifier, MC). Amon MLAs, k-Nearest Neighbors (kNN) is one of the most known used in the context of EMs. However, handling the parameter k might be difficult. This drawback exists almost for all MLA that are instances based. Here, we propose an approach based on neighborhood graphs as alternative. Thanks to theses related graphs, like relative neighborhood graphs (RNGs) or Gabriel graphs (GGs), we provide a generalized approach with less arbitrary parameters. Introducing neighborhood graphs in EMs approaches has never been done before. The results of our algorithm : Neighborhood Random Classification are very promising since they are equal to the best EMs approaches such as Random Forest or those based on SVMs. In this exploratory and experimental work, we provide the methodological approach and we provide many comparison results

A Real-Time Intrusion Detection and Protection System at System Call Level under the Assistance of a Grid

Part 2: The 2014 Asian Conference on Availability, Reliability and Security, AsiaARES 2014International audienceIn this paper, we propose a security system, named the Intrusion Detection and Protection System (IDPS for short) at system call level, which creates personal profiles for users to keep track of their usage habits as the forensic features, and determines whether a legally login users is the owner of the account or not by comparing his/her current computer usage behaviors with the user’s computer usage habits collected in the account holder’s personal profile. The IDPS uses a local computational grid to detect malicious behaviors in a real-time manner. Our experimental results show that the IDPS’s user identification accuracy is 93%, the accuracy on detecting its internal malicious attempts is up to 99% and the response time is less than 0.45 sec., implying that it can prevent a protected system from internal attacks effectively and efficiently

Dimensions and metrics for evaluating recommendation systems

Recommendation systems support users and developers of various computer and software systems to overcome information overload, perform information discovery tasks, and approximate computation, among others. They have recently become popular and have attracted a wide variety of application scenarios ranging from business process modeling to source code manipulation. Due to this wide variety of application domains, different approaches and metrics have been adopted for their evaluation. In this chapter, we review a range of evaluation metrics and measures as well as some approaches used for evaluating recommendation systems. The metrics presented in this chapter are grouped under sixteen different dimensions, e.g., correctness, novelty, coverage. We review these metrics according to the dimensions to which they correspond. A brief overview of approaches to comprehensive evaluation using collections of recommendation system dimensions and associated metrics is presented. We also provide suggestions for key future research and practice directions