A triangle area based nearest neighbors approach to intrusion detection

Intrusion detection is a necessary step to identify unusual access or attacks to secure internal networks. In general, intrusion detection can be approached by machine learning techniques. In literature, advanced techniques by hybrid learning or ensemble methods have been considered, and related work has shown that they are superior to the models using single machine learning techniques. This paper proposes a hybrid learning model based on the triangle area based nearest neighbors (TANN) in order to detect attacks more effectively. In TANN, the k-means clustering is firstly used to obtain cluster centers corresponding to the attack classes, respectively. Then, the triangle area by two cluster centers with one data from the given dataset is calculated and formed a new feature signature of the data. Finally, the k-NN classifier is used to classify similar attacks based on the new feature represented by triangle areas. By using KDD-Cup ’99 as the simulation dataset, the experimental results show that TANN can effectively detect intrusion attacks and provide higher accuracy and detection rates, and the lower false alarm rate than three baseline models based on support vector machines, k-NN, and the hybrid centroid-based classification model by combining k-means and k-NN.     

JADE implemented mobile multi-agent based,distributed information platform for pervasive health care monitoring

In this paper we aim to implement a highly distributed information infrastructure—MADIP by using Intelligent Agent paradigm, which is able to notify the responsible care-provider of abnormality automatically, offer distance medical advice, and perform continuous health monitoring for those who need it. To confront the issues of interoperability, scalability, and openness in heterogeneous e-health environments, a FIPA2000 standard compliant agent development platform—JADE (Java Agent DEvelopment Framework) was adopted for the design and implementation of the proposed intelligent multi-agent based MADIP system.     

The interparticulate friction behavior of acetal copolymer pellets with bilobal and trilobal cross-sections

The interparticulate friction coefficient has been measured for a range of pellet cross-sections. This measurement is postulated as representing the integrity of the solid bed in plasticating extrusion, reciprocating-screw injection molding and extrusion blow molding. The value of the interparticulate friction coefficient has been found to be consistently greater for profiled pellet cross-sections such as bilobal and trilobal than for conventional pellets with circular cross-sections. It is postulated that this result means that solids beds formed from pellets with profiled cross-sections are less likely to fracture, or break-up, than are solids beds formed with conventional pellets, during plasticating extrusion, reciprocating-screw injection molding and extrusion blow molding. It is further postulated that this will result in surge inhibition, and allow the manufacturing units to run faster with fewer operational problems. The effects of normal load, pellet geometry, polymer molecular weight and agitation of the bed are also presented. In addition to the measurement of the interparticulate friction coefficients, the manufacturing procedures for profiled pellets are explained.