Raihana Ferdous

An efficient k-means algorithm integrated with Jaccard distance measure for document clustering

Document Clustering is a widely studied problem in Text Categorization. It is the process of partitioning or grouping a given set of documents into disjoint clusters where documents in the same cluster are similar. K-means, one of the simplest unsupervised learning algorithms, solves the well known clustering problem following a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed a priori. The main idea is to define k centroids, one for each cluster. This clustering algorithm uses an iterative procedure which converges to one of numerous local minima. We have found that these iterative techniques are especially sensitive to initial starting conditions of the centroid of each cluster and the more the distance among the cluster centroid the better the clustering performance. In simple K-means algorithm the way to initialize the centroid is not specified and one popular way to start is to randomly choose k points of the samples as k centroids but this process does not guarantee to choose the maximum dissimilar documents as the centroid point for k-cluster. In this paper we proposed a modified k-means algorithm which uses Jaccard distance measure for computing the most dissimilar k documents as centroids for k clusters. Our experimental results demonstrate that our proposed K-means algorithm with Jaccard distance measure for computing the centroid improves the clustering performance of the simple K-means algorithm.

Augustus: a CCN router for programmable networks

Despite the considerable attention that the ICN paradigm received so far, its deployment has been hindered by the scale of upgrades required to the existing infrastructure. Software programmable networking frameworks would constitute a remarkable opportunity for ICN as they enable fast deployment of novel technologies on commodity hardware. However, a software ICN router implementation for commodity platforms guaranteeing adequate packet processing performance is not available yet. This paper introduces Augustus, a software architecture for ICN routers, and detail two implementations, stand-alone and modular, released as open-source code. We deployed both implementations on a state-of-the-art hardware platform and analyzed their performance under different configurations. Our analysis shows that with both implementations it is possible to achieve a throughput of approximately 10 Mpps, saturating 10 Gbit/s links with packet as small as 100 bytes. However, to achieve such performance, routers must be carefully configured to fully exploit the capabilities of the hardware platforms they run on.

Classification of SIP messages by a syntax filter and SVMs

The Session Initiation Protocol (SIP) is at the root of many sessions-based applications such as VoIP and media streaming that are used by a growing number of users and organizations. The increase of the availability and use of such applications calls for careful attention to the possibility of transferring malformed, incorrect, or malicious SIP messages as they can cause problems ranging from relatively innocuous disturbances to full blown attacks and frauds. To this end, SIP messages are analyzed to be classified as “good” or “bad” depending on whether this structure and content are deemed acceptable or not. This paper presents a classifier of SIP messages based on a two stage filter. The first stage uses a straightforward lexical analyzer to detect and remove all messages that are lexically incorrect with reference to the grammar that is defined by the protocol standard. The second stage uses a machine learning approach based on a Support Vector Machine (SVM) to analyze the structure of the remaining syntactically correct messages in order to detect semantic anomalies which are deemed a strong indication of a possibly malicious message. The SVM “learns” the structure of the “good” and “bad” SIP messages through an initial training phase and the SVM thus configured correctly classifies messages produced by a synthetic generator and also “real” SIP messages that have been collected from the communication network at our institution. The preliminary results of such classification look very promising and are presented in the final section of this paper.

Efficient testing environment for wireless networking experiments

Wireless testing depends on the wireless test environments where test environment refers to the setup or environment into which the device being tested and the test equipment are placed. Four primary types of test environments are Faraday Cages, RF Chambers, Cabled and Open air. Most wireless testing is conducted in indoor environment which is veritable RF jungles, especially when we consider the ISM bands. It is important to understand the strengths and weaknesses of the range of wireless test environments because the impact of the wireless environment is often underestimated. But in reality wireless environment is responsible for producing different results of an experiment. We tried to find out a perfect test environment for wireless testing specially when they venture into the RF jungle. Our aim is to find out a testbed which provide better control over the RF environment to experimenters.

An efficient voice over internet telephone call network (TCN) model based on user perspective for analyzing the dynamic behavior of the telephone calls

An appropriate telephone call network (TCN) model is very important for any VoIP provider to analyze the practical scenario of the telephone network. Telephone call network model on user perspective is another significant research area as this type of model is particularly useful in predicting the dynamics of the telephone calls during incidents like earthquakes or typhoons, when there is a sudden surge in the number of telephone calls. Again telephone call network (TCN) model on user perspective plays significant importance in detecting spam call. It is found that in a telephone network, the call distribution over the user is not uniform. Some users originate or receive more calls than others. Again, real-life data have proved that the number of calls received by a user in a network has a power-law property. Another important perspective is that usually the receiver of a call is found from the neighbor group of the caller as people generally make call to others whom they know. In this paper, we have analyzed and develop the appropriate telephone call network model considering all these real-life user behavior in a VoIP telephone network.