Siti Hajar Aminah Ali

Hilbert Curve Fractal Antenna for RFID Application

The implementation of radio frequency identification (RFID) involved two main components; the transponder and the reader. The transponder or simply known as a tag comprises of a programmable chip and an antenna. The antenna of the tag has to be of compact size. This paper presents a compact antenna based on the Hilbert curve fractal. The basic antenna is designed at 2.4 GHz, one of the frequencies used in RFID applications. The antenna geometry went through three iterations of fractal process. The designed antennas were then simulated using electromagnetic simulation software. It was observed that a compact Hilbert curve fractal antenna performs well at the desired frequency of operation

A neural network model for detecting DDoS attacks using darknet traffic features.

This paper presents a fast and large-scale monitoring system for detecting one of the major cyber-attacks, Distributed Denial of Service (DDoS). The proposed system monitors the packet traffic on a subnet of unused IPs called darknet. Almost all darknet packets are originated from malicious activities. However, it is not obvious what traffic patterns DDoS attacks have. Therefore, we adopt a classifier and train it with traffic features of known DDoS attacks using 80/TCP and 53/UDP packets which can be labeled based on the header information and payloads. The proposed system consists of the two parts: pre-processing and classifier. In the pre-processing part, darknet packets for 30 seconds are transformed into a feature vector which consists of 17 traffic features on darknet traffic. As for the classifier part, we adopt Resource Allocating Network with Locality Sensitive Hashing (RAN-LSH) in which data to be trained are selected by using LSH and fast online learning is actualized by training only selected data. The learning of RAN-LSH is carried out not only with the training data for 80/TCP and 53/UDP packets but also with new training data labeled by a supervisor. The performance of the proposed detection system is evaluated for 9,968 training data obtained from 80/TCP and 53/UDP packets and 5,933 test data obtained from darknet packets with other protocols and source/destination ports. The results indicate that the proposed system detects backscatter packets caused by DDoS attacks accurately and adapts to new attacks quickly.

An autonomous online malicious spam email detection system using extended RBF network

In this paper, we propose a new online system to detect malicious spam emails and to adapt to the changes of malicious URLs in the body of spam emails by updating the system daily. For this purpose, we develop an autonomous system that learns from double-bounce emails collected at a mail server. To adapt to new malicious campaigns, only new types of spam emails are learned by introducing an active learning scheme into a classifier model. Here, we adopt Resource Allocating Network with Locality Sensitive Hashing (RAN-LSH) as a classifier model with data selection. In this data selection, the same or similar spam emails that have already been learned are quickly searched for a hash table using Locally Sensitive Hashing, and such spam emails are discarded without learning. On the other hand, malicious spam emails are sometimes drastically changed along with a new arrival of malicious campaign. In this case, it is not appropriate to classify such spam emails into malicious or benign by a classifier. It should be analyzed by using a more reliable method such as a malware analyzer. In order to find new types of spam emails, an outlier detection mechanism is implemented in RAN-LSH. To analyze email contents, we adopt the Bag-of-Words (BoW) approach and generate feature vectors whose attributes are transformed based on the normalized term frequency-inverse document frequency. To evaluate the developed system, we use a dataset of double-bounce spam emails which are collected from March 1st, 2013 to August 29th, 2013. In the experiment, we study the effect of introducing the outlier detection in RAN-LSH. As a result, by introducing the outlier detection, we confirm that the detection accuracy is enhanced on average over the testing period.

A fast online learning algorithm of radial basis function network with locality sensitive hashing

In this paper, we propose a new incremental learning algorithm of radial basis function (RBF) Network to accelerate the learning for large-scale data sequence. Along with the development of the internet and sensor technologies, a time series of large data chunk are continuously generated in our daily life. Thus it is usually difficult to learn all the data within a short period. A remedy for this is to select only essential data from a given data chunk and provide them to a classifier model to learn. In the proposed method, only data in untrained regions, which correspond to a region with a low output margin, are selected. The regions are formed by grouping the data based on their near neighbor using locality sensitive hashing (LSH), in which LSH has been developed to search neighbors quickly in an approximated way. As the proposed method does not use all training data to calculate the output margins, the time of the data selection is expected to be shortened. In the incremental learning phase, in order to suppress catastrophic forgetting, we also exploit LSH to select neighbor RBF units quickly. In addition, we propose a method to update the hash table in LSH so that the data selection can be adaptive during the learning. From the performance of nine datasets, we confirm that the proposed method can learn large-scale data sequences fast without sacrificing the classification accuracies. This fact implies that the data selection and the incremental learning work effectively in the proposed method.

Modeling Information Pathway of Motor Control Using Coherence Analysis

Motor behavior can be guided by visual information. For example, hand actions were modified by visual cues that provided initial weight and size estimates of objects. Vision provides important inputs to the representational system which are linked not directly to motor outputs but are linked to cognitive systems. The visual and voluntary motor tasks are controlled by the brain signals which drive the cortical activity and perception. But the cortical activity are not driven by these external stimulus alone, relatively sensory information need to be integrated with various internal constraints such as planned actions, expectations, recent memories, etc. This paper presents C3-EEG coherence analysis during two different visual tasks concurrent with isometric contraction of the first dorsal interosseous (FDI) muscle. The functional connection between different brain regions was investigated to model information pathway of motor control in humans.