Problem statement: Flaws either in users' implementation of a network or in the standard specification of protocols has resulted in gaps that allow various kinds of network attack to be launched. Of the kinds of network attacks, denial-of-service flood attacks have caused the most severe impact. Approach: This study reviews recent researches on flood attacks and their mitigation, classifying such attacks as either high-rate flood or low-rate flood. Finally, the attacks are compared against criteria related to their characteristics, methods and impacts. Results: Denial-of-service flood attacks vary in their rates, traffic, targets, goals and impacts. However, they have general similarities that are the methods used are flooding and the main purpose is to achieve denial of service to the target. Conclusion/Recommendations: Mitigation of the denial-of-service flood attacks must correspond to the attack rates, traffic, targets, goals and impacts in order to achieve effective solution.

A statistical approach to automatic speech recognition using the atomic speech units constructed from overlapping articulatory features Abstract. A novel method is proposed to recognize the Arab/Jawi and Roman digits. This new method is based on features from the triangle geometry, normalized into nine features. The features are used for zoning which results in five and 25 zones. The algorithm is validated by using three standard datasets which are publicly available and used by researchers in this field. The first dataset is HODA that contains 60,000 images for training and 20,000 images for testing. The second dataset is IFHCDB. This dataset has 52,380 isolated characters and 17,740 digits. Only the 17,740 images of digits are used for this research. For the roman digit, MNIST are chosen. MNIST dataset has 60,000 images for training and 10,000 images for testing. Supervised (SML) and Unsupervised Machine Learning (UML) are used to test the nine features. The SML used are Neural Network (NN) and Support Vector Machine (SVM). Whereas the UML uses Euclidean Distance Method with data mining algorithms; namely Mean Average Precision (eMAP) and Frequency Based (eFB). Results for SML testing for HODA dataset are 98.07% accuracy for SVM, and 96.73% for NN. For IFHCDB and MNIST the accuracy are 91.75% and 93.095% respectively. For the UML tests, HODA dataset is 93.91%, IFHCDB 85.94% and MNIST 86.61%. The train and test images are selected using both random and the original dataset's distribution. The results show that the accuracy of proposed algorithm is over 90% for each SML trained datasets where the highest result is the one that uses 25 zones features.

No abstract

No abstract

University is required to keep printed documents for a certain duration. Documents are evidence, for instance, student’s proof of payment or final exams script, which need to be stored in a safe place within five years or more. The creation of a physical or printed document follows the standard of the International Organization for Standardization (ISO) due to quality management and interrelation between universities for sharing information. However, an inventive way of ISO documentation for storing the evidence from loss and document misallocation is highly requested. Therefore, to overcome the document misallocation problem, an Internet-of-Things (IoT) based file tracking prototype is implemented. The prototype devices consist of Radio-frequency Identification (RFID) that has two parts, which are RFID reader and RFID tag, Arduino Uno, and mobile application (Android). RFID reader scans the information from the RFID tags located at the ISO document’s file. The RFID reader is integrated with Arduino to establish a connection to the network to communicate with mobile applications (Android). This method tracked the current location of the ISO document’s file. The result shows that the ISO document’s file is located in the respective area. Finally, the impact of this research brings efficiency in searching the ISO document’s file in the university.