Research on heteregeneous data for recognizing threat

The information increasingly large of volume dataset and multidimensional data has grown rapidly in recent years. Inter-related and update information from security communities or vendor network security has present of content vulnerability and patching bug from new attack (pattern) methods. It given a collection of datasets, we were asked to examine a sample of such data and look for pattern which may exist between certain pattern methods over time. There are several challenges, including handling dynamic data, sparse data, incomplete data, uncertain data, and semistructured/unstructured data. In this paper, we are addressing these challenges and using data mining approach to collecting scattered information in routine update regularly from provider or security community

ATLAS: Adaptive Text Localization Algorithm in High Color Similarity Background

One of the major problems that occur in text localization process is the issue of color similarity between text and background image. The limitation of localization algorithms due to high color similarity is highlighted in several research papers. Hence, this research focuses towards the improvement of text localizing capability in high color background image similarity by introducing an adaptive text localization algorithm (ATLAS). ATLAS is an edge-based text localization algorithm that consists of two parts.  Text-Background Similarity Index (TBSI) being the first part of ATLAS, measures the similarity index of every text region while the second, Multi Adaptive Threshold (MAT), performs multiple adaptive thresholds calculation using size filtration and degree deviation for locating the possible text region. In this research, ATLAS is verified and compared with other localization techniques based on two parameters, localizing strength and precision. The experiment has been implemented and verified using two types of datasets, generated text color spectrum dataset and Document Analysis and Recognition dataset (ICDAR). The result shows ATLAS has significant improvement on localizing strength and slight improvement on precision compared with other localization algorithms in high color text-background image

Attack and Vulnerability Penetration Testing: FreeBSD

 Computer system security has become a major concern over the past few years. Attacks, threats or intrusions, against computer system and network have become commonplace events. However, there are some system devices and other tools that are available to overcome the threat of these attacks. Currently, cyber attack is a major research and inevitable. This paper presents some steps of penetration in FreeBSD operating system, some tools and new steps to attack used in this experiment, probes for reconnaissance, guessing password via brute force, gaining privilege access and flooding victim machine to decrease availability. All these attacks were executed and infiltrate within the environment of Intrusion Threat Detection Universiti Teknologi Malaysia (ITD UTM) data set. This work is expected to be a reference for practitioners to prepare their systems from Internet attacks

Ke Arah Impian Menggapai Taraf Universiti Penyelidikan di Malaysia: Kajian Kes Universiti Malaysia Sabah

The Ministry of Higher Education of Malaysia has recently called for all public universities in Malaysia to focus more on research activities. This call is to ensure that a high-quality standard of research can be achieved in order to produce more research universities in the future. Accordingly, Malaysia University of Sabah (UMS), as the ninth public university in Malaysia, has enthusiastically answered this call by encouraging and facilitating its academic staff to actively engage in research activities. UMS, however, realizes that it will take a long process before it can be regarded as a research university. Therefore, this article has identified six pro-active actions that may be taken by UMS to achieve its vision i.e. (1) strengthen physical needs; (2) increase the number of postgraduate students; (3) acquire more highly qualified needs; (3) acquire more highly qualified academicians; (4) enhance international networking; (5) form several research leaders or “Malim Sarjana”; and (6) publish and commercialize research outcomes in highly refereed journals and commercial sectors. Especially the latest, UMS should cooperate in publishing sector with some foreign universities to make its name is acknowledged and well-known by international world. Without the effort of productive and qualified publication, this beautiful and prominent infrastructure will not contribute to establishing a pioneer and superior university in research field

IoT Smart Device for e-Learning Content Sharing on Hybrid Cloud Environment

Centralized e-Learning technology has dominated the learning ecosystem that brings a lot of potential usage on media rich learning materials. However, the centralized architecture has their own constraint to support large number of users for accessing large size of learning contents. On the other hand, Content Delivery Network (CDN) solution which relies on distributed architecture provides an alternative solution to eliminate  bottleneck  access.  Although  CDN  is   an  effective solution, the implementation of technology is expensive and has less impact for student who lives in limited or non-existence internet access in geographical area. In this paper, we introduce an IoT smart device to provide e-Learning access for content sharing on hybrid cloud environment with distributed peer-to- peer communication solution for data synchronization and updates. The IoT smart device acts as an intermediate device between user and cloud services, and provides content sharing solution without fully depending on the cloud server

Text localization in images using reverse thresholds algorithm

High color similarity between text pixels and background pixels is the major problem that causes failure during text localization. In this paper, a novel algorithm, Reverse Thresholds (RT) algorithm is proposed to localize text from the images with various text-background color similarities. First, a rough calculation is proposed to determine the similarity index for every text region. Then, by applying reverse operation, the best thresholds for each text region are calculated by its similarity index. To remove other uncertainties, self-generated images with the same text features but different similarity index are used as experiment dataset. Experiment result shows that RT algorithm has higher localizing strength which is able to localize text in a wider range of similarity index

IoT Botnet Malware Classification Using Weka Tool and Scikit-learn Machine Learning

Botnet is one of the threats to internet network security-Botmaster in carrying out attacks on the network by relying on communication on network traffic. Internet of Things (IoT) network infrastructure consists of devices that are inexpensive, low-power, always-on, always connected to the network, and are inconspicuous and have ubiquity and inconspicuousness characteristics so that these characteristics make IoT devices an attractive target for botnet malware attacks. In identifying whether packet traffic is a malware attack or not, one can use machine learning classification methods. By using Weka and Scikit-learn analysis tools machine learning, this paper implements four machine learning algorithms, i.e.: AdaBoost, Decision Tree, Random Forest, and Naïve Bayes. Then experiments are conducted to measure the performance of the four algorithms in terms of accuracy, execution time, and false positive rate (FPR). Experiment results show that the Weka tool provides more accurate and efficient classification methods. However, in false positive rate, the use of Scikit-learn provides better results

Important Features of CICIDS-2017 Dataset For Anomaly Detection in High Dimension and Imbalanced Class Dataset

The growth in internet traffic volume presents a new issue in anomaly detection, one of which is the high data dimension. The feature selection technique has been proven to be able to solve the problem of high data dimension by producing relevant features. On the other hand, high-class imbalance is a problem in feature selection. In this study, two feature selection approaches are proposed that are able to produce the most ideal features in the high-class imbalanced dataset. CICIDS-2017 is a reliable dataset that has a problem in high-class imbalance, therefore it is used in this study. Furthermore, this study performs experiments in Information Gain feature selection technique on the imbalance class datasaet. For validation, the Random Forest classification algorithm is used, because of its ability to handle multi-class data. The experimental results show that the proposed approaches have a very surprising performance, and surpass the state-of-the-art methods

Time Efficiency on Computational Performance of PCA, FA and TSVD on Ransomware Detection

Ransomware is able to attack and take over access of the targeted user'scomputer. Then the hackers demand a ransom to restore the user's accessrights. Ransomware detection process especially in big data has problems interm of computational processing time or detection speed. Thus, it requires adimensionality reduction method for computational process efficiency. Thisresearch work investigates the efficiency of three dimensionality reductionmethods, i.e.: Principal Component Analysis (PCA), Factor Analysis (FA) andTruncated Singular Value Decomposition (TSVD). Experimental results onCICAndMal2017 dataset show that PCA is the fastest and most significantmethod in the computational process with average detection time of 34.33s.Furthermore, result of accuracy, precision and recall also show that the PCAis superior compared to FA and TSVD

Improving the Anomaly Detection by Combining PSO Search Methods and J48 Algorithm

The feature selection techniques are used to find the most important and relevant features in a dataset. Therefore, in this study feature selection technique was used to improve the performance of Anomaly Detection. Many feature selection techniques have been developed and implemented on the NSL-KDD dataset. However, with the rapid growth of traffic on a network where more applications, devices, and protocols participate, the traffic data is complex and heterogeneous contribute to security issues. This makes the NSL-KDD dataset no longer reliable for it. The detection model must also be able to recognize the type of novel attack on complex network datasets. So, a robust analysis technique for a more complex and larger dataset is required, to overcome the increase of security issues in a big data network. This study proposes particle swarm optimization (PSO) Search methods as a feature selection method. As contribute to feature analysis knowledge, In the experiment a combination of particle swarm optimization (PSO) Search methods with other search methods are examined. To overcome the limitation NSL-KDD dataset, in the experiments the CICIDS2017 dataset used. To validate the selected features from the proposed technique J48 classification algorithm used in this study. The detection performance of the combination PSO Search method with J48 examined and compare with other feature selection and previous study. The proposed technique successfully finds the important features of the dataset, which improve detection performance with 99.89% accuracy. Compared with the previous study the proposed technique has better accuracy, TPR, and FPR