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Information technology is progressively used by end users, business, and government on the different device models with various operating systems in all over the world. The widespread usage of information technology could create the environment that is potentially conducive to malicious activities and illegal operations. Forensic investigation on heterogeneous infrastructure via client-server architecture becomes an emerging requirement field for forensic practitioners in Myanmar. There is a need for the investigation research of locating and documenting the data remnants on diverse platforms to trace the criminal activities. Nowadays, we need to develop cyber forensics tool that is easy to use for non-technical forensic investigator. The purpose of this paper is to present the importance for the study of cyber forensics facing against cyber-crime. The discovered data remnants can provide the forensic examiners generating the effective evidences in real-world forensics. The discovered data remnants can lead the forensic data reduction. We proposed applicable framework and tool (MYANFOSIS) for cyber-crime dynamic forensics investigation in Myanmar.

In many organizations, it is vital to protect sensitive and confidential data in today’s digital world. For this case such information is made as an open database system; businesses can face legal or financial ramifications. According to the various nature and structures of the organizations, they may not have the same defense and interrelated communities which consist of military services, bank, and other intelligence organizations. All kinds of organizations must set policies to systematically and consistently categorize their data security from theft and threats. There are many types of access control mechanisms depending on the nature of organization such as mandatory access control (MAC), role-based access control (RBAC), and discretionary access control (DAC). This proposed framework focuses on the common threats that can face in the multilevel organizations, the security mechanisms to be considered, and how to secure a database. Multilevel security system allows users with different clearance levels, authorization, and need to know ability to process information in the same system. Moreover, the technical security controls such as user authentication, file access authorization, data encryption, and privacy controls for each user are applied for this framework. The proposed system will present the implementation of effective security framework for multilevel organizations using Strict Integrity Policy by labeling clearances levels to system’s users and classification levels to system’s stored data.

Each and every day, malicious software writers continue to create new variants, new innovation, new infection, and more obfuscated malware. Malicious software classification and detection play an important role and big challenge for cyber security research. Due to the increasing rate of false alarm, the accurate classification and detection of malware is a big necessity issue to be solved. This approach provides the classification system to differentiate malware from cleanware. This paper also contributes the prominent feature extraction for Windows API (application programming interface) calls and important feature selection to discriminate malware and cleanware. Chi-square and principal component analysis (PCA) attribute selection methods have been applied in this proposed system. N-gram approach is also applied to construct the sequence of malware API features. Classification algorithms like K-nearest neighbor and random forest (RF) are used for classifying malware and cleanware executable files. The proposed system provides the accuracy of 99% on unigram- and bigram-selected API features using χ2 and PCA. The proposed approach is able to identify the malicious executable files and cleanware effectively.

Today universal cloud computing and digital systems would not exist without virtualization and hypervisors. These are basic tools to support varying demands without sluggish, expensive physical reconfiguration of hardware and heavy software stack investment. On the other hand, virtualization is the main component in cloud and suffers from many security vulnerabilities. Understanding the virtualization vulnerabilities will help the organizations in reducing the risk. The security of a virtualization is mainly dependent on the hypervisor. According to the domino effect, if one of the virtual management environments is compromised, all virtual machines (VMs) can be easily compromised. To demonstrate the weakness of hypervisor environment, hypervisor hijacking and some handling techniques are discussed with popular open-source type 1 and type 2 hypervisor vulnerabilities. Finally, the system presents how to handle the virtualization attacks with user awareness vulnerabilities, avoiding methods, and behavior-based hypervisor detecting method.

The era of the Internet of Things (IoT) is very rapidly developing with millions of devices that are useful in the smart home, smart city, and many other smart systems for education, organization and so on. On the other side, attackers are mostly targeting these devices. After infecting the malware attacks on these devices, they become bots that are controlled by attackers, and these will be targeted to the organizations not only for stealing important information but also for breaking down the network. Although some security mechanisms have developed to protect against cyber-attacks, most such systems are rule-based systems, like public IDS systems. And also, the formal rule-based detection could be circumvented by the malware attackers’ knowledge. Therefore, the machine learning-based detection scheme is the replacement for the lack of previous detection techniques. The proposed detection architecture is based on machine learning methodology, like the CART algorithm and public IDS dataset, named N-BaIoT. The experimental results indicate that the detection accuracy of the selected classifier, CART is significantly better than that of the Naïve Bayes classifier, and the overall detection rate using CART is reached up to 99%.

Communication technology and devices are timely changing and increasing various levels and different operating systems in all over the world. As the threats of cyberspace continue to become more hyperconnected it will be imperative that cybercrime has become a reality in the developing countries. Nowadays, our country is just developing country and most of people aren’t friendly to use the modern communications system and digital devices. The purpose of this paper is to present the importance for the study of offensive and forensic investigation on cyber space facing against cybercrime using HACKFOSICS forensics tool. There is a need for forensic examiners to investigate the locating and documenting the remnant data on various digital devices to trace the criminal offensive activities. We proposed forensics framework, process flow and (HACKFOSICS) tool for cybercrime forensics investigation.

Due to the rapid growth of targeted malware attacks, malware analysis and family classification are important for all types of users such as personal, enterprise, and government. Traditional signature-based malware detection and anti-virus systems fail to classify the new variants of unknown malware into their corresponding families. Therefore, we propose malware family classification system for 11 malicious families by extracting their prominent API features from the reports of enhanced and scalable version of cuckoo sandbox. Moreover, the proposed system contributes feature extraction algorithm, feature reduction and representation procedure for identifying and representing the extracted feature attributes. To classify the different types of malicious software Random Forest (RF), K-Nearest Neighbor (KNN), and Decision Table (DT) machine learning multi-class classifiers have been used in this system and RF and KNN classifiers provide 95.8% high accuracy in malware family classification.

Ciphering algorithms play a main role in this digital era, especially when data are transferred via internet. Many algorithms has been developed and used to encrypt and decrypt data for secure storing or transmission. At present, both synchronous and asynchronous encryptions are used to achieve the high security and to speed up the encryption time and process. Advanced Encryption Standard (AES) plays a prominent role among the synchronous encryption algorithms. It is also known as Rijndael Algorithm. Because of high performance of AES algorithm, it has been chosen as a standard among symmetric cipher algorithms. In this paper, we would like to propose a symmetric encryption algorithm. Modification is based on AES and we add an additional or second key. Another modification is also done at SubBytes step by adding the transportation operation in the original SubBytes operation. To analyze the performance of the modified proposed algorithm, Java language is used to implement the algorithm and then the performance is analyzed. After analyzing and verifying the experimental results, the proposed revised algorithm also shows good performance and high security from the cryptographic point of view. Based on the results of comparison between modified AES and original AES algorithm, our proposed algorithm can be used as a symmetric encryption algorithm, especially for the applications that share sensitive data files via insecure network.

With the rapidly growth of mobile technology, the lifestyle of people is becoming reliant on various mobile devices like smartphones, tablets and so on. By using these mobile devices, users can perform transactions such as purchasing goods, making bank transaction and paying for tickets and coupons. It may bring flexibility and convenience for communication, storing and sharing data, social media networks and entertainments. On the other side, criminals may commit the crimes by using Android devices. Therefore, the investigators need to prepare enough Android forensics tools for investigation and analysis. In this paper, we proposed the applicable tool (ANDROSICS) for Android forensics which emphasizes on evidence data collection process. This tool can support Live Forensics, Dead Forensics, Data Collection, Bypass Screen Lock, Imaging, Android Debug Bridge Utility, Virtual Keys and many forensic features.

Entity Resolution also known as data matching or record linkage, is the task of identifying records from several databases that refer to the same entities. The efficiency of a blocking method is hindered by large blocks since the resulting number of record pairs is dominated by the sizes of these large blocks. So, the researchers are still doing researches on handling the problems of large blocks. Same blocking methods can yield bipolar results against different datasets, selecting a suitable blocking method for the given record linkage algorithm and dataset requires significant domain knowledge. Many researches in entity resolution has concentrated on either improving the matching quality, making entity resolution scalable to very large databases, or reducing the manual efforts required throughout the resolution process. In this paper, we propose an efficient record searching using dynamic blocking in entity resolution systems.

Building systems in which knowledge can be shared in an ad hoc, distributed environment is one of the promises of Semantic Web. Mappings between disparate models are fundamental to any application that requires interoperability between heterogeneous data and applications. This paper intends to develop web information extraction technique using semantic technology. This system presents an approach to extract and integrate information from unstructured documents (e.g. HTML) and is converted to standard format (XML) by using ontology. A mapping technique will be used to automatically generate RDF descriptions from XML documents and the system will create RDF Sitemap which will provide more efficient and effective for Information Retrieval (IR).

Natural Language Processing (NLP) based technologies are now becoming important and future intelligent systems will use more of these techniques as the technology is improving explosively. But Asia becomes a dense area in NLP field because of linguistic diversity. Many Asian languages are inadequately supported on computers. Myanmar language is an analytic language but it includes special character like killer, medial, etc.. In English or European languages, all of the syllables are formed by combining the alphabets that represent only consonants and vowels but Myanmar language uses compound syllables that make more difficult to analyze. So we can face difficulties in word sorting. In our proposed system, the condensed form of Myanmar ordinary scripts will be transformed into analyzable elaborated scripts based on LIPIDIPIKAR treatise written by Yaw Min Gyi U Pho Hlaing. These elaborated words can be easily sorted by using this treatise. In our proposed system, complexity of Myanmar condensed words sorting compared with complexity of elaborated words sorting.

In spite of being provided the abundant raw data from World Wide Web (WWW), it is difficult to make any sense out of such massive data without data mining techniques. In this paper, we emphasize on the mining of web access logs, web usage data. Our propose framework is composed of five steps. The first step is defining the purposes that our multipurpose analyzer can provide. In second step, we define the ontology mapping based on Web Sites defined in the previous step. After that, we perform preprocessing step base on our web log access data as the fundamental requirements of the proposed framework. Web usage based mining based on frequent item sets and our proposed algorithms are the core step of the system. In the final step, Naive Bayesian classifier is applied to predict for the future depending on the current analysis outcomes. Our system is intended to provide for Web Site Maintainers, Web Site Developers, Personalization Systems, Pre-fetched Systems, Recommender Systems and Web Site Analysts, etc.