• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.317-323
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• 2021

Social media is increasingly becoming a part of our daily life for communicating each other. There are various tools and applications for communication and therefore, identity theft is a common issue among users of such application. A new style of identity theft occurs when cybercriminals break into WhatsApp account, pretend as real friends and demand money or blackmail emotionally. In order to prevent from such issues, data mining can be used for text classification (TC) in analysis authorship attribution (AA) to recognize original sender of the message. Arabic is one of the most spoken languages around the world with different variants. In this research, we built a machine learning model for mining and analyzing the Arabic messages to identify the author of the messages in Saudi dialect. Many points would be addressed regarding authorship attribution mining and analysis: collect Arabic messages in the Saudi dialect, filtration of the messages' tokens. The classification would use a cross-validation technique and different machine-learning algorithms (Naïve Baye, Support Vector Machine). Results of average accuracy for Naïve Baye and Support Vector Machine have been presented and suggestions for future work have been presented.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.777-779
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• 2014

The LEA(Lightweight Encryption Algorithm) is a 128-bit high-speed/lightweight block cipher algorithm developed by National Security Research Institute(NSRI) in 2012. The LEA encrypts plain text of 128-bit using cipher key of 128/192/256-bit, and produces cipher text of 128-bit, and vice versa. To reduce hardware complexity, we propose an efficient architecture which shares hardware resources for encryption and decryption in round transformation block. Hardware sharing technique for key scheduler was also devised to achieve area-efficient and low-power implementation. The designed LEA cryptographic processor was verified by using FPGA implementation.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.238-246
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• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.132-138
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• 2022

Due to the impacts of the current pandemic COVID-19 and the continuation of studying online. There is an urgent need for an effective and efficient education platform to help with the continuity of studying online. Therefore, the question bank system (QB) is introduced. The QB system is designed as a website to create a single platform used by faculty members in universities to generate questions and store them in a bank of questions. In addition to allowing them to add two types of questions, to help the lecturer create exams and present the results of the students to them. For the implementation, two languages were combined which are PHP and Python to generate questions by using Artificial Intelligence (AI). These questions are stored in a single database, and then these questions could be viewed and included in exams smoothly and without complexity. This paper aims to help the faculty members to reduce time and efforts by using the Question Bank System by using AI and Natural Language Processing (NLP) to extract and generate questions from given text. In addition to the tools used to create this function such as NLTK and TextBlob.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.15-24
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• 2023

One of the objectives and aspirations of scientists and engineers ever since the development of computers has been to interact naturally with machines. Hence features of artificial intelligence (AI) like natural language processing and natural language generation were developed. The field of AI that is thought to be expanding the fastest is interactive conversational systems. Numerous businesses have created various Virtual Personal Assistants (VPAs) using these technologies, including Apple's Siri, Amazon's Alexa, and Google Assistant, among others. Even though many chatbots have been introduced through the years to diagnose or treat psychological disorders, we are yet to have a user-friendly chatbot available. A smart generative cognitive behavioral therapy with spoken dialogue systems support was then developed using a model Persona Perception (P2) bot with Generative Pre-trained Transformer-2 (GPT-2). The model was then implemented using modern technologies in VPAs like voice recognition, Natural Language Understanding (NLU), and text-to-speech. This system is a magnificent device to help with voice-based systems because it can have therapeutic discussions with the users utilizing text and vocal interactive user experience.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.21-31
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• 2023

In this paper, we focused on the problem of evaluating multi-class classification accuracy and simulation of multiple classifier performance metrics. Multi-class classifiers for sentiment analysis involved many challenges, whereas previous research narrowed to the binary classification model since it provides higher accuracy when dealing with text data. Thus, we take inspiration from the non-linear Support Vector Machine to modify the algorithm by embedding dynamic hyperplanes representing multiple class labels. Then we analyzed the performance of multi-class classifiers using macro-accuracy, micro-accuracy and several other metrics to justify the significance of our algorithm enhancement. Furthermore, we hybridized Enhanced Convolution Neural Network (ECNN) with Dynamic Support Vector Machine (DSVM) to demonstrate the effectiveness and efficiency of the classifier towards multi-class text data. We performed experiments on three hybrid classifiers, which are ECNN with Binary SVM (ECNN-BSVM), and ECNN with linear Multi-Class SVM (ECNN-MCSVM) and our proposed algorithm (ECNNDSVM). Comparative experiments of hybrid algorithms yielded 85.12 % for single metric accuracy; 86.95 % for multiple metrics on average. As for our modified algorithm of the ECNN-DSVM classifier, we reached 98.29 % micro-accuracy results with an f-score value of 98 % at most. For the future direction of this research, we are aiming for hyperplane optimization analysis.

• Proceedings of the KAIS Fall Conference
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• 2010.11a
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• pp.189-193
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• 2010

SIP 프로토콜 기반 VoIP 서비스는 편리함과 저렴한 통신비용으로 사용자 수가 급증하고 있다. 하지만 Text 형태의 SIP 헤더 정보를 UDP 방식으로 전송하기 때문에 손쉽게 위변조 할 수 있으며, 최근 SIP를 통해 제공되는 다양한 형태의 부가서비스에 대한 보안 위협이 증가하고 있다. FMC 폰을 통해 제공되는 동시착신, 착신전환, 3인통화 등과 같은 부가서비스에 대한 공격을 통해 과금우회 공격 등을 수행할 수 있다. 따라서 본 연구에서는 FMC 환경에서 제공되는 주요 SIP 부가서비스를 대상으로 각각의 취약성에 대해 조사 분석하였으며, 분석된 내용을 중심으로 SIP 기반 인터넷전화 부가서비스에 대한 공격 시나리오를 단계별로 설계 및 개발하였다.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.115-126
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• 2022

The goal of this research is to look into different techniques to solve the problem of authorship verification for Arabic short writings. Despite the widespread usage of Twitter among Arabs, short text research has so far focused on authorship verification in languages other than Arabic, such as English, Spanish, and Greek. To the best of the researcher's knowledge, no study has looked into the task of verifying Arabic-language Twitter texts. The impact of Stylometric and TF-IDF features of very brief texts (Arabic Twitter postings) on user verification was explored in this study. In addition, an analytical analysis was done to see how meta-data from Twitter tweets, such as time and source, can help to verify users perform better. This research is significant on the subject of cyber security in Arabic countries.

• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.125-142
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• 2021

Reports of rampant cross-site scripting (XSS) vulnerabilities raise growing concerns on the effectiveness of current Static Analysis Security Testing (SAST) tools as an internet security device. Attentive to these concerns, this study aims to examine seven open-source SAST tools in order to account for their capabilities in detecting XSS vulnerabilities in PHP applications and to determine their performance in terms of effectiveness and analysis runtime. The representative tools - categorized as either text-based or graph-based analysis tools - were all test-run using real-world PHP applications with known XSS vulnerabilities. The collected vulnerability detection reports of each tool were analyzed with the aid of PhpStorm's data flow analyzer. It is observed that the detection rates of the tools calculated from the total vulnerabilities in the applications can be as high as 0.968 and as low as 0.006. Furthermore, the tools took an average of less than a minute to complete an analysis. Notably, their runtime is independent of their analysis type.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.1
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• pp.87-101
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• 2017

As people increasingly rely on mobile networks in modern society, mobile communication security is becoming more and more important. In the Long Term Evolution/System Architecture Evolution (LTE/SAE) architecture, the 3rd Generation Partnership (3GPP) team has also developed the improved Evolved Packet System Authentication and Key Agreement (EPS AKA) protocol based on the 3rd Generation Authentication and Key Agreement (3G AKA) protocol in order to provide mutual authentication and secure communication between the user and the network. Unfortunately, the EPS AKA also has several vulnerabilities such as sending the International Mobile Subscriber Identity (IMSI) in plain text (which leads to disclosure of user identity and further causes location and tracing of the user, Mobility Management Entity (MME) attack), man-in-middle attack, etc. Hence, in this paper, we analyze the EPS AKA protocol and point out its deficiencies and then propose an Efficient and Security Enhanced Authentication and Key agreement (ESE-EPS AKA) protocol based on hybrid of Dynamic Pseudonym Mechanism (DPM) and Public Key Infrastructure (PKI) retaining the original framework and the infrastructure of the LTE network. Then, our evaluation proves that the proposed new ESE-EPS AKA protocol is relatively more efficient, secure and satisfies some of the security requirements such as confidentiality, integrity and authentication.