• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.1-10
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• 2021

Cyberbullying has become a severe issue and brought a powerful impact on the cyber world. Due to the low cost and fast spreading of news, social media has become a tool that helps spread insult, offensive, and hate messages or opinions in a community. Detecting cyberbullying from social media is an intriguing research topic because it is vital for law enforcement agencies to witness how social media broadcast hate messages. Twitter is one of the famous social media and a platform for users to tell stories, give views, express feelings, and even spread news, whether true or false. Hence, it becomes an excellent resource for sentiment analysis. This paper aims to detect cyberbully threats based on Naïve Bayes, support vector machine (SVM), and k-nearest neighbour (k-NN) classifier model. Sentiment analysis will be applied based on people's opinions on social media and distribute polarity to them as positive, neutral, or negative. The accuracy for each classifier will be evaluated.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.5
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• pp.851-860
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• 2014

With an increasing demand of powerful electronic goods, smart TV containing network module with digital TV gets more popular. These change are meaningful from a digital forensics perspective because smart TV store more user's data than digital TV. In this paper, we suggest smart TV forensics as a branch of digital forensics. With smart TV forensics, investigator can trace more wide age group's activities than existing digital forensics analysis.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.95-102
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• 2023

The emergence of Internet of Things (IoT) into our daily lives has grown rapidly. It's been integrated to our homes, cars, and cities, increasing the intelligence of devices involved in communications. Enormous amount of data is exchanged over smart devices through the internet, which raises security concerns in regards of privacy evasion. This paper is focused on the forensics and intrusion detection on one of the most common protocols in IoT environments, especially smart home environments, which is the Message Queuing Telemetry Transport (MQTT) protocol. The paper covers general IoT infrastructure, MQTT protocol and attacks conducted on it, and multiple network forensics frameworks in smart homes. Furthermore, a machine learning model is developed and tested to detect several types of attacks in an IoT network. A forensics tool (MQTTracker) is proposed to contribute to the investigation of MQTT protocol in order to provide a safer technological future in the warmth of people's homes. The MQTT-IOT-IDS2020 dataset is used to train the machine learning model. In addition, different attack detection algorithms are compared to ensure the suitable algorithm is chosen to perform accurate classification of attacks within MQTT traffic.

• Journal of the Korea Society of Computer and Information
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• v.11 no.6 s.44
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• pp.175-184
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• 2006

Computer Forensics functions by defending the effects and extracting the evidence of the side effects for production at the court. Has the faultlessness of the digital evidence been compromised during the investigation, a critical evidence may be denied or not even be presented at the trial. The presented monograph will deliberate the faultlessness-establishing chain procedures in disk forensics, system forensics, network forensics, mobile forensics and database forensics. Once the faultlessness is established by the methods proposed, the products of investigation will be adopted as a leading evidence. Moreover, the issues and alternatives in the reality of digital investigation are presented along with the actual computer forensics cases, hopefully contributing to the advances in computer digital forensics and the field research of information security.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1738-1756
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• 2020

To improve the accuracy and realism of the virtual surgical simulation system, this paper proposes an optimized mass-spring model with shape restoration ability based on volume conservation to simulate soft tissue deformation. The proposed method constructs a soft tissue surface model that adopts a new flexion spring for resisting bending and incorporates it into the mass-spring model (MSM) to restore the original shape. Then, we employ the particle swarm optimization algorithm to achieve the optimal solution of the model parameters. Besides, the volume conservation constraint is applied to the position-based dynamics (PBD) approach to maintain the volume of the deformable object for constructing the soft tissue volumetric model base on tetrahedrons. Finally, we built a simulation system on the PHANTOM OMNI force tactile interaction device to realize the deformation simulation of the virtual liver. Experimental results show that the proposed model has a good shape restoration ability and incompressibility, which can enhance the deformation accuracy and interactive realism.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.1
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• pp.25-38
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• 2017

Many security threats are occurring around the world due to the characteristics of industrial control systems that can cause serious damage in the event of a security incident including major national infrastructure. Therefore, the industrial control system network traffic should be analyzed so that it can identify the attack in advance or perform incident response after the accident. In this paper, we research the visualization technique as network forensics to enable reasonable suspicion of all possible attacks on DNP3 control system protocol, and define normal action based rules and derive visualization requirements. As a result, we developed a visualization tool that can detect sudden network traffic changes such as DDoS and attacks that contain anormal behavior from captured packet files on industrial control system network. The suspicious behavior in the industrial control system network can be found using visualization tool with Digital Bond packet.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.6
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• pp.1351-1364
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• 2019

When SCADA is exposed to cyber threats and attacks, serious disasters can occur throughout society. This is because various security threats have not been considered when building SCADA. The bigger problem is that it is difficult to patch vulnerabilities quickly because of its availability. Digital forensics procedures and techniques need to be used to analyze and investigate vulnerabilities in SCADA systems in order to respond quickly against cyber threats and to prevent incidents. This paper addresses SCADA forensics taxonomy and research trends for effective digital forensics investigation on SCADA system. As a result, we have not been able to find any research that goes far beyond traditional digital forensics on procedures and methodologies. But it is meaningful to develop an approach methodology using the characteristics of the SCADA system, or an exclusive tool for SCADA. Analysis techniques mainly focused on PLC and SCADA network protocol. It is because the cyber threats and attacks targeting SCADA are mostly related to PLC or network protocol. Such research seems to continue in the future. Unfortunately, there is lack of discussion about the 'Evidence Capability' such as the preservation or integrity of the evidence extracting from SCADA system in the past researches.

• The Journal of Information Technology
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• v.10 no.3
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• pp.93-120
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• 2007

A Smart Home is a technically expanded from home network that gives us a comfortable life. But still there is a problem such as mal function of devices and intrusions by malicious parties since it is based on home network. The intrusion by malicious parties causes a critical problem to the individual's privacy. Therefore to take legal actions against to the intruders, the intrusion evidence collecting and managing technology are widely researched in the world. The evidence collecting technology uses the system which was damaged by intruders and that system is used as evidence materials in the court of justice. However the collected evidences are easily modified and damaged in the gathering evidence process, the evidence analysis process and in the court. That's why we have to prove the evidence's integrity to be valuably used in the court. In this paper, we propose a mechanism for securing the reliability and the integrity of digital evidence that can properly support the Computer Forensics. The proposed mechanism shares and manages the digital evidence through mutual authenticating the damaged system, evidence collecting system, evidence managing system and the court(TTP: Trusted Third Party) and provides a secure access control model to establish the secure evidence management policy which assures that the collected evidence has the corresponded legal effect.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3488-3500
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• 2020

At the present time, the economy continues to flourish, and private cars have become the means of choice for most people. Therefore, the license plate recognition technology has become an indispensable part of intelligent transportation, with research and application value. In recent years, the convolution neural network for image classification is an application of deep learning on image processing. This paper proposes a strategy to improve the YOLO model by studying the deep learning convolutional neural network (CNN) and related target detection methods, and combines the OpenCV and TensorFlow frameworks to achieve efficient recognition of license plate characters. The experimental results show that target detection method based on YOLO is beneficial to shorten the training process and achieve a good level of accuracy.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.294-300
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• 2021

The current society relies upon social media on an everyday basis, which contributes to finding which of the following supervised machine learning algorithms used in sentiment analysis have higher accuracy in detecting Malay internet slang and short forms which can be offensive to a person. This paper is to determine which of the algorithms chosen in supervised machine learning with higher accuracy in detecting internet slang and short forms. To analyze the results of the supervised machine learning classifiers, we have chosen two types of datasets, one is political topic-based, and another same set but is mixed with 50 tweets per targeted keyword. The datasets are then manually labelled positive and negative, before separating the 275 tweets into training and testing sets. Naïve Bayes and Random Forest classifiers are then analyzed and evaluated from their performances. Our experiment results show that Random Forest is a better classifier compared to Naïve Bayes.