• The Journal of Bigdata
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• v.6 no.2
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• pp.99-108
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• 2021

Recently, unexpected and more advanced cyber medical treat attacks are on the rise. However, in responding to various patterns of cyber medical threat attack, rule-based security methodologies such as physical blocking and replacement of medical devices have the limitations such as lack of the man-power and high cost. As a way to solve the problems, the medical community is also paying attention to artificial intelligence technology that enables security threat detection and prediction by self-learning the past abnormal behaviors. In this study, there has collecting and learning the medical information data from integrated Medical-Information-Systems of the medical center and introduce the research methodology which is to develop the AI-based Net-Working Behavior Adaptive Information data. By doing this study, we will introduce all technological matters of rule-based security programs and discuss strategies to activate artificial intelligence technology in the medical information business with the various restrictions.

• Journal of Korea Multimedia Society
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• v.15 no.2
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• pp.226-233
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• 2012

Recently, Like we saw with 3.4 DDoS Cyber Terror, a behavior of cyber terror becomes increasingly more complicated, sophisticated and larger, and there has been largely damage on industry, the general economy. For responding cyber terrors which occur in the future, we should recognize security holes of system which isn't exposed yet before attacker in advance as we anticipate and implement new technique of cyber attack which not exist hitherto. We design and implement a new technique of cyber attack; it seems to us that a server denies agent' service by altering value of registry in windows system. Network connections of agent are restricted to the new technique we suggest as the a value of registry is changed to a less value than a necessary value and there has happened packet loss by attacker.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.12
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• pp.1841-1848
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• 2013

In this paper, the plans for improvement of real-time security monitoring accuracy and expansion of control region were investigated through comprehensive and systematic collection and analysis of the anomalous activities that inflow and outflow in the network on a large scale in order to overcome the existing security monitoring system based on stylized detection patterns which could correspond to only very limited cyber attacks. This study established an anomaly observation system to collect, store and analyze a diverse infringement threat information flowing into the darknet network, and presented the information classification system of cyber threats, unknown anomalies and high-risk anomalous activities through the statistics based trend analysis of hacking. If this security monitoring system utilizing darknet traffic as presented in the study is applied, it was indicated that detection of all infringement threats was increased by 12.6 percent compared with conventional case and 120 kinds of new type and varietal attacks that could not be detected in the past were detected.

• Journal of Internet Technology
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• v.22 no.4
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• pp.903-911
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• 2021

As cyber-attacks on Cyber-Physical System (CPS) become more diverse and sophisticated, it is important to quickly detect malicious behaviors occurring in CPS. Since CPS can collect sensor data in near real time throughout the process, there have been many attempts to detect anomaly behavior through normal behavior learning from the perspective of data-driven security. However, since the CPS datasets are big data and most of the data are normal data, it has always been a great challenge to analyze the data and implement the anomaly detection model. In this paper, we propose and evaluate the Clustered Deep One-Class Classification (CD-OCC) model that combines the clustering algorithm and deep learning (DL) model using only a normal dataset for anomaly detection. We use auto-encoder to reduce the dimensions of the dataset and the K-means clustering algorithm to classify the normal data into the optimal cluster size. The DL model trains to predict clusters of normal data, and we can obtain logit values as outputs. The derived logit values are datasets that can better represent normal data in terms of knowledge distillation and are used as inputs to the OCC model. As a result of the experiment, the F1 score of the proposed model shows 0.93 and 0.83 in the SWaT and HAI dataset, respectively, and shows a significant performance improvement over other recent detectors such as Com-AE and SVM-RBF.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.5
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• pp.839-850
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• 2014

As the social and financial damages caused by APT attack such as 3.20 cyber terror are increased, the technical solution against APT attack is required. It is, however, difficult to protect APT attack with existing security equipments because the attack use a zero-day malware persistingly. In this paper, we propose a host based anomaly detection method to overcome the limitation of the conventional signature-based intrusion detection system. First, we defined 39 features to identify between normal and abnormal behavior, and then collected 8.7 million feature data set that are occurred during running both malware and normal executable file. Further, each process is represented as 83-dimensional vector that profiles the frequency of appearance of features. the vector also includes the frequency of features generated in the child processes of each process. Therefore, it is possible to represent the whole behavior information of the process while the process is running. In the experimental results which is applying C4.5 decision tree algorithm, we have confirmed 2.0% and 5.8% for the false positive and the false negative, respectively.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1027-1041
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• 2015

Recently, there are rapidly increasing cases of APT (Advanced Persistent Threat) attacks such as Verizon(2010), Nonghyup(2011), SK Communications(2011), and 3.20 Cyber Terror(2013), which cause leak of confidential information and tremendous damage to valuable assets without being noticed. Several anomaly detection technologies were studied to defend the APT attacks, mostly focusing on detection of obvious anomalies based on known malicious codes' signature. However, they are limited in detecting APT attacks and suffering from high false-negative detection accuracy because APT attacks consistently use zero-day vulnerabilities and have long latent period. Detecting APT attacks requires long-term analysis of data from a diverse set of sources collected over the long time, real-time analysis of the ingested data, and correlation analysis of individual attacks. However, traditional security systems lack sophisticated analytic capabilities, compute power, and agility. In this paper, we propose a Fast Data based real-time abnormal behavior detection system to overcome the traditional systems' real-time processing and analysis limitation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.47-56
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• 2018

National-scale industrial control systems for gas, electric power, water processing, nuclear power, and traffic control systems increasingly use open networks and open standards protocols based on advanced information and communications technologies. The frequency of cyberattacks increases steadily because of the use of open networks and open standards protocols, but follow-up actions are limited. Therefore, the application of security solutions to an industrial control system is very important. However, it is not possible to apply security solutions to a real system because of the characteristics of industrial control systems. And a security system that can detect attacks without affecting the existing system is imperative. Therefore, in this paper, we propose an intrusion detection system based on packet analysis that can detect anomalous behaviors without affecting the industrial control system, and we verify the effectiveness of the proposed intrusion detection system by applying it in a test bed simulating a real environment.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.1
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• pp.51-61
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• 2023

Messengers such as KakaoTalk, LINE, and Facebook Messenger are universal means of communication used by anyone. As the convenience functions provided to users and their usage time increase, so does the user behavior information remaining in the artifacts, which is being used as important evidence from the perspective of digital forensic investigation. However, for security reasons, most of the data is currently stored encrypted. In addition, cover-up behaviors such as intentional manipulation, concealment, and deletion are increasing, causing the problem of delaying digital forensic analysis time. In this paper, we conducted a study on the data decryption and artifacts analysis in a Windows environment for KakaoTalk, the messenger with the largest number of users in Korea. An efficient way of obtaining a decryption key and a method of identifying and decrypting messages attempted to be deleted are presented, and thumbnail artifacts are analyzed.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.143-153
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• 2022

With the development of networks and the increase in the number of network devices, the number of cyber attacks targeting them is also increasing. Since these cyber-attacks aim to steal important information and destroy systems, it is necessary to minimize social and economic damage through early detection and rapid response. Many studies using machine learning (ML) and artificial intelligence (AI) have been conducted, among which payload learning is one of the most intuitive and effective methods to detect malicious behavior. In this study, we propose a preprocessing method to maximize the performance of the model when learning the payload in term units. The proposed method constructs a high-quality learning data set by eliminating unnecessary noise (stopwords) and preserving important features in consideration of the machine language and natural language characteristics of the packet payload. Our method consists of three steps: Preserving significant special characters, Generating a stopword list, and Class label refinement. By processing packets of various and complex structures based on these three processes, it is possible to make high-quality training data that can be helpful to build high-performance ML/AI models for security monitoring. We prove the effectiveness of the proposed method by comparing the performance of the AI model to which the proposed method is applied and not. Forthermore, by evaluating the performance of the AI model applied proposed method in the real-world Security Operating Center (SOC) environment with live network traffic, we demonstrate the applicability of the our method to the real environment.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.199-202
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• 2023

Carriage return (CR) and line feed (LF), also known as CRLF injection is a type of vulnerability that allows a hacker to enter special characters into a web application, altering its operation or confusing the administrator. Log poisoning and HTTP response splitting are two prominent harmful uses of this technique. Additionally, CRLF injection can be used by an attacker to exploit other vulnerabilities, such as cross-site scripting (XSS). Email injection, also known as email header injection, is another way that can be used to modify the behavior of emails. The Open Web Application Security Project (OWASP) is an organization that studies vulnerabilities and ranks them based on their level of risk. According to OWASP, CRLF vulnerabilities are among the top 10 vulnerabilities and are a type of injection attack. Automated testing can help to quickly identify CRLF vulnerabilities, and is particularly useful for companies to test their applications before releasing them. However, CRLF vulnerabilities can also lead to the discovery of other high-risk vulnerabilities, and it fosters a better approach to mitigate CRLF vulnerabilities in the early stage and help secure applications against known vulnerabilities. Although there has been a significant amount of research on other types of injection attacks, such as Structure Query Language Injection (SQL Injection). There has been less research on CRLF vulnerabilities and how to detect them with automated testing. There is room for further research to be done on this subject matter in order to develop creative solutions to problems. It will also help to reduce false positive alerts by checking the header response of each request. Security automation is an important issue for companies trying to protect themselves against security threats. Automated alerts from security systems can provide a quicker and more accurate understanding of potential vulnerabilities and can help to reduce false positive alerts. Despite the extensive research on various types of vulnerabilities in web applications, CRLF vulnerabilities have only recently been included in the research. Utilizing automated testing as a recurring task can assist companies in receiving consistent updates about their systems and enhance their security.