• The Journal of the Korea Contents Association
• /
• v.21 no.10
• /
• pp.59-66
• /
• 2021

In this paper, an improved integrated security control procedure is newly proposed by applying artificial intelligence technology to integrated security control and unifying the existing security control and AI security control response procedures. Current cyber security control is highly dependent on the level of human ability. In other words, it is practically unreasonable to analyze various logs generated by people from different types of equipment and analyze and process all of the security events that are rapidly increasing. And, the signature-based security equipment that detects by matching a string and a pattern has insufficient functions to accurately detect advanced and advanced cyberattacks such as APT (Advanced Persistent Threat). As one way to solve these pending problems, the artificial intelligence technology of supervised and unsupervised learning is applied to the detection and analysis of cyber attacks, and through this, the analysis of logs and events that occur innumerable times is automated and intelligent through this. The level of response has been raised in the overall aspect by making it possible to predict and block the continuous occurrence of cyberattacks. And after applying AI security control technology, an improved integrated security control service model was newly proposed by integrating and solving the problem of overlapping detection of AI and SIEM into a unified breach response process(procedure).

• International Journal of Computer Science & Network Security
• /
• v.22 no.7
• /
• pp.191-198
• /
• 2022

With the ever-increasing frequency of public sector and smalls-cale industries going live on the internet in developing countries, their security of which, while crucial, is often overlooked in most cases. This is especially true in Government services, whilst essential, are poorly monitored if at all. This is due to lack of funds and personnel. Most available software which can help these organizations monitor their services are either expensive or very outdated. Thus, there is a need for any developing country to develop a networking monitoring system. However, developing a network monitoring system is still a challenge and expensive and out sourcing network monitoring system to third party is a security threat. Therefore, in this article we propose a method to anonymize network logs and outsource networking monitoring system to third-party without breach in integrity of their network logs.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.27 no.4
• /
• pp.763-773
• /
• 2017

In recent years, personal information leakage and technology leakage accidents are frequently occurring. According to the survey, the most important part of this spill is the 'insider' within the organization, and the leakage of technology by insiders is considered to be an increasingly important issue because it causes huge damage to the organization. In this paper, we try to learn the normal behavior of employees using machine learning to prevent insider threats, and to investigate how to detect abnormal behavior. Experiments on the detection of abnormal behavior by implementing an Autoencoder composed of Recurrent Neural Network suitable for learning time series data among the neural network models were conducted and the validity of this method was verified.

• Smart Media Journal
• /
• v.13 no.3
• /
• pp.45-52
• /
• 2024

In today's growing threat environment, rapid and effective detection and response to security events is essential. To solve these problems, many companies and organizations respond to security threats by introducing security control systems. However, existing security control systems are experiencing difficulties due to the complexity and diverse characteristics of security events. In this study, we propose an automated integrated security control system model based on artificial intelligence. It is based on deep learning, an artificial intelligence technology, and provides effective detection and processing functions for various security events. To this end, the model applies various artificial intelligence algorithms and machine learning methods to overcome the limitations of existing security control systems. The proposed model reduces the operator's workload, ensures efficient operation, and supports rapid response to security threats.

• Journal of Internet Computing and Services
• /
• v.20 no.2
• /
• pp.9-19
• /
• 2019

Service and users over the Internet are increasing rapidly. Cyber attacks are also increasing. As a result, information leakage and financial damage are occurring. Government, public agencies, and companies are using security systems that use signature-based detection rules to respond to known malicious codes. However, it takes a long time to generate and validate signature-based detection rules. In this paper, we propose and develop signature based detection rule generation and verification systems using the signature extraction scheme developed based on the LDA(latent Dirichlet allocation) algorithm and the traffic analysis technique. Experimental results show that detection rules are generated and verified much more quickly than before.

• International Journal of Computer Science & Network Security
• /
• v.24 no.1
• /
• pp.52-60
• /
• 2024

APT (Advanced Persistent Threat) attack is a dangerous, targeted attack form with clear targets. APT attack campaigns have huge consequences. Therefore, the problem of researching and developing the APT attack detection solution is very urgent and necessary nowadays. On the other hand, no matter how advanced the APT attack, it has clear processes and lifecycles. Taking advantage of this point, security experts recommend that could develop APT attack detection solutions for each of their life cycles and processes. In APT attacks, hackers often use phishing techniques to perform attacks and steal data. If this attack and phishing phase is detected, the entire APT attack campaign will be crash. Therefore, it is necessary to research and deploy technology and solutions that could detect early the APT attack when it is in the stages of attacking and stealing data. This paper proposes an APT attack detection framework based on the Network traffic analysis technique using open-source tools and deep learning models. This research focuses on analyzing Network traffic into different components, then finds ways to extract abnormal behaviors on those components, and finally uses deep learning algorithms to classify Network traffic based on the extracted abnormal behaviors. The abnormal behavior analysis process is presented in detail in section III.A of the paper. The APT attack detection method based on Network traffic is presented in section III.B of this paper. Finally, the experimental process of the proposal is performed in section IV of the paper.

• Journal of Information Technology Services
• /
• v.15 no.2
• /
• pp.157-167
• /
• 2016

Task environment for enterprises and public institutions are moving into cyberspace-based environment and structing the LTE wireless network. The applications "App" operated in the LTE wireless network are mostly being developed with Android-based. But Android-based malwares are surging and they are the potential DDoS attacks. DDoS attack is a major information security threat and a means of cyber attacks. DDoS attacks are difficult to detect in advance and to defense effectively. To this end, a DMZ is set up in front of a network infrastructure and a particular server for defensive information security. Because There is the proliferation of mobile devices and apps, and the activation of android diversify DDoS attack methods. a DMZ is a limit to detect and to protect against DDoS attacks. This paper proposes an information security method to detect and Protect DDoS attacks from the terminal phase using a Preemptive military strategy concept. and then DDoS attack detection and protection app is implemented and proved its effectiveness by reducing web service request and memory usage. DDoS attack detection and protecting will ensure the efficiency of the mobile network resources. This method is necessary for a continuous usage of a wireless network environment for the national security and disaster control.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.30 no.3
• /
• pp.357-367
• /
• 2020

According to Symantec's 2018 internet security threat report, The number of new mobile malware variants increased by 54 percent in 2017, as compared to 2016. And last year, there were an average of 24,000 malicious mobile applications blocked each day. Existing signature-based technologies of malware detection have limitations. So, malware detection technique through machine learning is being researched to detect malware variant. However, even in the case of applying machine learning, if the proper features of the malware are not properly selected, the machine learning cannot be shown correctly. We are focusing on feature selection method to find the features of malware variant in this research.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.16 no.1
• /
• pp.71-77
• /
• 2006

Several network attacks, such as distributed denial of service (DDoS) attack, present a very serious threat to the stability of the internet. The threat posed by network attacks on large networks, such as the internet, demands effective detection method. Therefore, a simple intrusion detection system on large-scale backbone network is needed for the sake of real-time detection, preemption and detection efficiency. In this paper, in order to discriminate attack traffic from legitimate traffic on backbone links, we suggest a relatively simple statistical measure, entropy, which can track value frequency. Den is conspicuous distinction of entropy values between attack traffic and legitimate traffic. Therefore, we can identify what kind of attack it is as well as detecting the attack traffic using entropy value.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.29 no.5
• /
• pp.1167-1177
• /
• 2019

In this paper, the improved security operation model based on the vulnerability database is studied. The proposed model consists of information protection equipment, vulnerability database, and a dashboard that visualizes and provides the results of interworking with detected logs. The evaluation of the model is analyzed by setting up a simulated attack scenario in a virtual infrastructure. In contrast to the traditional method, it is possible to respond quickly to threats of attacks specific to the security vulnerabilities that the asset has, and to find redundancy between detection rules with a secure agent, thereby creating an optimal detection rule.