• Journal of the Korea Society of Computer and Information
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• v.25 no.8
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• pp.31-37
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• 2020

In this paper, we propose a system architecture and methodology based on cyber kill chain and MITRE ATT&CK for experiment analysis on cyber warfare simulation. Threat analysis is possible by applying various attacks that have actually occurred with continuous updates to reflect newly emerging attacks. In terms of cyber attack and defense, the current system(AS-IS) and the new system(TO-BE) are analyzed for effectiveness and quantitative results are presented. It can be used to establish proactive cyber COA(Course of Action) strategy, and also for strategic decision making. Through a case study, we presented the usability of the system architecture and methodology proposed in this paper. The proposed method will contribute to strengthening cyber warfare capabilities by increasing the level of technology for cyber warfare experiments.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.390-399
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• 2022

Cyber security and resilience are phrases that describe safeguards of ICTs (information and communication technologies) from cyber-attacks or mitigations of cyber event impacts. The sole purpose of Risk models are detections, analyses, and handling by considering all relevant perceptions of risks. The current research effort has resulted in the development of a new paradigm for safeguarding services offered online which can be utilized by both service providers and users. customers. However, rather of relying on detailed studies, this approach emphasizes task selection and execution that leads to successful risk treatment outcomes. Modelling intelligent CSGs (Cyber Security Games) using MLTs (machine learning techniques) was the focus of this research. By limiting mission risk, CSGs maximize ability of systems to operate unhindered in cyber environments. The suggested framework's main components are the Threat and Risk models. These models are tailored to meet the special characteristics of online services as well as the cyberspace environment. A risk management procedure is included in the framework. Risk scores are computed by combining probabilities of successful attacks with findings of impact models that predict cyber catastrophe consequences. To assess successful attacks, models emulating defense against threats can be used in topologies. CSGs consider widespread interconnectivity of cyber systems which forces defending all multi-step attack paths. In contrast, attackers just need one of the paths to succeed. CSGs are game-theoretic methods for identifying defense measures and reducing risks for systems and probe for maximum cyber risks using game formulations (MiniMax). To detect the impacts, the attacker player creates an attack tree for each state of the game using a modified Extreme Gradient Boosting Decision Tree (that sees numerous compromises ahead). Based on the findings, the proposed model has a high level of security for the web sources used in the experiment.

• Journal of the Korea Society of Computer and Information
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• v.24 no.5
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• pp.27-33
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• 2019

One of serious security threats is a botnet-based attack. A botnet in general consists of numerous bots, which are computing devices with networking function, such as personal computers, smartphones, or tiny IoT sensor devices compromised by malicious codes or attackers. Such botnets can launch various serious cyber-attacks like DDoS attacks, propagating mal-wares, and spreading spam e-mails over the network. To establish a botnet, attackers usually inject malicious URLs into web source codes stealthily by using data hiding methods like Javascript obfuscation techniques to avoid being discovered by traditional security systems such as Firewall, IPS(Intrusion Prevention System) or IDS(Intrusion Detection System). Meanwhile, it is non-trivial work in practice for software developers to manually find such malicious URLs which are hidden in numerous web source codes stored in web servers. In this paper, we propose a security defense system to discover such suspicious, malicious URLs hidden in web source codes, and present experiment results that show its discovery performance. In particular, based on our experiment results, our proposed system discovered 100% of URLs hidden by Javascript encoding obfuscation within sample web source files.

• ETRI Journal
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• v.42 no.2
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• pp.205-216
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• 2020

An arbiter physical unclonable function (APUF) has exponential challenge-response pairs and is easy to implement on field-programmable gate arrays (FPGAs). However, modeling attacks based on machine learning have become a serious threat to APUFs. Although the modeling-attack resistance of an MA-APUF has been improved considerably by architecture modifications, the response generation method of an MA-APUF results in low uniqueness. In this study, we demonstrate three design problems regarding the low uniqueness that APUF-based strong PUFs may exhibit, and we present several foundational principles to improve the uniqueness of APUF-based strong PUFs. In particular, an improved MA-APUF design is implemented in an FPGA and evaluated using a well-established experimental setup. Two types of evaluation metrics are used for evaluation and comparison. Furthermore, evolution strategies, logistic regression, and K-junta functions are used to evaluate the security of our design. The experiment results reveal that the uniqueness of our improved MA-APUF is 81.29% (compared with that of the MA-APUF, 13.12%), and the prediction rate is approximately 56% (compared with that of the MA-APUF (60%-80%).

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.1
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• pp.123-133
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• 2018

Cyber-Physical System (CPS) is a system in which a physical system and a cyber system are strongly integrated. In order to operate the target physical system stably, the CPS constantly monitors the physical system through the sensor and performs control using the actuator according to the current state. If a malicious attacker performs a forgery attack on the measured values of the sensors in order to conceal their attacks, the cyber system operated based on the collected data can not recognize the current operation status of the physical system. This causes the delay of the response of the automation system and the operator, and then more damage will occur. To protect the CPS from increasingly sophisticated and targeted attacks, countermeasures must be developed that can detect stealthy deception attacks. However, in the CPS environment composed of various heterogeneous devices, the process of analyzing and demonstrating the vulnerability to actual field devices requires a lot of time. Therefore, in this study, we propose a method of constructing the experiment environment of the PLCitM (PLC in the middle) which can verify the performance of the techniques to detect the CPS stealthy deception attack and present the experimental results.

• Journal of Internet Computing and Services
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• v.6 no.1
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• pp.1-12
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• 2005

Recently, the patterns of cyber attack through internet have been various and have become more complicated and thus it is difficult to detect a network intruder effectively and to response the intrusion quickly. Therefore, It is almost not possible to chase the real location of a network intruder and to isolate the Intruder from network in UDP based DoS or DDoS attacks spoofing source IP address and in TCP based detour connection attacks. In this paper, we propose active security architecture on active network to correspond to various cyber attacks promptly. Security management framework is designed using active technology, and security control mechanism to chase and isolate a network intruder is implemented. We also test the operation of the active security mechanism implemented on test_bed according to several attack scenarios and analyze the experiment results.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.111-126
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• 2022

As operations that were only conducted in physical space in the past change to operations that include cyberspace, it is necessary to analyze how cyber attacks affect weapon systems using cyber systems. For this purpose, it would be meaningful to analyze a tool that analyzes the effects of physical weapon systems in connection with cyber. The ROK military has secured and is operating the US JMEM, which contains the results of analyzing the effects of physical weapon systems. JMEM is applied only to conventional weapon systems, so it is impossible to analyze the impact of cyber weapon systems. In this study, based on the previously conducted cyber attack damage assessment framework, a framework for analyzing the impact of cyber attacks on physical missions was presented. To this end, based on the MOE and MOP of physical warfare, a cyber index for the analysis of cyber weapon system effectiveness was calculated. In addition, in conjunction with JMEM, which is used as a weapon system effect manual in physical operations, a framework was designed and tested to determine the mission impact by comparing and analyzing the results of the battle in cyberspace with the effects of physical operations. In order to prove the proposed framework, we analyzed and designed operational scenarios through domestic and foreign military manuals and previous studies, defined assets, and conducted experiments. As a result of the experiment, the larger the decrease in the cyber mission effect value, the greater the effect on physical operations. It can be used to predict the impact of physical operations caused by cyber attacks in various operations, and it will help the battlefield commander to make quick decisions.

• Convergence Security Journal
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• v.22 no.4
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• pp.17-24
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• 2022

Recently, machine learning-based cyber attack detection and classification research has been actively conducted, achieving a high level of detection accuracy. However, low-spec IoT devices and large-scale network traffic make it difficult to apply machine learning-based detection models in IoT environment. Therefore, In this paper, we propose an efficient IoT attack detection and classification method through PCA(Principal Component Analysis) and LightGBM(Light Gradient Boosting Model) using datasets collected in a MQTT(Message Queuing Telementry Transport) IoT protocol environment that is also used in the defense field. As a result of the experiment, even though the original dataset was reduced to about 15%, the performance was almost similar to that of the original. It also showed the best performance in comparative evaluation with the four dimensional reduction techniques selected in this paper.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.5
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• pp.1313-1322
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• 2016

Recently, many virus and hacking attacks on public organizations and financial institutions by internet are becoming increasingly intelligent and sophisticated. The Advanced Persistent Threat has been considered as an important cyber risk. This attack is basically accomplished by spreading malicious codes through complex networks. To detect and extract PE files in smart manufacturing industry networks, an efficient processing method which is performed before analysis procedure on malicious codes is proposed. We implement a preprocessor of open intrusion detection system Snort for fast extraction of PE files and install on a hardware sensor equipment. As a result of practical experiment, we verify that the network sensor can extract the PE files which are often suspected as a malware.

• Journal of Internet Computing and Services
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• v.18 no.6
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• pp.35-46
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• 2017

Recently, attackers using malicious code in cyber security have been increased by attaching malicious code to a mail and inducing the user to execute it. Especially, it is dangerous because it is easy to execute by attaching a document type file. The author analysis is a research area that is being studied in NLP (Neutral Language Process) and text mining, and it studies methods of analyzing authors by analyzing text sentences, texts, and documents in a specific language. In case of attack mail, it is created by the attacker. Therefore, by analyzing the contents of the mail and the attached document file and identifying the corresponding author, it is possible to discover more distinctive features from the normal mail and improve the detection accuracy. In this pager, we proposed IADA2(Intelligent Attack mail Detection based on Authorship Analysis) model for attack mail detection. The feature vector that can classify and detect attack mail from the features used in the existing machine learning based spam detection model and the features used in the author analysis of the document and the IADA2 detection model. We have improved the detection models of attack mails by simply detecting term features and extracted features that reflect the sequence characteristics of words by applying n-grams. Result of experiment show that the proposed method improves performance according to feature combinations, feature selection techniques, and appropriate models.