• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.53-61
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• 2010

In wireless sensor networks(WSNs) individual sensor nodes are subject to security compromises. An adversary can physically capture sensor nodes and obtain the security information. And the adversary injects false reports into the network using compromised nodes. If undetected, these false reports are forwarded to the base station. False reports injection attacks can not only result in false alarms but also depletion of the limited amount of energy in battery powered sensor nodes. To combat these false reports injection attacks, several filtering schemes have been proposed. The statistical en-routing filtering(SEF) scheme can detect and drop false reports during the forwarding process. In SEF, The number of the message authentication codes(threshold) is important for detecting false reports and saving energy. In this paper, we propose a dynamic threshold determination method for energy efficient SEF using fuzzy-logic in wireless sensor networks. The proposed method consider false reports rate and the number of compromised partitions. If low rate of false reports in the networks, the threshold should low. If high rate of false reports in networks, the threshold should high. We evaluated the proposed method’s performance via simulation.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.499-510
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• 2023

Recently, an intelligent and advanced cyber attack attacks a computer network of a public institution using a file containing malicious code or leaks information, and the damage is increasing. Even in public institutions with various information protection systems, known attacks can be detected, but unknown dynamic and encryption attacks can be detected when existing signature-based or static analysis-based malware and ransomware file detection methods are used. vulnerable to The detection method proposed in this study extracts the detection result data of the system that can detect malicious code and ransomware among the information protection systems actually used by public institutions, derives various attributes by combining them, and uses a machine learning classification algorithm. Results are derived through experiments on how the derived properties are classified and which properties have a significant effect on the classification result and accuracy improvement. In the experimental results of this paper, although it is different for each algorithm when a specific attribute is included or not, the learning with a specific attribute shows an increase in accuracy, and later detects malicious code and ransomware files and abnormal behavior in the information protection system. It is expected that it can be used for property selection when creating algorithms.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.67-76
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• 2023

Although deep learning models are making innovative achievements in the field of computer vision, the problem of vulnerability to adversarial examples continues to be raised. Adversarial examples are attack methods that inject fine noise into images to induce misclassification, which can pose a serious threat to the application of deep learning models in the real world. In this paper, we propose a model that detects adversarial examples using differences in predictive values between edge-learned classification models and underlying classification models. The simple process of extracting the edges of the objects and reflecting them in learning can increase the robustness of the classification model, and economical and efficient detection is possible by detecting adversarial examples through differences in predictions between models. In our experiments, the general model showed accuracy of {49.9%, 29.84%, 18.46%, 4.95%, 3.36%} for adversarial examples (eps={0.02, 0.05, 0.1, 0.2, 0.3}), whereas the Canny edge model showed accuracy of {82.58%, 65.96%, 46.71%, 24.94%, 13.41%} and other edge models showed a similar level of accuracy also, indicating that the edge model was more robust against adversarial examples. In addition, adversarial example detection using differences in predictions between models revealed detection rates of {85.47%, 84.64%, 91.44%, 95.47%, and 87.61%} for each epsilon-specific adversarial example. It is expected that this study will contribute to improving the reliability of deep learning models in related research and application industries such as medical, autonomous driving, security, and national defense.

• Journal of Industrial Convergence
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• v.22 no.4
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• pp.1-9
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• 2024

Smart factories represent production facilities where cutting-edge information and communication technologies are fused with manufacturing processes, reflecting rapid advancements and changes in the global manufacturing sector. They capitalize on the integration of robotics and automation, the Internet of Things (IoT), and the convergence of artificial intelligence technologies to maximize production efficiency in various manufacturing environments. However, the smart factory environment is prone to security threats and vulnerabilities due to various attack techniques. When security threats occur in smart factories, they can lead to financial losses, damage to corporate reputation, and even human casualties, necessitating an appropriate security response. Therefore, this paper proposes a security authentication mechanism for safe communication in the smart factory environment. The components of the proposed authentication mechanism include smart devices, an internal operation management system, an authentication system, and a cloud storage server. The smart device registration process, authentication procedure, and the detailed design of anomaly detection and update procedures were meticulously developed. And the safety of the proposed authentication mechanism was analyzed, and through performance analysis with existing authentication mechanisms, we confirmed an efficiency improvement of approximately 8%. Additionally, this paper presents directions for future research on lightweight protocols and security strategies for the application of the proposed technology, aiming to enhance security.

• IMMUNE NETWORK
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• v.24 no.2
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• pp.17.1-17.16
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• 2024

We have reported that anterior cruciate ligament (ACL) injury leads to the differential dysregulation of the complement system in the synovium as compared to meniscus tear (MT) and proposed this as a mechanism for a greater post-injury prevalence of post traumatic osteoarthritis (PTOA). To explore additional roles of complement proteins and regulators, we determined the presence of decay-accelerating factor (DAF), C5b, and membrane attack complexes (MACs, C5b-9) in discarded surgical synovial tissue (DSST) collected during arthroscopic ACL reconstructive surgery, MT-related meniscectomy, osteoarthritis (OA)-related knee replacement surgery and normal controls. Multiplexed immunohistochemistry was used to detect and quantify complement proteins. To explore the involvement of body mass index (BMI), after these 2 injuries, we examined correlations among DAF, C5b, MAC and BMI. Using these approaches, we found that synovial cells after ACL injury expressed a significantly lower level of DAF as compared to MT (p<0.049). In contrast, C5b staining synovial cells were significantly higher after ACL injury (p<0.0009) and in OA DSST (p<0.039) compared to MT. Interestingly, there were significantly positive correlations between DAF & C5b (r=0.75, p<0.018) and DAF & C5b (r=0.64 p<0.022) after ACL injury and MT, respectively. The data support that DAF, which should normally dampen C5b deposition due to its regulatory activities on C3/C5 convertases, does not appear to exhibit that function in inflamed synovia following either ACL injury or MT. Ineffective DAF regulation may be an additional mechanism by which relatively uncontrolled complement activation damages tissue in these injury states.

• The Korean Journal of Air & Space Law and Policy
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• v.32 no.2
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• pp.155-188
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• 2017

On the one hand, the 4th Industrial Revolution provides a positive opportunity to build a new civilization paradigm for mankind. However, on the other hand, due to the 4th Industrial Revolution, artificial intelligence such as 'Goggle Alpha Go' revolutionized and even the human ability was replaced with a 'Silicon Chip' as the opportunity to communicate decreases, the existence of human beings is weakened. And there is a growing concern that the number of violent crimes, such as psychopath, which hunts humans as games, will increase. Moreover, recent international terrorism is being developed in a form similar to 'Psychopathic Violent-Crime' that indiscriminately attacks innocent people. So, the probability that terrorist organizations abuse the positive effects provided by the Fourth Industrial Revolution as means of terrorism is increasing. Therefore, the paradigm of aviation terrorism is expected to change in a way that attacks airport facilities and users rather than aircraft. Because airport facilities are crowded, and psychopathic terrorists are easily accessible. From this point of view, our counter terrorism system of aviation has many weak points in various aspects such as: (1) limitations of counter-terrorism center (2) inefficient on-site command and control system (3) separated organization for aviation security consultation (4) dispersed information collection function in government (5) vulnerable to cyber attack (6) lack of international cooperation network for aviation terrorism. Consequently, it is necessary to improve the domestic counter terrorism system of aviation so as to preemptively respond to the international terrorism. This study propose the following measures to improve the aviation security system by (1) create 'Aviation Special Judicial Police' (2) revise the anti-terrorism law and aviation security law (3) Strengthening the ability respond to terrorism in cyberspace (4) building an international cooperation network for aviation terrorism.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.175-181
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• 2024

The U.N. Security Council's North Korea Sanctions Committee estimated that the amount of North Korea's cyberattacks on virtual asset-related companies from 2017 to 2023 was about 4 trillion won. North Korea's cyberattacks have secured funds through cryptocurrency hacking as it has been restricted from securing foreign currency due to economic sanctions by the international community, and it also shows the form of technology theft against defense companies, and illegal assets are being used to maintain the Kim Jong-un regime and develop nuclear and missile development. When North Korea conducted its sixth nuclear test on September 3, 2017, and declared the completion of its national nuclear armament following the launch of an intercontinental ballistic missile on November 29 of the same year, the U.N. imposed sanctions on North Korea, which are considered the strongest economic sanctions in history. In these difficult economic situations, North Korea tried to overcome the crisis through cyberattacks, but as a result of analyzing the changes through the North's cyber attack cases, the strategic goal from the first period from 2009 to 2016 was to verify and show off North Korea's cyber capabilities through the neutralization of the national network and the takeover of information, and was seen as an intention to create social chaos in South Korea. When foreign currency earnings were limited due to sanctions against North Korea in 2016, the second stage seized virtual currency and secured funds to maintain the Kim Jong-un regime and advance nuclear and missile development. The third stage is a technology hacking of domestic and foreign defense companies, focusing on taking over key technologies to achieve the five strategic weapons tasks proposed by Chairman Kim Jong-un at the 8th Party Congress in 2021. At the national level, security measures for private companies as well as state agencies should be established against North Korea's cyberattacks, and measures for legal systems, technical problems, and budgets related to science are urgently needed. It is also necessary to establish a system and manpower to respond to the ever-developing cyberattacks by focusing on cultivating and securing professional manpower such as white hackers.