• The Magazine of the IEIE
• /
• v.40 no.8
• /
• pp.40-48
• /
• 2013

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.10
• /
• pp.25-32
• /
• 2019

In this paper, we propose a method to apply the attack surface expansion through decoy traps to a protected server network. The network consists of a large number of decoys and protected servers. In the network, each protected server dynamically mutates its IP address and port numbers based on Hidden Tunnel Networking that is a network-based moving target defense scheme. The moving target defense is a new approach to cyber security and continuously changes system's attack surface to prevent attacks. And, the attack surface expansion is an approach that uses decoys and decoy groups to protect attacks. The proposed method modifies the NAT table of the protected server with a custom chain and a RETURN target in order to make attackers waste all their time and effort in the decoy traps. We theoretically analyze the attacker success rate for the protected server network before and after applying the proposed method. The proposed method is expected to significantly reduce the probability that a protected server will be identified and compromised by attackers.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.10
• /
• pp.1311-1319
• /
• 2019

Currently, the web environment is a commonly used area for sharing information and conducting business. It is becoming an attack point for external hacking targeting on personal information leakage or system failure. Conventional signature-based detection is used in cyber threat but signature-based detection has a limitation that it is difficult to detect the pattern when it is changed like polymorphism. In particular, injection attack is known to the most critical security risks based on web vulnerabilities and various variants are possible at any time. In this paper, we propose a novelty detection technique to detect abnormal state that deviates from the normal state on web-server log dataset(WSLD). The proposed method is a machine learning-based technique to detect a minor anomalous data that tends to be different from a large number of normal data after replacing strings in web-server log dataset with vectors using machine learning-based embedding algorithm.

• KIPS Transactions on Computer and Communication Systems
• /
• v.3 no.5
• /
• pp.163-172
• /
• 2014

The Scale Invariant Feature Transform (SIFT) has been widely used in a lot of applications for image feature matching. Such a transform allows us to strong matching ability, stability in rotation, and scaling with the variety of different scales. Recently, it has been made one of the most successful algorithms in the research areas of copy-move forgery detections. Though this transform is capable of identifying copy-move forgery, it does not widely address the possibility that counter-forensics operations may be designed and used to hide the evidence of image tampering. In this paper, we propose a targeted counter-forensics method for impeding SIFT-based copy-move forgery detection by applying a semantically admissible distortion in the processing tool. The proposed method allows the attacker to delude a similarity matching process and conceal the traces left by a modification of SIFT keypoints, while maintaining a high fidelity between the processed images and original ones under the semantic constraints. The efficiency of the proposed method is supported by several experiments on the test images with various parameter settings.

• KIPS Transactions on Computer and Communication Systems
• /
• v.2 no.8
• /
• pp.357-368
• /
• 2013

Our society has evolved into a fully connected society in a mixed reality environment enabling various knowledge sharing / management / control / creation due to the expansion of broadband ICT infrastructure, smart devices, cloud services and social media services. Therefore cyber threats have increased with the convenience. The society of the future can cause more complex and subtle problems, if you do not have an effective response to cyber threats, due to fusion of logical space and physical space, organic connection of the smart object and the universalization of fully connected society. In this paper, we propose the strategy to build knowledge-base as the basis to actively respond to new cyber threats caused by future various environmental changes and the universalization of fully connected society.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.27 no.4
• /
• pp.961-970
• /
• 2017

The differential power analysis calculates the intermediate value related to sensitive information and substitute into the power model to obtain (hypothesized) power consumption. After analyzing the calculated power consumption and measuring power consumption, the secret information value can be obtained. Hamming weight and hamming distance models are most commonly used power consumption model, and the power consumption model is obtained through the modeling technique. If the power consumption model assumed by the actual equipment differs from the power consumption of the actual equipment, the side channel analysis performance is declined. In this paper, we propose a method that records measured power consumption and exploits as power consumption model. The proposed method uses the power consumption at the time when the information (plain text, cipher text, etc.) available in the encryption process. The proposed method does not need template in advance and uses the power consumption measured by the actual equipment, so it accurately reflects the power consumption model of the equipment.. Simulation and experiments show that by using our proposed method, side channel analysis is improved on the existing power modeling method.

• Smart Media Journal
• /
• v.11 no.11
• /
• pp.40-48
• /
• 2022

In this paper, an educational kit that can be used in AI education was developed to solve the difficulties of AI education. Through this, object detection and person detection in computer vision using CNN and OpenCV to learn practical-oriented experiences from theory-centered and user image recognition (Your Own) that learns and recognizes specific objects Image Recognition), user object classification (Segmentation) and segmentation (Classification Datasets), IoT hardware control that attacks the learned target, and Jetson Nano GPIO, an AI board, are developed and utilized to develop and utilize textbooks that help effective AI learning made it possible.