• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.33 no.6
• /
• pp.1111-1123
• /
• 2023

In recent years, as generative models have developed, research that threatens them has also been actively conducted. We propose a new membership inference attack against text-to-image model. Existing membership inference attacks on Text-to-Image models produced a single image as captions of query images. On the other hand, this paper uses personalized embedding in query images through Textual Inversion. And we propose a membership inference attack that effectively generates multiple images as a method of generating Adversarial Prompt. In addition, the membership inference attack is tested for the first time on the Stable Diffusion model, which is attracting attention among the Text-to-Image models, and achieve an accuracy of up to 1.00.

• Journal of Internet Computing and Services
• /
• v.16 no.2
• /
• pp.27-40
• /
• 2015

Inferring firewall policy is to discover firewall policy by analyzing response packets as results of active probing without any prior information. However, a brute-force approach for generating probing packets is unavailable because the probing packets may be regarded as attack traffic and blocked by attack detection threshold of a firewall. In this paper, we propose a firewall policy inference method using an efficient probing algorithm which considers the number of source IP addresses, maximum probing packets per second and interval size of adjacent sweep lines as inference parameters to avoid detection. We then verify whether the generated probing packets are classified as network attack patterns by a firewall, and present the result of evaluation of the correctness by comparing original firewall policy with inferred firewall policy.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.7
• /
• pp.3671-3689
• /
• 2019

Software defined networking brings unique security risks such as control plane saturation attack while enhancing the performance of wireless sensor networks. The attack is a new type of distributed denial of service (DDoS) attack, which is easy to launch. However, it is difficult to detect and hard to defend. In response to this, the attack threat model is discussed firstly, and then a DDoS attack prevention extension, called FuzzyGuard, is proposed. In FuzzyGuard, a control network with both the protection of data flow and the convergence of attack flow is constructed in the data plane by using the idea of independent routing control flow. Then, the attack detection is implemented by fuzzy inference method to output the current security state of the network. Different probabilistic suppression modes are adopted subsequently to deal with the attack flow to cost-effectively reduce the impact of the attack on the network. The prototype is implemented on SDN-WISE and the simulation experiment is carried out. The evaluation results show that FuzzyGuard could effectively protect the normal forwarding of data flow in the attacked state and has a good defensive effect on the control plane saturation attack with lower resource requirements.

• Computers and Concrete
• /
• v.5 no.6
• /
• pp.573-597
• /
• 2008

Among artificial intelligence-based computational techniques, adaptive neuro-fuzzy inference systems (ANFIS) are particularly suitable for modelling complex systems with known input-output data sets. Such systems can be efficient in modelling non-linear, complex and ambiguous behaviour of cement-based materials undergoing single, dual or multiple damage factors of different forms (chemical, physical and structural). Due to the well-known complexity of sulfate attack on cement-based materials, the current work investigates the use of ANFIS to model the behaviour of a wide range of self-consolidating concrete (SCC) mixture designs under various high-concentration sodium sulfate exposure regimes including full immersion, wetting-drying, partial immersion, freezing-thawing, and cyclic cold-hot conditions with or without sustained flexural loading. Three ANFIS models have been developed to predict the expansion, reduction in elastic dynamic modulus, and starting time of failure of the tested SCC specimens under the various high-concentration sodium sulfate exposure regimes. A fuzzy inference system was also developed to predict the level of aggression of environmental conditions associated with very severe sodium sulfate attack based on temperature, relative humidity and degree of wetting-drying. The results show that predictions of the ANFIS and fuzzy inference systems were rational and accurate, with errors not exceeding 5%. Sensitivity analyses showed that the trends of results given by the models had good agreement with actual experimental results and with thermal, mineralogical and micro-analytical studies.

• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.8
• /
• pp.878-882
• /
• 2010

DaaS model that surrogates their data has a problem of privacy leakage by service provider. In this paper, we analyze inference attack that can occur on encrypted data that consist of multiple column through index, and we suggest b-anonymity to protect data against inference attack. We use R+-tree technique to minimize false-positive that can happen when we use an index for efficiency of data processing.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.26 no.1
• /
• pp.41-47
• /
• 2016

In Black hat USA 2014, a hacking method to infer the password entry of smartphone or smartpad with google glass in distance is presented. In this paper, we design the secure keypad to protect the key stroke inference attacks with google glass which has unique layout ensuring same input entry but different input value.

• Proceedings of the Korea Concrete Institute Conference
• /
• 2005.05b
• /
• pp.237-240
• /
• 2005

As a part of the effort for improving the durability design based on a set of the deem-to-satisfy specifications, it is important and primary to quantitatively identify the environmental impact to a target reinforced concrete structure. In this work, an effort is made to quantitatively calculate the environmental affecting factor with using a fuzzy inference that it indicates the severity of environmental impact to the exposed reinforced concrete structure or member. This system is composed of input region, output region and rule base. For developing the fuzzy inference system surface chloride concentration{chloride), cyclic degree of wet and dry(CWD), relative humidity(RH) and temperature (TEMP) were selected as the input parameter to environmental affecting factor(EAF) of output parameter. The Rules in inference engine are generated from the engineering knowledge and intuition based on some international code of practises as well as various researcher's experimental data. The devised fuzzy inference system was verified comparing the inferred value with the investigation data, and proved to be validated. Thus it is anticipated that this system for quantifying EAF is certain to be considered into the starting point to develop the performance-based durability design considering the service life of structure.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.32 no.5
• /
• pp.817-826
• /
• 2022

AI technology is being successfully introduced in many fields, and models deployed as a service are deployed with black box environment that does not expose the model's information to protect intellectual property rights and data. In a black box environment, attackers try to steal data or parameters used during training by using model output. This paper proposes a method of inferring the type of model to directly find out the composition of layer of the target model, based on the fact that there is no attack to infer the information about the type of model from the deep learning model. With ResNet, VGGNet, AlexNet, and simple convolutional neural network models trained with MNIST datasets, we show that the types of models can be inferred using the output values in the gray box and black box environments of the each model. In addition, we inferred the type of model with approximately 83% accuracy in the black box environment if we train the big and small relationship feature that proposed in this paper together, the results show that the model type can be infrerred even in situations where only partial information is given to attackers, not raw probability vectors.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.5
• /
• pp.2086-2098
• /
• 2018

Security plays a vital role and is the key challenge in Mobile Ad-hoc Networks (MANET). Infrastructure-less nature of MANET makes it arduous to envisage the genre of topology. Due to its inexhaustible access, information disseminated by roaming nodes to other nodes is susceptible to many hazardous attacks. Intrusion Detection and Prevention System (IDPS) is undoubtedly a defense structure to address threats in MANET. Many IDPS methods have been developed to ascertain the exceptional behavior in these networks. Key issue in such IDPS is lack of fast self-organized learning engine that facilitates comprehensive situation awareness for optimum decision making. Proposed "Intelligent Behavioral Hybridized Intrusion Detection and Prevention System (IBH_IDPS)" is built with computational intelligence to detect complex multistage attacks making the system robust and reliable. The System comprises of an Intelligent Client Agent and a Smart Server empowered with fuzzy inference rule-based service engine to ensure confidentiality and integrity of network. Distributed Intelligent Client Agents incorporated with centralized Smart Server makes it capable of analyzing and categorizing unethical incidents appropriately through unsupervised learning mechanism. Experimental analysis proves the proposed model is highly attack resistant, reliable and secure on devices and shows promising gains with assured delivery ratio, low end-to-end delay compared to existing approach.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.19 no.1
• /
• pp.57-65
• /
• 2016

Threat evaluation is a process to estimate the threat score which enemy aerial threat poses to defended assets. The objective of threat evaluation is concerned with making an engagement priority list for optimal weapon allocation. Traditionally, the threat evaluation of massive air threats has been carried out by air defence experts, but the human decision making is less effective in real aerial attack situations with massive enemy fighters. Therefore, automation to enhance the speed and efficiency of the human operation is required. The automatic threat evaluation by air defense experts who will perform multi-variable judgment needs formal models to accurately quantify their linguistic evaluation of threat level. In this paper we propose a threat evaluation model by using a fuzzy rule-based inference method. Fuzzy inference is an appropriate method for quantifying threat level and integrating various threat attribute information. The performance of the model has been tested with a simulation that reflected real air threat situation and it has been verified that the proposed model was better than two conventional threat evaluation models.