• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.330-334
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• 2017

Container-based virtualization reduces performance overhead compared with other virtualization technologies and guarantees an isolation of each virtual execution environment. So, it is being studied to block access to host resources or container resources for sandboxing in restricted system resource like embedded devices. However, because security threats which are caused by security vulnerabilities of the host OS or the security issues of the host environment exist, the needs of the technology to prevent an illegal accesses and unauthorized behaviors by malware has to be increased. In this paper, we define additional access permissions to access a virtual execution environment newly and control them in kernel space to protect attacks from illegal access and unauthorized behaviors by malware and suggest the Container Access Control to control them. Also, we suggest a way to block a loading of unauthenticated kernel driver to disable the Container Access Control running in host OS by malware. We implement and verify proposed technologies on Linux Kernel.

• The KIPS Transactions:PartC
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• v.19C no.1
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• pp.47-54
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• 2012

Sharing cross-site resources has been adopted by many recent websites in the forms of service-mashup and social network services. In this change, exploitation of the new vulnerabilities increases, which includes inserting malicious codes into the interaction points between clients and services instead of attacking the websites directly. In this paper, we present a system model to identify malicious script codes in the web contents by means of a remote verification while the web contents downloaded from multiple trusted origins are executed in a client's browser space. Our system classifies verification items according to the origin of request based on the information on the service code implementation and stores the verification results into three databases composed of white, gray, and black lists. Through the experimental evaluations, we have confirmed that our system provides clients with increased security by effectively detecting malicious scripts in the mashup web environment.

• Journal of Internet Computing and Services
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• v.16 no.5
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• pp.1-9
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• 2015

As attacks in cyber space become advanced and complex, monotonous defense approach of one-one matching manner between attack and defense may be limited to defend them. More efficient defense method is required. This paper proposes multi layers security scheme that can support to defend assets against diverse cyber attacks in systematical and adaptive. We model multi layers security scheme based on Defense Zone including several defense layers and also discuss essential technical elements necessary to realize multi layers security scheme such as cyber threats analysis and automated assignment of defense techniques. Also effects of multi layers security scheme and its applicability are explained. In future, for embodiment of multi layers security scheme, researches about detailed architecture design for Defense Zone, automated method to select the best defense technique against attack and modeling normal state of asset for attack detection are needed.

• Convergence Security Journal
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• v.18 no.5_1
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• pp.45-51
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• 2018

As data utilization and importance becomes important, data-related accidents and damages are gradually increasing. Especially, insider threats are the most harmful threats. And these insider threats are difficult to detect by traditional security systems, so rule-based abnormal behavior detection method has been widely used. However, it has a lack of adapting flexibly to changes in new attacks and new environments. Therefore, in this paper, we propose an adaptive anomaly movement detection framework based on a statistical Markov model to detect insider threats in advance. This is designed to minimize false positive rate and false negative rate by adopting environment factors that directly influence the behavior, and learning data based on statistical Markov model. In the experimentation, the framework shows good performance with a high F2-score of 0.92 and suspicious behavior detection, which seen as a normal behavior usually. It is also extendable to detect various types of suspicious activities by applying multiple modeling algorithms based on statistical learning and environment factors.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.4
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• pp.739-751
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• 2019

Security in embedded devices has become a significant issue. Threats on the sup-ply chain, like using counterfeit components or inserting backdoors intentionally are one of the most significant issues in embedded devices security. To mitigate these threats, high-level security evaluation and certification more than EAL (Evaluation Assurance Level) 5 on CC (Common Criteria) are necessary on hardware components, especially on the cryptographic module such as AES. High-level security evaluation and certification require detecting covert channel such as backdoors on the cryptographic module. However, previous studies have a limitation that they cannot detect some kinds of backdoors which leak the in-formation recovering a secret key on the cryptographic module. In this paper, we present an expanded definition of backdoor on hardware AES module and show how to detect the backdoor which is never detected in Verilog HDL using model checker NuSMV.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.3
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• pp.373-385
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• 2021

To provide convenience and safety of drivers, the recent vehicles are being equipped with a number of electronic control units (ECUs). Multiple ECUs construct a network inside a vehicle to share information related to the vehicle's status; in addition, the CAN protocol is normally applied. As the modern vehicles provide highly convenient and safe services, it provides many types of attack surfaces; as a result, it makes them vulnerable to cyber attacks. The automotive IDS (Intrusion Detection System) is one of the promising techniques for securing vehicles. However, the existing methods for automotive IDS are able to analyze only periodic messages. If someone attacks on non-periodic messages, the existing methods are not able to properly detect the intrusion. In this paper, we present a method to detect intrusions including an attack using non-periodic messages. Moreover, we evaluate our method on the real vehicles, where we show that our method has 0% of FPR and 0% of FNR under our attack model.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1031-1041
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• 2020

Cloud computing technology plays an important role in the deep learning industry as deep learning services are deployed frequently on top of cloud infrastructures. In such cloud environment, virtualization technology provides logically independent and isolated computing space for each tenant. However, recent studies demonstrate that by leveraging vulnerabilities of virtualization techniques and shared processor architectures in the cloud system, various side-channels can be established between cloud tenants. In this paper, we propose a novel attack scenario that can steal internal information of deep learning models by exploiting the Meltdown vulnerability in a multi-tenant system environment. On the basis of our experiment, the proposed attack method could extract internal information of a TensorFlow deep-learning service with 92.875% accuracy and 1.325kB/s extraction speed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.17-22
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• 2022

In this paper, we design a lightweight embedded device that can support intelligent edge computing, and show that the device quickly detects an object in an image input from a camera device in real time. The proposed system can be applied to environments without pre-installed infrastructure, such as an intelligent video control system for industrial sites or military areas, or video security systems mounted on autonomous vehicles such as drones. The On-Device AI(Artificial intelligence) technology is increasingly required for the widespread application of intelligent vision recognition systems. Computing offloading from an image data acquisition device to a nearby edge device enables fast service with less network and system resources than AI services performed in the cloud. In addition, it is expected to be safely applied to various industries as it can reduce the attack surface vulnerable to various hacking attacks and minimize the disclosure of sensitive data.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.4
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• pp.125-144
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• 2022

When a variable message signs (VMS) system displays false information related to traffic safety caused by malicious attacks, it could pose a serious risk to drivers. If the normal message patterns displayed on the VMS system are learned, it would be possible to detect and respond to the anomalous messages quickly. This paper proposes a method for detecting anomalous messages by learning the normal patterns of messages using a bi-directional generative pre-trained transformer (GPT) network. In particular, the proposed method was trained using the normal messages and their system parameters to minimize the corresponding negative log-likelihood (NLL) values. After adequate training, the proposed method could detect an anomalous message when its NLL value was larger than a pre-specified threshold value. The experiment results showed that the proposed method could detect malicious messages and cases when the system error occurs.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.751-763
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• 2022

With the recent increase in spending growth in the IoT sector worldwide, the importance of lightweight block ciphers to encrypt them is also increasing. The lightweight block cipher PIPO algorithm proposed in ICISC 2020 is an SPN-structured cipher using an unbalanced bridge structure. The white box attack model refers to a state in which an attacker may know the intermediate value of the encryption operation. As a technique to cope with this, Chow et al. proposed a white box implementation technique and applied it to DES and AES in 2002. In this paper, we propose a white box PIPO applying a white box implementation to a lightweight block cipher PIPO algorithm. In the white box PIPO, the size of the table decreased by about 5.8 times and the calculation time decreased by about 17 times compared to the white box AES proposed by Chow and others. In addition, white box PIPO was used for mobile security products, and experimental results for each test case according to the scope of application are presented.