• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.86-91
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• 2022

Cache bypassing is a scheme to prevent unnecessary cache blocks from occupying the capacity of the cache for avoiding cache contamination. This method is introduced to alleviate the problems of non-volatile memories (NVMs)-based memory system. However, the prior works have been studied without considering wear-out attack. Malicious writing to a small area in NVMs leads to the failure of the system due to the limited write endurance of NVMs. This paper proposes a novel scheme to prolong the lifetime with higher resistance for the wear-out attack. First, the memory reference pattern is found by modified reuse distance calculation for each cache block. If a cache block is determined as the target of the attack, it is forwarded to higher level cache or main memory without updating the NVM-based cache. The experimental results show that the write endurance is improved by 14% on average and 36% on maximum.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.2
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• pp.431-438
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• 2019

Fileless malware uses memory injection attacks to hide traces of payloads to perform malicious works. During the memory injection attack, an attack named "process hollowing" is a method of creating paused benign process like system processes. And then injecting a malicious payload into the benign process allows malicious behavior by pretending to be a normal process. In this paper, we propose a method to detect the memory injection regardless of whether or not the malicious action is actually performed when a process hollowing attack occurs. The replication process having same execution condition as the process of suspending the memory injection is executed, the data set belonging to each process virtual memory area is compared using the fuzzy hash, and the similarity is calculated.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.5
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• pp.3121-3131
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• 2014

Vulnerabilities related to memory have been known as major threats to the security of a computer system. Actually, the number of attacks using memory vulnerability has been increased. Accordingly, various memory protection mechanisms have been studied and implemented on operating system while new attack techniques bypassing the protection systems have been developed. Especially, buffer overflow attacks have been developed as Return-Oriented Programing(ROP) and Jump-Oriented Programming(JOP) called Code Re-used attack to bypass the memory protection mechanism. Thus, in this paper, I analyzed code re-use attack techniques emerged recently among attacks related to memory, as well as analyzed various detection mechanisms proposed previously. Based on the results of the analyses, a mechanism that could detect various code re-use attacks on a binary level was proposed. In addition, it was verified through experiments that the proposed mechanism could detect code re-use attacks effectively.

• ETRI Journal
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• v.41 no.5
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• pp.560-573
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• 2019

Two supervised learning algorithms, a basic neural network and a long short-term memory recurrent neural network, are applied to traffic including DDoS attacks. The joint effects of preprocessing methods and hyperparameters for machine learning on performance are investigated. Values representing attack characteristics are extracted from datasets and preprocessed by two methods. Binary classification and two optimizers are used. Some hyperparameters are obtained exhaustively for fast and accurate detection, while others are fixed with constants to account for performance and data characteristics. An experiment is performed via TensorFlow on three traffic datasets. Three scenarios are considered to investigate the effects of learning former traffic on sequential traffic analysis and the effects of learning one dataset on application to another dataset, and determine whether the algorithms can be used for recent attack traffic. Experimental results show that the used preprocessing methods, neural network architectures and hyperparameters, and the optimizers are appropriate for DDoS attack detection. The obtained results provide a criterion for the detection accuracy of attacks.

• The KIPS Transactions:PartC
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• v.18C no.2
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• pp.89-96
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• 2011

ASR is an excellent program security technique that protects various data memory areas without run-time overhead. ASR hides the addresses of variables from attackers by reordering variables within a data memory area; however, it can be broken by brute force attacks because of a limited data memory space. In this paper, we propose Multiple ASR to overcome the limitation of previous ASR approaches. Multiple ASR separates a data memory area into original and duplicated areas, and compares variables in each memory area to detect an attack. In original and duplicated data memory areas variables are arranged in the opposite order. This makes it impossible to overwrite the same variables in the different data areas in a single attack. Although programs with Multiple ASR show a relatively high run-time overhead due to duplicated execution, programs with many I/O operations such as web servers, a favorite attack target, show 40~50% overhead. In this paper we develop and test a tool that transforms a program into one with Multiple ASR applied.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1272-1290
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• 2013

Privacy-preserving collaborative filtering schemes are becoming increasingly popular because they handle the information overload problem without jeopardizing privacy. However, they may be susceptible to shilling or profile injection attacks, similar to traditional recommender systems without privacy measures. Although researchers have proposed various privacy-preserving recommendation frameworks, it has not been shown that such schemes are resistant to profile injection attacks. In this study, we investigate two memory-based privacy-preserving collaborative filtering algorithms and analyze their robustness against several shilling attack strategies. We first design and apply formerly proposed shilling attack techniques to privately collected databases. We analyze their effectiveness in manipulating predicted recommendations by experimenting on real data-based benchmark data sets. We show that it is still possible to manipulate the predictions significantly on databases consisting of masked preferences even though a few of the attack strategies are not effective in a privacy-preserving environment.

• International journal of advanced smart convergence
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• v.5 no.4
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• pp.54-56
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• 2016

Code-reuse attacks are very dangerous in various systems. This is because they do not inject malicious codes into target systems, but reuse the instruction sequences in executable files or libraries of target systems. Moreover, code-reuse attacks could be more harmful to IoT systems in the sense that it may not be easy to devise efficient and effective mechanism for code-reuse attack detection in resource-restricted IoT devices. In this paper, we propose a detection scheme with using Kullback-Leibler (KL) divergence to combat against code-reuse attacks in IoT. Specifically, we detect code-reuse attacks by calculating KL divergence between the probability distributions of the packets that generate from IoT devices and contain code region addresses in memory system and the probability distributions of the packets that come to IoT devices and contain code region addresses in memory system, checking if the computed KL divergence is abnormal.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1944-1956
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• 2016

The Digital Video Broadcasting-Common Scrambling Algorithm is an ETSI-designated algorithm designed for protecting MPEG-2 signal streams, and it is universally used. Its structure is a typical hybrid symmetric cipher which contains stream part and block part within a symmetric cipher, although the entropy is 64 bits, there haven't any effective cryptanalytic results up to now. This paper studies the security level of CSA against impossible differential cryptanalysis, a 20-round impossible differential for the block cipher part is proposed and a flaw in the cipher structure is revealed. When we attack the block cipher part alone, to recover 16 bits of the initial key, the data complexity of the attack is O(2^44.5), computational complexity is O(2^22.7) and memory complexity is O(2^10.5) when we attack CSA-BC reduced to 21 rounds. According to the structure flaw, an attack on CSA with block cipher part reduced to 21 rounds is proposed, the computational complexity is O(2^21.7), data complexity is O(2^43.5) and memory complexity is O(2^10.5), we can recover 8 bits of the key accordingly. Taking both the block cipher part and stream cipher part of CSA into consideration, it is currently the best result on CSA which is accessible as far as we know.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.3
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• pp.143-155
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• 2006

ARIA is a 128-bit block cipher, which became a Korean Standard in 2004. According to recent research, this cipher is attacked by first order DPA attack. In this paper, we propose a new masking technique as a countermeasure against first order DPA attack and apply it to the ARIA. The proposed method is suitable for low memory environment. By using this countermeasure, we verified that it is secure against first order DPA attack. In addition, our method based on precomputation of inverse table can reduce the computational cost as increasing the number of S-BOX masking.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1341-1368
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• 2024

The concept of privacy-preserving collaborative filtering (PPCF) has been gaining significant attention. Due to the fact that model-based recommendation methods with privacy are more efficient online, privacy-preserving memory-based scheme should be avoided in favor of model-based recommendation methods with privacy. Several studies in the current literature have examined ant colony clustering algorithms that are based on non-privacy collaborative filtering schemes. Nevertheless, the literature does not contain any studies that consider privacy in the context of ant colony clustering-based CF schema. This study employed the ant colony clustering model-based PPCF scheme. Attacks like shilling or profile injection could potentially be successful against privacy-preserving model-based collaborative filtering techniques. Afterwards, the scheme's robustness was assessed by conducting a shilling attack using six different attack models. We utilize masked data-based profile injection attacks against a privacy-preserving ant colony clustering-based prediction algorithm. Subsequently, we conduct extensive experiments utilizing authentic data to assess its robustness against profile injection attacks. In addition, we evaluate the resilience of the ant colony clustering model-based PPCF against shilling attacks by comparing it to established PPCF memory and model-based prediction techniques. The empirical findings indicate that push attack models exerted a substantial influence on the predictions, whereas nuke attack models demonstrated limited efficacy.