• 한국인터넷방송통신학회논문지
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• 제18권5호
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• pp.55-62
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• 2018

MTD(Moving Target Defense)는 자가 복원력이 있는 무선 통신 시스템을 구축할 수 있도록 대상 시스템의 다양한 구조 및 운영 관련 파라미터들을 동적으로 변경시키도록 설계함으로써 공격자의 악의적 공격으로부터 시스템의 취약점을 보호하는 기술이다. 본 논문에서는 MTD-SDR 기술을 기반으로 하는 통신 시스템에서 랜덤/순서적 스캐닝/재밍 공격 성공 확률에 대한 식을 유도하고 시뮬레이션을 통해 그 결과를 검증하였다. 결과적으로 랜덤 스캐닝 공격의 경우에는 전송채널 변화주기 값이 증가할수록 공격성공률이 증가하는 반면, 랜덤 재밍 공격의 경우에는 변화가 없다. 순서적 공격의 경우에는 랜덤 공격과 유사한 경향의 성공률 패턴을 보이지만 전송채널 변화주기가 커져서 전체 전송채널 수에 접근할수록 재밍 공격의 경우에는 최대 2배, 스캐닝 공격의 경우에는 최대 36% 정도 랜덤 공격에 비해 공격 성공률이 높은 것으로 나타났다.

• 디지털콘텐츠학회 논문지
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• 제19권2호
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• pp.327-334
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• 2018

PC가 악성코드에 감염되면 C&C서버와 통신하며 공격자의 명령에 따라 내부 네트워크에 확산, 정보획득 등의 과정을 거쳐 최종적인 악성행위를 하게 된다. 기업은 외부로부터의 공격을 사전에 차단하는데 중점을 두고 있으나 APT공격을 목적으로 한 악성코드는 어떤 형대로든 내부로 유입된다. 이때 피해의 확산을 방지하기 위하여 악성코드에 감염되어 C&C서버와 통신을 시도하는 PC를 찾아내는 내부 모니터링이 필요하다. 본 논문에서 수많은 패킷들의 목적지IP가 블랙리스트 IP인지 여부를 빠르고 효과적으로 대조하기 위한 블룸필터를 이용한 목적지 IP 모니터링 방안을 제시한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권10호
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• pp.4191-4203
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• 2015

WebView is a vital component in smartphone platforms like Android, Windows and iOS that enables smartphone applications (apps) to embed a simple yet powerful web browser inside them. WebView not only provides the same functionalities as web browser, it, more importantly, enables a rich interaction between apps and webpages loaded inside the WebView. However, the design and the features of WebView lays path to tamper the sandbox protection mechanism implemented by browsers. As a consequence, malicious attacks can be launched either against the apps or by the apps through the exploitation of WebView APIs. This paper presents a critical attack called Supplementary Event-Listener Injection (SEI) attack which adds auxiliary event listeners, for executing malicious activities, on the HTML elements in the webpage loaded by the WebView via JavaScript Injection. This paper also proposes an automated static analysis system for analyzing WebView embedded apps to classify the kind of vulnerability possessed by them and a solution for the mitigation of the attack.

• International Journal of Computer Science & Network Security
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• 제21권12호
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• pp.213-218
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• 2021

This paper proposes a technique for detecting a significant threat that attempts to get sensitive and confidential information such as usernames, passwords, credit card information, and more to target an individual or organization. By definition, a phishing attack happens when malicious people pose as trusted entities to fraudulently obtain user data. Phishing is classified as a type of social engineering attack. For a phishing attack to happen, a victim must be convinced to open an email or a direct message [1]. The email or direct message will contain a link that the victim will be required to click on. The aim of the attack is usually to install malicious software or to freeze a system. In other instances, the attackers will threaten to reveal sensitive information obtained from the victim. Phishing attacks can have devastating effects on the victim. Sensitive and confidential information can find its way into the hands of malicious people. Another devastating effect of phishing attacks is identity theft [1]. Attackers may impersonate the victim to make unauthorized purchases. Victims also complain of loss of funds when attackers access their credit card information. The proposed method has two major subsystems: (1) Data collection: different websites have been collected as a big data corresponding to normal and phishing dataset, and (2) distributed detection system: different artificial algorithms are used: a neural network algorithm and machine learning. The Amazon cloud was used for running the cluster with different cores of machines. The experiment results of the proposed system achieved very good accuracy and detection rate as well.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권10호
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• pp.3793-3814
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• 2021

Most secure solutions like cryptography are software based and they are designed to mainly deal with the outside attacks for traditional networks, but such soft security is hard to be implemented in wireless sensor networks to counter the inside attacks from internal malicious nodes. To address this issue, reputation has been introduced to tackle the inside malicious nodes. Reputation is essentially a stimulating mechanism for nodes' cooperation and is employed to detect node misbehaviors and improve the trust-worthiness between individual nodes. Among the reputation models, binomial distribution based reputation has many advantages such as light weight and ease of implementation in resource-constraint sensor nodes, and accordingly researchers have proposed many insightful related methods. However, some of them either directly use the modelling results, apply the models through simple modifications, or only use the required components while ignoring the others as an integral part of the whole model, this topic still lacks a comprehensive and systematical review. Thus the motivation of this study is to provide a thorough survey concerning each detailed functional components of binomial distribution based reputation for wireless sensor networks. In addition, based on the survey results, we also argue some open research problems and suggest the directions that are worth future efforts. We believe that this study is helpful to better understanding the reputation modeling mechanism and its components for wireless sensor networks, and can further attract more related future studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권6호
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• pp.1657-1673
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• 2023

With the advancement of information technology, criminals employ multiple cyberspaces to promote cybercrime. To combat cybercrime and cyber dangers, banks and financial institutions use artificial intelligence (AI). AI technologies assist the banking sector to develop and grow in many ways. Transparency and explanation of AI's ability are required to preserve trust. Deep learning protects client behavior and interest data. Deep learning techniques may anticipate cyber-attack behavior, allowing for secure banking transactions. This proposed approach is based on a user-centric design that safeguards people's private data over banking. Here, initially, the attack data can be generated over banking transactions. Routing is done for the configuration of the nodes. Then, the obtained data can be preprocessed for removing the errors. Followed by hierarchical network feature extraction can be used to identify the abnormal features related to the attack. Finally, the user data can be protected and the malicious attack in the transmission route can be identified by using the Wrapper stepwise ResNet classifier. The proposed work outperforms other techniques in terms of attack detection and accuracy, and the findings are depicted in the graphical format by employing the Python tool.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(4)
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• pp.319-322
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• 2002

In this paper, we present a digital authentication technique using content-based watermarking in digital images. To digest the image contents, Hopfield network is employed on the block-based edge image. The Hopfield function extracts the same tit fur similarly looking blocks so that the values are unlikely to change to the innocuous manipulations while being changed far malicious manipulations. By inputting the extracted bit sequence with secret key to the cryptographic hash function, we generate a watermark for each block by seeding a pseudo random number generator with a hash output Therefore, the proposed authentication technique can distinguish between malicious attacks and innocuous attacks. Watermark embedding is based on the block-based spread spectrum method in DCT domain and the strength of watermark is adjusted according to the local statistics of DCT coefficients in a zig-zag scan line in AC subband. The numerical experiments show that the proposed technique is very efficient in the performance of robust authentication.

• International journal of advanced smart convergence
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• 제7권3호
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• pp.101-109
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• 2018

In this paper, we propose an adaptive filtering scheme of connection requests for the defense of malicious energy consumption attacks against wireless computing devices with limited energy budget. The energy consumption attack tries to consume the battery energy of a wireless device with repeated connection requests and shut down the wireless device by exhausting its energy budget. The proposed scheme blocks a connection request of the energy consumption attack in the middle, if the same connection request is repeated and its request result is failed continuously. In order to avoid the blocking of innocuous mistakes of normal users, the scheme gives another chance to allow connection request after a fixed blocking time. The scheme changes the blocking time adaptively by comparing the message arriving ate during non-blocking period and that during blocking period. Evaluation shows that the proposed defense scheme saves up to 94% energy consumption compared to the non-defense case.

• International journal of advanced smart convergence
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• 제12권1호
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• pp.149-156
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• 2023

OOXML-based MS-Office digital files are extensively utilized by businesses and organizations worldwide. However, OOXML-based MS-Office digital files are vulnerable to forgery and corruption attack by including hidden suspicious information, which can lead to activating malware or shell code being hidden in the file. Such malicious code can cause a computer system to malfunction or become infected with ransomware. To prevent such attacks, it is necessary to analyze and detect the corruption of OOXML-based MS-Office files. In this paper, we examine the weaknesses of the existing OOXML-based MS-Office file structure and analyzes how concealment and forgery are performed on MS-Office digital files. As a result, we propose a system to detect hidden data effectively and proactively respond to ransomware attacks exploiting MS-Office security vulnerabilities. Proposed system is designed to provide reliable and efficient detection of hidden data in OOXML-based MS-Office files, which can help organizations protect against potential security threats.

• ETRI Journal
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• 제41권6호
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• pp.739-749
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• 2019

Security and accuracy are two issues in the localization of wireless sensor networks (WSNs) that are difficult to balance in hostile indoor environments. Massive numbers of malicious positioning requests may cause the functional failure of an entire WSN. To eliminate the misjudgments caused by malicious nodes, we propose a compressive-sensing-based multiregional secure localization (CSMR_SL) algorithm to reduce the impact of malicious users on secure positioning by considering the resource-constrained nature of WSNs. In CSMR_SL, a multiregion offline mechanism is introduced to identify malicious nodes and a preprocessing procedure is adopted to weight and balance the contributions of anchor nodes. Simulation results show that CSMR_SL may significantly improve robustness against attacks and reduce the influence of indoor environments while maintaining sufficient accuracy levels.