• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.85-94
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• 2024

In this paper, we present the very first time the generalized notion of (α, β, γ, δ) - convex (concave) function in mixed kind, which is the generalization of (α, β) - convex (concave) functions in 1^st and 2^nd kind, (s, r) - convex (concave) functions in mixed kind, s - convex (concave) functions in 1^st and 2^nd kind, p - convex (concave) functions, quasi convex(concave) functions and the class of convex (concave) functions. We would like to state the well-known Ostrowski inequality via SVN-Riemann Integrals for (α, β, γ, δ) - convex (concave) function in mixed kind. Moreover we establish some SVN-Ostrowski type inequalities for the class of functions whose derivatives in absolute values at certain powers are (α, β, γ, δ)-convex (concave) functions in mixed kind by using different techniques including Hölder's inequality and power mean inequality. Also, various established results would be captured as special cases with respect to convexity of function.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.107-114
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• 2023

The attack technique by the malware distribution form is a dangerous, difficult to detect and prevent attack method. Current malware detection studies and proposals are often based on two main methods: using sign sets and analyzing abnormal behaviors using machine learning or deep learning techniques. This paper will propose a method to detect malware on Endpoints based on Event IDs using deep learning. Event IDs are behaviors of malware tracked and collected on Endpoints' operating system kernel. The malware detection proposal based on Event IDs is a new research approach that has not been studied and proposed much. To achieve this purpose, this paper proposes to combine different data mining methods and deep learning algorithms. The data mining process is presented in detail in section 2 of the paper.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.205-211
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• 2024

The increasing number of botnet attacks incorporating new evasion techniques making it infeasible to completely secure complex computer network system. The botnet infections are likely to be happen, the timely detection and response to these infections helps to stop attackers before any damage is done. The current practice in traditional IP networks require manual intervention to response to any detected malicious infection. This manual response process is more probable to delay and increase the risk of damage. To automate this manual process, this paper proposes to automatically select relevant countermeasures for detected botnet infection. The propose approach uses the concept of flow trace to detect botnet behavior patterns from current and historical network activity. The approach uses the multiclass machine learning based approach to detect and classify the botnet activity into IRC, HTTP, and P2P botnet. This classification helps to calculate the risk score of the detected botnet infection. The relevant countermeasures selected from available pool based on risk score of detected infection.

• Annual Conference of KIPS
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• 2024.05a
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• pp.395-396
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• 2024

최근 국가 핵심 기반시설을 중단시키거나 파괴시킴으로서 사회적 혼란 및 국가 경제적 손실을 일으키는 공격 사례가 증가되고 있는 실정이다. 이와 같은 사이버 공격에 대응하기 위해 각 국가는 인터넷이나 다른 네트워크와 물리적 또는 논리적으로 분리되어 있는 폐쇄망 환경을 기반으로 기반시설을 구성함으로서 높은 수준의 보안성과 안정성을 유지하고자 한다. 하지만, 악의적인 공격자들은 Covert Channel을 통해 폐쇄망 환경 내 민감한 데이터 및 기밀 데이터를 탈취하고 있는 실정이다. 이에 본 논문에서는 음향신호 기반 Covert Channel 공격 기술에 대해 분석함으로써 안전한 폐쇄망 환경 구축의 필요성을 보이고자 한다.

• Annual Conference of KIPS
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• 2024.05a
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• pp.345-346
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• 2024

디지털 기술과 정보통신 기술이 발전하면서 디지털 콘텐츠의 불법복제 및 유통으로 인한 저작권 침해 피해가 증가하고 있다. 저작권 침해 문제를 예방하기 위해 다양한 디지털 워터마킹 기술이 제안되었지만, 디지털 이미지 워터마킹은 이미지에 기하학적 변형을 가하면 삽입된 워터마크가 훼손되어 탐지가 어렵다는 문제가 있다. 본 연구에서는 왜곡 공격에 강인한 상관관계 측정 기반 워터마킹 기법을 제안한다. 제안한 방식은 교차 상관 기법을 이용해 이미지와 워터마크의 상관관계를 계산하고 임계값과 비교하여 공간 영역에서의 비가시성 워터마크의 존재 여부를 검증할 수 있는 디지털 워터마킹 방법이다. 실험 결과에 따르면 표준편차 120의 가우시안 노이즈 공격을 가해도 원본 워터마크와 0.1 이상의 상관관계를 보이며, 종래의 방식보다 높은 탐지 성능을 나타냈다.

• Annual Conference of KIPS
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• 2024.05a
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• pp.331-332
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• 2024

최근 디지털 워터마킹 기술은 디지털 콘텐츠의 저작권 보호 및 추적을 위해 활용되고 있다. 그러나 종래의 워터마킹 기술은 이미지에 워터마크 이미지 전체를 삽입하기 때문에 왜곡 공격에 취약하다. 이러한 문제를 해결하기 위해 본 연구에서는 워터마크 분할 삽입 기법을 제안하였다. 워터마크 분할 삽입 기법을 사용하면 종래 방법 대비 20%p의 손실률이 증가하더라도 원본 워터마크를 복구할 수 있어 1.5배 향상된 성능을 보인다.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.21-29
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• 2024

Precoding of the orthogonal frequency division multiplexing (OFDM) with Walsh Hadamard transform (WHT) is known in the literature. Instead of performing WHT precoding and inverse discrete Fourier transform separately, a product of two matrix can yield a new matrix that can be applied with lower complexity. This resultant transform, T-transform, results in T-OFDM. This paper extends the limited existing work on T-OFDM significantly by presenting detailed account of its computational complexity, a lower complexity receiver design, an expression for PAPR and its cumulative distribution function (cdf), sensitivity of T-OFDM to timing synchronization errors, and novel analytical expressions signal to noise ratio (SNR) for multiple equalization techniques. Simulation results are presented to show significant improvements in PAPR performance, as well improvement in bit error rate (BER) in Rayleigh fading channel. This paper is Part II of a three-paper series on alternative transforms and many of the concepts and result refer to and stem from results in generalized multicarrier communication (GMC) system presented in Part I of this series.

• Journal of Internet Computing and Services
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• v.25 no.3
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• pp.1-8
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• 2024

As the Internet of Things (IoT) has gained significant prominence in our daily lives, most IoT devices rely on over-the-air technology to automatically update firmware or software remotely via the network connection to relieve the burden of manual updates by users. And preserving security for OTA interface is one of the main requirements to defend against potential threats. This paper presents a simulation of an attack scenario on the commoditized System-on-a-chip, ESP32 chip, utilized for drones during their OTA update process. We demonstrate three types of attacks, WiFi cracking, ARP spoofing, and TCP SYN flooding techniques and postpone the OTA update procedure on an ESP32 Drone. As in this scenario, unpatched IoT devices can be vulnerable to a variety of potential threats. Additionally, we review the chip to obtain traces of attacks from a forensics perspective and acquire memory forensic artifacts to indicate the SYN flooding attack.

• Electronics and Telecommunications Trends
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• v.39 no.5
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• pp.40-48
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• 2024

Quantum computers can likely perform computations that are unattainable by classical computers, and they represent the next generation of computing technologies. Due to high costs and complex maintenance, direct ownership of quantum computers by individuals users is challenging. Future utilization is predicted to involve quantum computing servers performing delegated computations for clients lacking quantum capabilities, similar to the current utilization of supercomputing. This delegation model allows several users to benefit from quantum computing without requiring ownership, thereby providing innovation potential in various fields. Ensuring data privacy and computational integrity in this model is critical for ensuring the reliability of quantum cloud computing services. However, these requirements are difficult to achieve because classical security techniques cannot be directly applied to quantum computing. We review research on security protocols for the delegation of quantum computing with focus on data privacy and integrity verification. Our analysis covers the background of quantum computing, privacy-preserving quantum computational models, and recent research trends. Finally, we discuss challenges and future directions for secure quantum delegated computations, highlighting their importance for the commercialization and widespread adoption of quantum computing.

• International Journal of Computer Science & Network Security
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• v.24 no.10
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• pp.25-31
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• 2024

Currently, on social media, malicious comments have emerged as a serious issue. Existing artificial intelligence-based comment classification systems have limitations due to data bias and overfitting. To address this, this study proposed a novel comment classification system that combines crowdsourcing and federated learning. This system collects data from various users and utilizes a large language model like KoBERT through federated learning to classify comments accurately while protecting user privacy. It is expected to provide higher accuracy than existing methods and improve significantly the efficiency of detecting malicious comments. The proposed system can be applied to social media platforms and online communities.