• International Journal of Computer Science & Network Security
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• 제22권1호
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• pp.97-104
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• 2022

In recent years, data privacy has become increasingly important. The goal of network cryptography is to protect data while it is being transmitted over the internet or a network. Social media and smartphone apps collect a lot of personal data which if exposed, might be damaging to privacy. As a result, sensitive data is exposed and data is shared without the data owner's consent. Personal Information is one of the concerns in data privacy. Protecting user data and sensitive information is the first step to keeping user data private. Many applications user data can be found on other websites. In this paper, we discuss the issue of privacy and suggest a mechanism for keeping user data hidden in other applications.

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.285-291
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• 2022

WSN is the major component for information transfer in IoT environments. Source Location Privacy (SLP) has attracted attention in WSN environments. Effective SLP can avoid adversaries to backtrack and capture source nodes. This work presents a Two-Level Randomized Sector-based Routing (TLRSR) model to ensure SLP in wireless environments. Sector creation is the initial process, where the nodes in the network are grouped into defined sectors. The first level routing process identifies sector-based route to the destination node, which is performed by Ant Colony Optimization (ACO). The second level performs route extraction, which identifies the actual nodes for transmission. The route extraction is randomized and is performed using Simulated Annealing. This process is distributed between the nodes, hence ensures even charge depletion across the network. Randomized node selection process ensures SLP and also avoids depletion of certain specific nodes, resulting in increased network lifetime. Experiments and comparisons indicate faster route detection and optimal paths by the TLRSR model.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.113-120
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• 2023

One of the most prevalent disease among women that leads to death is breast cancer. It can be diagnosed by classifying tumors. There are two different types of tumors i.e: malignant and benign tumors. Physicians need a reliable diagnosis procedure to distinguish between these tumors. However, generally it is very difficult to distinguish tumors even by the experts. Thus, automation of diagnostic system is needed for diagnosing tumors. This paper attempts to improve the accuracy of breast cancer detection by utilizing deep learning convolutional neural network (CNN). Experiments are conducted using Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Compared to existing techniques, the used of CNN shows a better result and achieves 99.66%% in term of accuracy.

• International Journal of Computer Science & Network Security
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• 제23권6호
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• pp.147-154
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• 2023

Network intrusion refers to any unauthorized penetration or activity on a computer network. This upsets the confidentiality, integrity, and availability of the network system. One of the major threats to any system's availability is a Denial-of-Service (DoS) attack, which is intended to deny a legitimate user access to resources. Therefore, due to the complexity of DoS attacks, it is increasingly important to abstract and describe these attacks in a way that will be effectively detected. The automaton theory is used in this paper to implement a SYN Flood detection system based on Time-Dependent Finite Automata (TDFA).

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.155-162
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• 2024

Anonymity online has been an ever so fundamental topic among journalists, experts, cybersecurity professionals, corporate whistleblowers. Highest degree of anonymity online can be obtained by mimicking a normal everyday user of the internet. Without raising any flags of suspicion and perfectly merging with the masses of public users. Online Security is a very diverse topic, with new exploits, malwares, ransomwares, zero-day attacks, breaches occurring every day, staying updated with the latest security measures against them is quite expensive and resource intensive. Network security through anonymity focuses on being unidentifiable by disguising or blending into the public to become invisible to the targeted attacks. By following strict digital discipline, we can avoid all the malicious attacks as a whole. In this paper we have demonstrated a proof of concept and feasibility of securing yourself on a network by being anonymous.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.95-100
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• 2024

Cyclic Delay Diversity (CDD) is a diversity scheme used in OFDM-based telecommunication systems, transforming spatial diversity into frequency diversity and thus avoiding intersymbol interference without entailing the receiver to be aware of the transmission strategy making the signal more reliable achieving full diversity gain in cooperative systems. Here the analyzation of the influence of CDD-SC scheme in Cognitive Radio Network (CRN) is done with the challenge of overcoming the complication called channel estimation along with overhead in CNR. More specifically, the closed-form expressions for outage probability and symbol error rate are divided under different frequencies among independent and identically distributed (i.i.d.) frequency selective fading channel model i.e., the signal is divided into different frequencies and transmitted among several narrow band channels of different characteristics. It is useful in the reduction of interference and crosstalk. The results reveal the diversity order of the proposed system to be mainly affected by the number of multipath components that are available in the CNR.

• 통상정보연구
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• 제19권3호
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• pp.3-23
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• 2017

세계경제의 글로벌화와 함께 국가 간 무역네트워크는 지리적 근접성을 초월해 확장되고 있으며 이러한 무역네트워크의 확장은 세계화를 촉진시키는 역할을 하고 있다. 우리나라는 한 칠레 FTA를 시작으로 지난 10년 동안 명실상부한 FTA 강국으로 자리매김 했으며 단기간의 성공적인 네트워크 구축은 체결국과의 교역증대, 해외시장 점유율 상승 등 긍정적 경제효과가 나타나고 있다. 다른 국가 역시 지역경제 통합 및 양자간 무역협정 네트워크 형성으로 경제 패러다임 전환을 이루고 있어, 전체 RTA 및 FTA 네트워크에 대한 이해를 통한 새로운 기회에 대한 연구의 필요성이 제기되고 있는 시점이다. 이에 본 연구에서는 1960년대부터 현재시점까지의 세계 각국의 RTA 및 FTA 현황을 파악하고 사회네트워크분석을 통해 네트워크 구조와 중심성 분석을 실시하였다. 연구결과로 전 세계 FTA 네트워크의 구조는 점차 확장 중에 있으며 초기 유럽중심에서 아시아권 국가들의 성장이 두드러지고 있다. 중심성(Degree, Betweenness, Closeness 및 Eigenvector)분석결과 한국은 짧은 기간 동안 모든 중심성 지표에서 상위권을 차지하며 전략적으로 FTA 성과를 이룬 것으로 분석되었다. 본 연구는 FTA를 거시적 관점으로 살펴보고 이를 통해 세계 FTA의 구조와 한국의 FTA 전략을 평가하고 네트워크적 관점의 FTA 네트워크를 평가하였다.

• International Journal of Computer Science & Network Security
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• 제22권12호
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• pp.51-56
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• 2022

In space based constellation network, users are allowed to enter or leave the network arbitrarily. Hence, the number, identities and transmitted data of active users vary with time and have considerable impacts on the receiver's performance. The so-called problem of multiuser detection means identifying the identity of each active user and detecting the data transmitted by each active user. Traditional methods assume that the number of active users is equal to the maximum number of users that the network can hold. The model of traditional methods are simple and the performance are suboptimal. In this paper a Maximum A Posteriori Probability (MAP) based multiuser detection method is proposed. The proposed method models the activity state of users as Markov chain and transforms multiuser detection into searching optimal path in grid map with BCJR algorithm. Simulation results indicate that the proposed method obtains 2.6dB and 1dB E_b/N₀ gains respectively when activity detection error rate and symbol error rate reach 10^-3, comparing with reference methods.

• International Journal of Computer Science & Network Security
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• 제23권7호
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• pp.39-48
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• 2023

Breast cancer screening makes extensive utilization of mammography. Even so, there has been a lot of debate with regards to this application's starting age as well as screening interval. The deep learning technique of transfer learning is employed for transferring the knowledge learnt from the source tasks to the target tasks. For the resolution of real-world problems, deep neural networks have demonstrated superior performance in comparison with the standard machine learning algorithms. The architecture of the deep neural networks has to be defined by taking into account the problem domain knowledge. Normally, this technique will consume a lot of time as well as computational resources. This work evaluated the efficacy of the deep learning neural network like Visual Geometry Group Network (VGG Net) Residual Network (Res Net), as well as inception network for classifying the mammograms. This work proposed optimization of ResNet with Teaching Learning Based Optimization (TLBO) algorithm's in order to predict breast cancers by means of mammogram images. The proposed TLBO-ResNet, an optimized ResNet with faster convergence ability when compared with other evolutionary methods for mammogram classification.

• International Journal of Computer Science & Network Security
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• 제24권1호
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• pp.52-60
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• 2024

APT (Advanced Persistent Threat) attack is a dangerous, targeted attack form with clear targets. APT attack campaigns have huge consequences. Therefore, the problem of researching and developing the APT attack detection solution is very urgent and necessary nowadays. On the other hand, no matter how advanced the APT attack, it has clear processes and lifecycles. Taking advantage of this point, security experts recommend that could develop APT attack detection solutions for each of their life cycles and processes. In APT attacks, hackers often use phishing techniques to perform attacks and steal data. If this attack and phishing phase is detected, the entire APT attack campaign will be crash. Therefore, it is necessary to research and deploy technology and solutions that could detect early the APT attack when it is in the stages of attacking and stealing data. This paper proposes an APT attack detection framework based on the Network traffic analysis technique using open-source tools and deep learning models. This research focuses on analyzing Network traffic into different components, then finds ways to extract abnormal behaviors on those components, and finally uses deep learning algorithms to classify Network traffic based on the extracted abnormal behaviors. The abnormal behavior analysis process is presented in detail in section III.A of the paper. The APT attack detection method based on Network traffic is presented in section III.B of this paper. Finally, the experimental process of the proposal is performed in section IV of the paper.