• Title/Summary/Keyword: Security Techniques

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Incorporating RSA with a New Symmetric-Key Encryption Algorithm to Produce a Hybrid Encryption System

  • Prakash Kuppuswamy;Saeed QY Al Khalidi;Nithya Rekha Sivakumar
    • International Journal of Computer Science & Network Security
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    • v.24 no.1
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    • pp.196-204
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    • 2024
  • The security of data and information using encryption algorithms is becoming increasingly important in today's world of digital data transmission over unsecured wired and wireless communication channels. Hybrid encryption techniques combine both symmetric and asymmetric encryption methods and provide more security than public or private key encryption models. Currently, there are many techniques on the market that use a combination of cryptographic algorithms and claim to provide higher data security. Many hybrid algorithms have failed to satisfy customers in securing data and cannot prevent all types of security threats. To improve the security of digital data, it is essential to develop novel and resilient security systems as it is inevitable in the digital era. The proposed hybrid algorithm is a combination of the well-known RSA algorithm and a simple symmetric key (SSK) algorithm. The aim of this study is to develop a better encryption method using RSA and a newly proposed symmetric SSK algorithm. We believe that the proposed hybrid cryptographic algorithm provides more security and privacy.

Study on Trends of the Future Internet Security : FIA Work (미래 인터넷 보안 연구 동향 분석 : FIA를 중심으로)

  • Jun, Eun-A;Lee, Do-Geon;Lee, Sang-Woo;Seo, Dong-Il;Kim, Jeom-Goo
    • Convergence Security Journal
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    • v.12 no.1
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    • pp.79-87
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    • 2012
  • Future Internet has been designing and developing in the world because of overcoming limits of current Internet and accepting new requirements. Therefore it is totally different from architectures of current Internet and it is based on Clean-Slate. Future Internet already has been studying with enormous investment by advanced countries such as USA, EU etc. Technical characteristics of Future Internet can be categorized into Infra techniques, Architectures and Service techniques. Especially, our country is in a superior position in Infra techniques and Service techniques. We can have competitiveness to develop trust communication in Future Internet because we have advantages of various Services such as mobile communication, Machine to Machine and Sensor Networks. This paper aims to analysis reference model of trust communication in Future Internet. To achieve this, we studied analysis of security techniques in four Future Internet researches of NSF.

Comparative Analysis of Intrusion Detection Attack Based on Machine Learning Classifiers

  • Surafel Mehari;Anuja Kumar Acharya
    • International Journal of Computer Science & Network Security
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    • v.24 no.10
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    • pp.115-124
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    • 2024
  • In current day information transmitted from one place to another by using network communication technology. Due to such transmission of information, networking system required a high security environment. The main strategy to secure this environment is to correctly identify the packet and detect if the packet contain a malicious and any illegal activity happened in network environments. To accomplish this we use intrusion detection system (IDS). Intrusion detection is a security technology that design detects and automatically alert or notify to a responsible person. However, creating an efficient Intrusion Detection System face a number of challenges. These challenges are false detection and the data contain high number of features. Currently many researchers use machine learning techniques to overcome the limitation of intrusion detection and increase the efficiency of intrusion detection for correctly identify the packet either the packet is normal or malicious. Many machine-learning techniques use in intrusion detection. However, the question is which machine learning classifiers has been potentially to address intrusion detection issue in network security environment. Choosing the appropriate machine learning techniques required to improve the accuracy of intrusion detection system. In this work, three machine learning classifier are analyzed. Support vector Machine, Naïve Bayes Classifier and K-Nearest Neighbor classifiers. These algorithms tested using NSL KDD dataset by using the combination of Chi square and Extra Tree feature selection method and Python used to implement, analyze and evaluate the classifiers. Experimental result show that K-Nearest Neighbor classifiers outperform the method in categorizing the packet either is normal or malicious.

A system for detecting document leakage by insiders through continuous user authentication by using document reading behavior (문서 읽기 행위를 이용한 연속적 사용자 인증 기반의 내부자 문서유출 탐지기술 연구)

  • Cho, Sungyoung;Kim, Minsu;Won, Jongil;Kwon, SangEun;Lim, Chaeho;Kang, Brent ByungHoon;Kim, Sehun
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.23 no.2
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    • pp.181-192
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    • 2013
  • There have been various techniques to detect and control document leakage; however, most techniques concentrate on document leakage by outsiders. There are rare techniques to detect and monitor document leakage by insiders. In this study, we observe user's document reading behavior to detect and control document leakage by insiders. We make each user's document reading patterns from attributes gathered by a logger program running on Microsoft Word, and then we apply the proposed system to help determine whether a current user who is reading a document matches the true user. We expect that our system based on document reading behavior can effectively prevent document leakage.

A Correspondence Training Scenario against Bypassing Information Protection System Attacks (네트워크 정보보호시스템 우회 공격에 대한 대응훈련 시나리오)

  • Hong, Jeong Soo;Yang, Dong Min;Lee, Bong Hwan
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.22 no.5
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    • pp.818-828
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    • 2018
  • Nowadays, various security systems are developed and used for protecting information on the network. Although security solutions can prevent some of the security risks, they provide high performance only if used appropriately in accordance with their purposes and functionality. Security solutions commonly used in information protection systems include firewalls, IDS, and IPS. However, despite various information protection systems are introduced, there are always techniques that can threaten the security systems through bypassing them. The purpose of this paper is to develop effective training techniques for responding to the bypass attack techniques in the information security systems and to develop effective techniques that can be applied to the training. In order to implement the test bed we have used GNS3 network simulator, and deployed it on top of virtual operating system using VirtualBox. The proposed correspondence training scenario against bypassing information protection system attacks could be very effectively used to counteract the real attacks.

Machine Learning-Based Transactions Anomaly Prediction for Enhanced IoT Blockchain Network Security and Performance

  • Nor Fadzilah Abdullah;Ammar Riadh Kairaldeen;Asma Abu-Samah;Rosdiadee Nordin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.18 no.7
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    • pp.1986-2009
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    • 2024
  • The integration of blockchain technology with the rapid growth of Internet of Things (IoT) devices has enabled secure and decentralised data exchange. However, security vulnerabilities and performance limitations remain significant challenges in IoT blockchain networks. This work proposes a novel approach that combines transaction representation and machine learning techniques to address these challenges. Various clustering techniques, including k-means, DBSCAN, Gaussian Mixture Models (GMM), and Hierarchical clustering, were employed to effectively group unlabelled transaction data based on their intrinsic characteristics. Anomaly transaction prediction models based on classifiers were then developed using the labelled data. Performance metrics such as accuracy, precision, recall, and F1-measure were used to identify the minority class representing specious transactions or security threats. The classifiers were also evaluated on their performance using balanced and unbalanced data. Compared to unbalanced data, balanced data resulted in an overall average improvement of approximately 15.85% in accuracy, 88.76% in precision, 60% in recall, and 74.36% in F1-score. This demonstrates the effectiveness of each classifier as a robust classifier with consistently better predictive performance across various evaluation metrics. Moreover, the k-means and GMM clustering techniques outperformed other techniques in identifying security threats, underscoring the importance of appropriate feature selection and clustering methods. The findings have practical implications for reinforcing security and efficiency in real-world IoT blockchain networks, paving the way for future investigations and advancements.

A Practical Security Risk Analysis Process and Tool for Information System

  • Chung, Yoon-Jung;Kim, In-Jung;Lee, Do-Hoon
    • Journal of Information Processing Systems
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    • v.2 no.2
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    • pp.95-100
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    • 2006
  • While conventional business administration-based information technology management methods are applied to the risk analysis of information systems, no security risk analysis techniques have been used in relation to information protection. In particular, given the rapid diffusion of information systems and the demand for information protection, it is vital to develop security risk analysis techniques. Therefore, this paper will suggest an ideal risk analysis process for information systems. To prove the usefulness of this security risk analysis process, this paper will show the results of managed, physical and technical security risk analysis that are derived from investigating and analyzing the conventional information protection items of an information system.

A Risk Management Model for Efficient Domestic Information Technology Security (효율적 국내 정보기술 보안을 위한 위험관리 모형)

  • Ahn, Choon-soo;Cho, Sung-Ku
    • Journal of Korean Institute of Industrial Engineers
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    • v.28 no.1
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    • pp.44-56
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    • 2002
  • For the risk analysis and risk assessment techniques to be effectively applied to the field of information technology (IT) security, it is necessary that the required activities and specific techniques to be applied and their order of applications are to be determined through a proper risk management model. If the adopted risk management model does not match with the characteristics of host organization, an inefficient management of security would be resulted. In this paper, a risk management model which can be well adapted to Korean domestic IT environments is proposed for an efficient security management of IT. The structure and flow of the existing IT-related risk management models are compared and analysed, and their common and/or strong characteristics are extracted and incorporated in the proposed model in the light of typical threat types observed in Korean IT environments.

Improved Multi-layer Authentication Scheme by Merging One-time Password with Voice Biometric Factor

  • ALRUWAILI, Amal;Hendaoui, Saloua
    • International Journal of Computer Science & Network Security
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    • v.21 no.9
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    • pp.346-353
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    • 2021
  • In this proposal, we aim to enhance the security of systems accounts by improving the authentication techniques. We mainly intend to enhance the accuracy of the one-time passwords via including voice biometric and recognition techniques. The recognition will be performed on the server to avoid redirecting voice signatures by hackers. Further, to enhance the privacy of data and to ensure that the active user is legitimate, we propose to periodically update the activated sessions using a user-selected biometric factor. Finally, we recommend adding a pre-transaction re-authentication which will guarantee enhanced security for sensitive operations. The main novelty of this proposal is the use of the voice factor in the verification of the one-time password and the various levels of authentications for a full-security guarantee. The improvement provided by this proposal is mainly designed for sensitive applications. From conducted simulations, findings prove the efficiency of the proposed scheme in reducing the probability of hacking users' sessions.

Securing SCADA Systems: A Comprehensive Machine Learning Approach for Detecting Reconnaissance Attacks

  • Ezaz Aldahasi;Talal Alkharobi
    • International Journal of Computer Science & Network Security
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    • v.23 no.12
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    • pp.1-12
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    • 2023
  • Ensuring the security of Supervisory Control and Data Acquisition (SCADA) and Industrial Control Systems (ICS) is paramount to safeguarding the reliability and safety of critical infrastructure. This paper addresses the significant threat posed by reconnaissance attacks on SCADA/ICS networks and presents an innovative methodology for enhancing their protection. The proposed approach strategically employs imbalance dataset handling techniques, ensemble methods, and feature engineering to enhance the resilience of SCADA/ICS systems. Experimentation and analysis demonstrate the compelling efficacy of our strategy, as evidenced by excellent model performance characterized by good precision, recall, and a commendably low false negative (FN). The practical utility of our approach is underscored through the evaluation of real-world SCADA/ICS datasets, showcasing superior performance compared to existing methods in a comparative analysis. Moreover, the integration of feature augmentation is revealed to significantly enhance detection capabilities. This research contributes to advancing the security posture of SCADA/ICS environments, addressing a critical imperative in the face of evolving cyber threats.