• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.56-62
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• 2021

The surge in generic attacks execution against cipher text on the computer network has led to the continuous advancement of the mechanisms to protect information integrity and confidentiality. The implementation of explicit decision tree machine learning algorithm is reported to accurately classifier generic attacks better than some multi-classification algorithms as the multi-classification method suffers from detection oversight. However, there is a need to improve the accuracy and reduce the false alarm rate. Therefore, this study aims to improve generic attack classification by implementing two hybridized decision tree algorithms namely Naïve Bayes Decision tree (NBTree) and Logistic Model tree (LMT). The proposed hybridized methods were developed using the 10-fold cross-validation technique to avoid overfitting. The generic attack detector produced a 99.8% accuracy, an FPR score of 0.002 and an MCC score of 0.995. The performances of the proposed methods were better than the existing decision tree method. Similarly, the proposed method outperformed multi-classification methods for detecting generic attacks. Hence, it is recommended to implement hybridized decision tree method for detecting generic attacks on a computer network.

• Journal of the Korea Computer Industry Society
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• v.5 no.4
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• pp.427-434
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• 2004

Attacks such as hacking, a virus intimidating a system and a network are increasing recently. However, the existing system security or network management system(NMS) cannot be safe on various threats. Therefore, Firewall, IDS, VPN, LAS(Log Analysis System) establishes security system and has defended a system and a network against a threat. But mutual linkage between security systems was short and cannot prepare an effective correspondence system, and inefficiency was indicated with duplication of security. Therefore, an active security and an Enterprise Security Management came to need. An effective security network was established recently by Enterprise Security Management, Intrusion Tracking, Intrustion Induction. But an internetworking is hard for an enterprise security systems, and a correspondence method cannot be systematic, and it is responded later. Therefore, we proposes the active enterprise security management module that can manage a network safely in this paper.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.129-134
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• 2024

Object Detection is an emerging technology in the field of Computer Vision and Image Processing that deals with detecting objects of a particular class in digital images. It has considered being one of the complicated and challenging tasks in computer vision. Earlier several machine learning-based approaches like SIFT (Scale-invariant feature transform) and HOG (Histogram of oriented gradients) are widely used to classify objects in an image. These approaches use the Support vector machine for classification. The biggest challenges with these approaches are that they are computationally intensive for use in real-time applications, and these methods do not work well with massive datasets. To overcome these challenges, we implemented a Deep Learning based approach Convolutional Neural Network (CNN) in this paper. The Proposed approach provides accurate results in detecting objects in an image by the area of object highlighted in a Bounding Box along with its accuracy.

• Journal of Advanced Navigation Technology
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• v.11 no.1
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• pp.79-86
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• 2007

In recent, one of the interesting research areas is about quality of network system. Therefore many research centers including ISO are preparing the measuring and evaluating method for network quality. This study will represent an evaluating model for network security based on checklist. In addition, we propose an measuring and evaluating method for network performance. The purpose of two studies is to present the evaluating procedure and method for measuring security of network on set workwill be identified and a measuring method and procedure will be proposed.

• Journal of the Korea Society of Computer and Information
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• v.7 no.4
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• pp.141-149
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• 2002

Recently, a number of network systems are developed rapidly and network architectures are more complex than before, and a policy-based network management should be used in network system. Especially, a new paradigm that policy-based network management can be applied for the network security is raised. A security policy server in the management layer can generate new policy, delete. update the existing policy and decide the policy when security policy is requested. The security server needs to analyze and manage the alert message received from server Policy enforcement system in the enforcement layer for the available information. In this paper, we implement an alert analyzer that analyze the stored alert data for making of security policy efficiently in framework of the policy-based network security management. We also propose a data mining system for the analysis of alert data The implemented mining system supports alert analyzer and the high level analyzer efficiently for the security.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.204-214
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• 2022

Speedy development has been witnessed in communication technologies and the adoption of the Internet across the world. Information dissemination is the primary goal of these technologies. One of the rapidly developing nations in the Middle East is Saudi Arabia, where the use of communication technologies, including mobile and Internet, has drastically risen in recent times. These advancements are relatively new to the region when contrasted to developed nations. Thus, offenses arising from the adoption of these technologies may be new to Saudi Arabians. This study examines cyber security awareness among Saudi Arabian citizens in distinct settings. A comparison is made between the cybersecurity policy guidelines adopted in Saudi Arabia and three other nations. This review will explore distinct essential elements and approaches to mitigating cybercrimes in the United States, Singapore, and India. Following an analysis of the current cybersecurity framework in Saudi Arabia, suggestions for improvement are determined from the overall findings. A key objective is enhancing the nationwide focus on efficient safety and security systems. While the participants display a clear knowledge of IT, the surveyed literature shows limited awareness of the risks related to cyber security practices and the role of government in promoting data safety across the Internet. As the findings indicate, proper frameworks regarding cyber security need to be considered to ensure that associated threats are mitigated as Saudi Arabia aspires to become an efficient smart nation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.2904-2918
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• 2020

With a huge demand for replicated content on the Internet, a new networking paradigm called information-centric networking (ICN) has been introduced for efficient content dissemination. In ICN, named content is distributed over the network cache and it is accessed by name instead of a location identifier. These aspects allow users to retrieve content from any of the nodes having replicas, and consequently 1) network resources are more efficiently utilized by avoiding redundant transmission and 2) more scalable services are provided by distributing server loads. However, in-network caching in ICN brings about a new type of security issues, called content poisoning attacks, where fabricated content is located in the network cache and interferes with the normal behavior of the system. In this paper, we look into the problems of content poisoning in ICN and discuss security architectures against them. In particular, we reconsider the state-of-the-art schemes from the perspective of feasibility, and propose a practical security architecture.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.157-164
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• 2022

The proposed privacy preserving scheme has identified the drawbacks of existing schemes in Vehicular Networks. This prototype enhances the number of nodes by decreasing the cluster size. This algorithm is integrated with the whale optimization algorithm and Block Chain Technology. A set of results are done through the NS-2 simulator in the direction to check the effectiveness of proposed algorithm. The proposed method shows better results than with the existing techniques in terms of Delay, Drop, Delivery ratio, Overhead, throughout under the denial of attack.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1577-1580
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• 2005

With remarkable growth of using Internet, attempts to try intrusions on network are now increasing. Intrusion Detection System is a security system which detects and copes illegal intrusions. Especially with increasing dispersive attacks through network, concerns for this Distributed Intrusion Detection are also rising. The previous Intrusion Detection System has difficulty in coping cause it detects intrusions only on particular network and only same segment. About same attacks, system lacks capacity of combining information and related data. Also it lacks cooperations against intrusions. Systematic and general security controls can make it possible to detect intrusions and deal with intrusions and predict. This paper considers Distributed Intrusion Detection preventing attacks and suggests the way sending active packets between nodes safely and performing in corresponding active node certainly. This study suggested improved E-IDS system which prevents service attacks and also studied sending messages safely by encoding. Encoding decreases security attacks in active network. Also described effective ways of dealing intrusions when misuses happens thorough case study. Previous network nodes can't deal with hacking and misuses happened in the middle nodes at all, cause it just encodes ends. With above suggested ideas, problems caused by security services can be improved.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.89-100
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• 2021

Smart Grid Network (SGN) is a next generation electrical power network which digitizes the power distribution grid and achieves smart, efficient, safe and secure operations of the electricity. The backbone of the SGN is information communication technology that enables the SGN to get full control of network station monitoring and analysis. In any network where communication is involved security is essential. It has been observed from several recent incidents that an adversary causes an interruption to the operation of the networks which lead to the electricity theft. In order to reduce the number of electricity theft cases, companies need to develop preventive and protective methods to minimize the losses from this issue. In this paper, we have introduced a machine learning based SVM method that detects malicious nodes in a smart grid network. The algorithm collects data (electricity consumption/electric bill) from the nodes and compares it with previously obtained data. Support Vector Machine (SVM) classifies nodes into Normal or malicious nodes giving the statues of 1 for normal nodes and status of -1 for malicious -abnormal-nodes. Once the malicious nodes have been detected, we have done a trust evaluation based on the nodes history and recorded data. In the simulation, we have observed that our detection rate is almost 98% where the false alarm rate is only 2%. Moreover, a Trust value of 50 was achieved. As a future work, countermeasures based on the trust value will be developed to solve the problem remotely.