• International Journal of Computer Science & Network Security
• /
• v.23 no.11
• /
• pp.21-31
• /
• 2023

In this paper, we focused on the problem of evaluating multi-class classification accuracy and simulation of multiple classifier performance metrics. Multi-class classifiers for sentiment analysis involved many challenges, whereas previous research narrowed to the binary classification model since it provides higher accuracy when dealing with text data. Thus, we take inspiration from the non-linear Support Vector Machine to modify the algorithm by embedding dynamic hyperplanes representing multiple class labels. Then we analyzed the performance of multi-class classifiers using macro-accuracy, micro-accuracy and several other metrics to justify the significance of our algorithm enhancement. Furthermore, we hybridized Enhanced Convolution Neural Network (ECNN) with Dynamic Support Vector Machine (DSVM) to demonstrate the effectiveness and efficiency of the classifier towards multi-class text data. We performed experiments on three hybrid classifiers, which are ECNN with Binary SVM (ECNN-BSVM), and ECNN with linear Multi-Class SVM (ECNN-MCSVM) and our proposed algorithm (ECNNDSVM). Comparative experiments of hybrid algorithms yielded 85.12 % for single metric accuracy; 86.95 % for multiple metrics on average. As for our modified algorithm of the ECNN-DSVM classifier, we reached 98.29 % micro-accuracy results with an f-score value of 98 % at most. For the future direction of this research, we are aiming for hyperplane optimization analysis.

• International Journal of Computer Science & Network Security
• /
• v.24 no.5
• /
• pp.119-128
• /
• 2024

The moveable ad hoc networks are untrustworthy and susceptible to any intrusion because of their wireless interaction approach. Therefore the information from these networks can be stolen very easily just by introducing the attacker nodes in the system. The straight route extent is calculated with the help of hop count metric. For this purpose, routing protocols are planned. From a number of attacks, the wormhole attack is considered to be the hazardous one. This intrusion is commenced with the help of couple attacker nodes. These nodes make a channel by placing some sensor nodes between transmitter and receiver. The accessible system regards the wormhole intrusions in the absence of intermediary sensor nodes amid target. This mechanism is significant for the areas where the route distance amid transmitter and receiver is two hops merely. This mechanism is not suitable for those scenarios where multi hops are presented amid transmitter and receiver. In the projected study, a new technique is implemented for the recognition and separation of attacker sensor nodes from the network. The wormhole intrusions are triggered with the help of these attacker nodes in the network. The projected scheme is utilized in NS2 and it is depicted by the reproduction outcomes that the projected scheme shows better performance in comparison with existing approaches.

• Journal of Korea Multimedia Society
• /
• v.21 no.5
• /
• pp.617-625
• /
• 2018

Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions, and to name but a few. One way to build anti-phishing detection mechanism is to construct classification algorithm based on machine learning techniques. The objective of this paper is to compare different classifier ensemble approaches, i.e. random forest, rotation forest, gradient boosted machine, and extreme gradient boosting against single classifiers, i.e. decision tree, classification and regression tree, and credal decision tree in the case of website phishing. Area under ROC curve (AUC) is employed as a performance metric, whilst statistical tests are used as baseline indicator of significance evaluation among classifiers. The paper contributes the existing literature on making a benchmark of classifier ensembles for web phishing detection.

• Proceedings of the KIEE Conference
• /
• 2004.11c
• /
• pp.187-189
• /
• 2004

This paper presents the system widely used for extraction of human body recognition system in the field of bio-metric identification. The Human body recognition system is used in many fields. This biological is appled to the human recognition in banking and the access control with security. The important algorithm of the identification software usese hand lines and hand shape geometry. We used the simple algorithm and recognizing the person by their hand image from the input camera. The geometrical characteristics in hand shape such as length of finger to whole hand length thickness of finger to length, etc are used.

• Journal of the Korea Society of Computer and Information
• /
• v.11 no.1 s.39
• /
• pp.19-25
• /
• 2006

Iris recognition is a bio metric personal identification which uses iris pattern of the pupil, and it is recognized as one of the best technology in personal identification and information security field. Before iris recognition, it is very important to search center of pupil. In recent years, there was developed many searching algorithms of center of pupil, but all most method are too many processing time. In this paper, we proposed a new method for searching center of pupil. This method is greatly reduced processing time about 30% compared with other algorithm using Hough transformation.

• Journal of Multimedia Information System
• /
• v.5 no.2
• /
• pp.99-104
• /
• 2018

Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions, and to name but a few. One way to build anti-phishing detection mechanism is to construct classification algorithm based on machine learning techniques. The objective of this paper is to compare different classifier ensemble approaches, i.e. random forest, rotation forest, gradient boosted machine, and extreme gradient boosting against single classifiers, i.e. decision tree, classification and regression tree, and credal decision tree in the case of website phishing. Area under ROC curve (AUC) is employed as a performance metric, whilst statistical tests are used as baseline indicator of significance evaluation among classifiers. The paper contributes the existing literature on making a benchmark of classifier ensembles for web phishing detection.

• Journal of Information Processing Systems
• /
• v.13 no.5
• /
• pp.1203-1212
• /
• 2017

Intrusion detection systems (IDSs) are crucial in this overwhelming increase of attacks on the computing infrastructure. It intelligently detects malicious and predicts future attack patterns based on the classification analysis using machine learning and data mining techniques. This paper is devoted to thoroughly evaluate classifier ensembles for IDSs in IEEE 802.11 wireless network. Two ensemble techniques, i.e. voting and stacking are employed to combine the three base classifiers, i.e. decision tree (DT), random forest (RF), and support vector machine (SVM). We use area under ROC curve (AUC) value as a performance metric. Finally, we conduct two statistical significance tests to evaluate the performance differences among classifiers.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2012.11a
• /
• pp.777-779
• /
• 2012

인터넷의 확산으로 인해 전송, 서비스 및 보안과 관련된 많은 장비들이 개발되고 출시되고 있다. 서비스 및 보안과 관련된 장비들은 새로운 개발과 급속한 도입으로 인해 적절한 성능측정방법의 부재로 인해 많은 혼란과 문제들을 가져오기도 했었다. 이에 장비를 도입하는 기관들은 전문 시험기관의 시험과 평가를 거쳐 발행된 성적서나 보고서를 참고하거나 직접 벤치마킹테스트를 거쳐 도입을 결정하고 있다. 본 논문에서는 IP 네트워크 장비들의 성능측정을 위한 방법들을 분석하고 표준으로 제정된 시험 방법론을 분석하여 시스템 성능측정을 위한 메트릭을 제시하고자 한다.

• The Journal of Society for e-Business Studies
• /
• v.16 no.2
• /
• pp.129-142
• /
• 2011

Traditionally research in Service Oriented Architecture(SOA) security has focused primarily on exploiting standards and solutions separately. There exists no unified methodology for SOA security to manage risks at the enterprise level. It needs to analyze preliminarily security threats and to manage enterprise risks by identifying vulnerabilities of SOA. In this paper, we propose a metric-based vulnerability assessment method using dynamic properties of services in SOA. The method is to assess vulnerability at the architecture level as well as the service level by measuring run-time dependency between services. The run-time dependency between services is an important characteristic to understand which services are affected by a vulnerable service. All services which directly or indirectly depend on the vulnerable service are exposed to the risk. Thus run-time dependency is a good indicator of vulnerability of SOA.

• KIPS Transactions on Computer and Communication Systems
• /
• v.13 no.1
• /
• pp.31-37
• /
• 2024

Conventional secret sharing techniques often derive shares from randomly generated polynomials or planes, resulting in lengthy and complex shares that are challenging to memorize and/or manage without the aid of a separate computer or specialized device. Modifying existing secret sharing methods to use a predetermined value, such as a memorizable password or bio-metric information, offers a solution. However, this approach raises concerns about security, especially when the predetermined value lacks randomness or has low entropy. In such cases, adversaries may deduce a secret S with just (t - 1) shares by guessing the predetermined value or employing brute force attacks. In this paper, we introduce a share hardening method designed to ensure the security of secret sharing while enabling the use of memorizable passwords or biometric information as predetermined shares.