• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.190-194
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• 2023

By looking the importance of communication, data delivery and access in various sectors including governmental, business and individual for any kind of data, it becomes mandatory to identify faults and flaws during cyber communication. To protect personal, governmental and business data from being misused from numerous advanced attacks, there is the need of cyber security. The information security provides massive protection to both the host machine as well as network. The learning methods are used for analyzing as well as preventing various attacks. Machine learning is one of the branch of Artificial Intelligence that plays a potential learning techniques to detect the cyber-attacks. In the proposed methodology, the Decision Tree (DT) which is also a kind of supervised learning model, is combined with the different cross-validation method to determine the accuracy and the execution time to identify the cyber-attacks from a very recent dataset of different network attack activities of network traffic in the UNSW-NB15 dataset. It is a hybrid method in which different types of attributes including Gini Index and Entropy of DT model has been implemented separately to identify the most accurate procedure to detect intrusion with respect to the execution time. The different DT methodologies including DT using Gini Index, DT using train-split method and DT using information entropy along with their respective subdivision such as using K-Fold validation, using Stratified K-Fold validation are implemented.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.179-191
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• 2024

With the advancement of modern technology, cyber-attacks are always rising. Specialized defense systems are needed to protect organizations against these threats. Malicious behavior in the network is discovered using security tools like intrusion detection systems (IDS), firewall, antimalware systems, security information and event management (SIEM). It aids in defending businesses from attacks. Delivering advance threat feeds for precise attack detection in intrusion detection systems is the role of cyber-threat intelligence (CTI) in the study is being presented. In this proposed work CTI feeds are utilized in the detection of assaults accurately in intrusion detection system. The ultimate objective is to identify the attacker behind the attack. Several data sets had been analyzed for attack detection. With the proposed study the ability to identify network attacks has improved by using machine learning algorithms. The proposed model provides 98% accuracy, 97% precision, and 96% recall respectively.

• 정보보호학회논문지
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• 제14권1호
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• pp.35-45
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• 2004

폭발적으로 증가하는 인터넷 환경에서 정보보호는 가장 중요한 고려사항 중의 하나이다. 현재 이에 대한 대응방안으로 IDS, 방화벽, VPN 등 여러 보안 솔루션들이 사용되고 있지만 TCP/IP를 근간으로 하는 인터넷 환경은 기본적으로 프로토콜 자체의 취약성을 가지고 있다. 그 중에서도, TCP/IP 헤더 중 ICMP Payload. Identification(ID), Sequence Number(SEQ), Acknowledge(ACK). Timestamp의 필드 내용을 조작함으로써 특정 정보를 전송할 수 있는 은닉채널이 가능하다고 이미 알려져 있다. 특히 본 논문에서는 TCP/IP 헤더의 여러 필드들 중에서도 IP 헤더의 ID 필드, TCP 헤더의 SEQ 필드를 이용한 은닉채널 탐지에 초점을 맞추었으며, 이러한 은닉채널의 탐지를 위하여, 패턴분류 문제 있어서 우수한 성능을 보이는 것으로 알려져 있는 Support Vector Machine(SVM)을 사용하였다. 본 논문의 실험결과에서는 제안된 탐지방안이 정상 TCP/IP 트래픽으로부터 은닉채널이 포함된 TCP/IP 패킷을 구분할 수 있음을 보여주었다.

• 정보보호학회논문지
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• 제30권2호
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• pp.169-177
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• 2020

악성코드 분석은 컴퓨터 보안의 중요한 관심사 중 하나로 분석 기법의 진보는 컴퓨터 보안의 중요 사항이 되었다. 기존에는 악성코드를 탐지할 때 Signature-based 방식을 사용하였으나 패킹된 악성코드의 비율이 높아지면서 기존 Signature-based 방식으로는 탐지에 어려움이 많아 졌다. 이에, 본 논문에서는 머신러닝을 사용하여 패킹된 프로그램의 패커를 식별하는 방법을 제안한다. 제안한 방법은 패킹된 프로그램을 파싱하여 패커를 특정 지을 수 있는 특정 PE 정보를 추출하고 머신러닝 모델 중 Adaptive Boosting 알고리즘을 사용하여 패커를 식별한다. 제안한 방법의 정확도를 확인하기 위해 12가지 종류의 패커로 패킹된 프로그램 391개를 수집하여 실험하였으며, 약 99.2%의 정확도로 패커를 식별하는 것을 알 수 있었다. 또한, Signature-based PE 식별 도구인 PEiD와 기존 머신러닝을 사용한 방법으로 식별한 결과를 제시하였으며, 본 논문에서 제안한 방법이 기존의 방법보다 패커를 식별하는데 정확도와 속도면에서 더 뛰어난 성능을 발휘하는 것을 알 수 있다.

• 정보보호학회논문지
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• 제18권6A호
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• pp.115-127
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• 2008

Native API(Application Programming Interfaces)는 관리자 권한에서 수행되는 system call의 일종으로 관리자 권한을 획득하여 공격하는 다양한 종류의 악성코드를 탐지하는데 사용된다. 이에 따라 Native API의 특징을 기반으로한 탐지방법들이 제안되고 있으며 다수의 탐지방법이 교사학습(supervised learning) 방법의 기계학습(machine learning)을 사용하고 있다. 하지만 Anti-Virus 업체의 분류기준은 Native API의 특징점을 반영하지 않았기 때문에 교사학습을 이용한 탐지에 적합한 학습 집합을 제공하지 못한다. 따라서 Native API를 이용한 탐지에 적합한 분류기준에 대한 연구가 필요하다. 본 논문에서는 정량적으로 악성코드를 분류하기 위해 Native API를 기준으로 악성코드를 퍼지 군집화하여 재그룹화하는 방법을 제시한다. 제시하는 재그룹화 방법의 적합성은 기계학습을 이용한 탐지성능의 차이를 기존 분류방법을 결과와 비교하여 검증한다.

• 한국소프트웨어감정평가학회 논문지
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• 제15권2호
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• pp.121-128
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• 2019

컴퓨팅 시스템들은 사이버공격에 대한 다양한 취약점을 가지고 있다. 특히 정보화 사회에서 지능화된 다양한 사이버공격은 사회적으로 심각한 문제와 경제적 손실을 초래한다. 전통적인 침입탐지시스템은 오용침입탐지(misuse)기반의 기술로 사이버공격을 정확하게 탐지하기 위해서는 지속적인 새로운 공격 패턴 갱신과 수많은 보안 장비에서 생성되는 방대한 양의 데이터에 대한 실시간 분석을 해야만 한다. 하지만 전통적인 보안시스템은 실시간으로 탐지 및 분석을 통한 대응을 할 수 없기 때문에 침해 사고의 인지시점이 지체되어 많은 피해를 야기할 수도 있다. 따라서 머신 러닝과 빅데이터 분석 모델 기반으로 끊임없이 증가하는 사이버 보안 위협을 신속하게 탐지, 분석을 통한 대응과 예측할 수 있는 새로운 보안 시스템이 필요하다. 본 논문에서는 머신 러닝과 빅데이터 기술을 활용한 IDS 구축 방안을 제시한다.

• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.103-114
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• 2022

The Internet of Things (IoT) is one of the fastest technologies that are used in various applications and fields. The concept of IoT will not only be limited to the fields of scientific and technical life but will also gradually spread to become an essential part of our daily life and routine. Before, IoT was a complex term unknown to many, but soon it will become something common. IoT is a natural and indispensable routine in which smart devices and sensors are connected wirelessly or wired over the Internet to exchange and process data. With all the benefits and advantages offered by the IoT, it does not face many security and privacy challenges because the current traditional security protocols are not suitable for IoT technologies. In this paper, we presented a comprehensive survey of the latest studies from 2018 to 2021 related to the security of the IoT and the use of machine learning (ML) and deep learning and their applications in addressing security and privacy in the IoT. A description was initially presented, followed by a comprehensive overview of the IoT and its applications and the basic important safety requirements of confidentiality, integrity, and availability and its application in the IoT. Then we reviewed the attacks and challenges facing the IoT. We also focused on ML and its applications in addressing the security problem on the IoT.

• International Journal of Computer Science & Network Security
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• 제21권1호
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• pp.220-225
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• 2021

Human population growth rate is an important parameter for real-world planning. Common approaches rely upon fixed parameters like human population, mortality rate, fertility rate, which is collected historically to determine the region's population growth rate. Literature does not provide a solution for areas with no historical knowledge. In such areas, machine learning can solve the problem, but a multitude of machine learning algorithm makes it difficult to determine the best approach. Further, the missing feature is a common real-world problem. Thus, it is essential to compare and select the machine learning techniques which provide the best and most robust in the presence of missing features. This study compares 17 machine learning techniques (base learners and ensemble learners) performance in predicting the human population growth rate of the country. Among the 17 machine learning techniques, random forest outperformed all the other techniques both in predictive performance and robustness towards missing features. Thus, the study successfully demonstrates and compares machine learning techniques to predict the human population growth rate in settings where historical data and feature information is not available. Further, the study provides the best machine learning algorithm for performing population growth rate prediction.

• International Journal of Computer Science & Network Security
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• 제21권1호
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• pp.1-5
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• 2021

Cyberbullying is a problem that is faced in many cultures. Due to their popularity and interactive nature, social media platforms have also been affected by cyberbullying. Social media users from Arab countries have also reported being a target of cyberbullying. Machine learning techniques have been a prominent approach used by scientists to detect and battle this phenomenon. In this paper, we compare different machine learning algorithms for their performance in cyberbullying detection based on a labeled dataset of Arabic YouTube comments. Three machine learning models are considered, namely: Multinomial Naïve Bayes (MNB), Complement Naïve Bayes (CNB), and Linear Regression (LR). In addition, we experiment with two feature extraction methods, namely: Count Vectorizer and Tfidf Vectorizer. Our results show that, using count vectroizer feature extraction, the Logistic Regression model can outperform both Multinomial and Complement Naïve Bayes models. However, when using Tfidf vectorizer feature extraction, Complement Naive Bayes model can outperform the other two models.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.258-270
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• 2022

Cancer has become a common disease for the past two decades throughout the globe and there is significant increase of cancer among women. Breast cancer and ovarian cancers are more prevalent among women. Majority of the patients approach the physicians only during their final stage of the disease. Early diagnosis of cancer remains a great challenge for the researchers. Although several drugs are being synthesized very often, their multi-benefits are less investigated. With millions of drugs synthesized and their data are accessible through open repositories. Drug repurposing can be done using machine learning techniques. We propose a feature selection technique in this paper, which is novel that generates multiple populations for the grey wolf algorithm and classifies breast cancer drugs efficiently. Leukemia drug dataset is also investigated and Multilayer perceptron achieved 96% prediction accuracy. Three supervised machine learning algorithms namely Random Forest classifier, Multilayer Perceptron and Support Vector Machine models were applied and Multilayer perceptron had higher accuracy rate of 97.7% for breast cancer drug classification.