• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권9호
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• pp.3663-3679
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• 2020

Scheduling plays a dynamic role in cloud computing in generating as well as in efficient distribution of the resources of each task. The principle goal of scheduling is to limit resource starvation and to guarantee fairness among the parties using the resources. The demand for resources fluctuates dynamically hence the prearranging of resources is a challenging task. Many task-scheduling approaches have been used in the cloud-computing environment. Security in cloud computing environment is one of the core issue in distributed computing. We have designed a deep learning-based security model for scheduling tasks in cloud computing and it has been implemented using CloudSim 3.0 simulator written in Java and verification of the results from different perspectives, such as response time with and without security factors, makespan, cost, CPU utilization, I/O utilization, Memory utilization, and execution time is compared with Round Robin (RR) and Waited Round Robin (WRR) algorithms.

• 한국멀티미디어학회논문지
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• 제23권11호
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• pp.1396-1405
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• 2020

Cyber threats such as forced personal information collection and distribution of malicious codes using malicious URLs continue to occur. In order to cope with such cyber threats, a security technologies that quickly detects malicious URLs and prevents damage are required. In a web environment, malicious URLs have various forms and are created and deleted from time to time, so there is a limit to the response as a method of detecting or filtering by signature matching. Recently, researches on detecting and predicting malicious URLs using machine learning techniques have been actively conducted. Existing studies have proposed various features and machine learning algorithms for predicting malicious URLs, but most of them are only suggesting specialized algorithms by supplementing features and preprocessing, so it is difficult to sufficiently reflect the strengths of various machine learning algorithms. In this paper, a system for predicting malicious URLs using multiple machine learning algorithms was proposed, and an experiment was performed to combine the prediction results of multiple machine learning models to increase the accuracy of predicting malicious URLs. Through experiments, it was proved that the combination of multiple models is useful in improving the prediction performance compared to a single model.

• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.342-347
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• 2022

On Cloud, the important data of the user that is protected on remote servers can be accessed via internet. Due to rapid shift in technology nowadays, there is a swift increase in the confidential and pivotal data. This comes up with the requirement of data security of the user's data. Data is of different type and each need discrete degree of conservation. The idea of data security data science permits building the computing procedure more applicable and bright as compared to conventional ones in the estate of data security. Our focus with this paper is to enhance the safety of data on the cloud and also to obliterate the problems associated with the data security. In our suggested plan, some basic solutions of security like cryptographic techniques and authentication are allotted in cloud computing world. This paper put your heads together about how machine learning techniques is used in data security in both offensive and defensive ventures, including analysis on cyber-attacks focused at machine learning techniques. The machine learning technique is based on the Supervised, UnSupervised, Semi-Supervised and Reinforcement Learning. Although numerous research has been done on this topic but in reference with the future scope a lot more investigation is required to be carried out in this field to determine how the data can be secured more firmly on cloud in respect with the Machine Learning Techniques and cryptographic methods.

• 융합보안논문지
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• 제5권2호
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• pp.37-42
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• 2005

정보통신기술의 발전과 지식정보 사회의 등장은 교육 및 훈련분야에도 거대한 변화를 가져왔다. 특히, 유비쿼터스 시대가 다가옴에 따라 e-Learning 또한 u-Learning으로 진화하려 하고 있다. 이는 지금까지와는 또 다른 형태로 교수-학습자 환경이 변화함을 말한다. 본 논문에서는 교육환경의 발전에 따른 다양한 학습 콘텐츠의 관리 방법을 제안, 구현하고 운영플랫폼 분석을 통하여 콘텐츠의 활용을 극대화 할 수 있는 LCMS를 제안하였다.

• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.83-92
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• 2023

The majority of product users rely on the reviews that are posted on the appropriate website. Both users and the product's manufacturer could benefit from these reviews. Daily, thousands of reviews are submitted; how is it possible to read them all? Sentiment analysis has become a critical field of research as posting reviews become more and more common. Machine learning techniques that are supervised, unsupervised, and semi-supervised have worked very hard to harvest this data. The complicated and technological area of feature engineering falls within machine learning. Using deep learning, this tedious process may be completed automatically. Numerous studies have been conducted on deep learning models like LSTM, CNN, RNN, and GRU. Each model has employed a certain type of data, such as CNN for pictures and LSTM for language translation, etc. According to experimental results utilizing a publicly accessible dataset with reviews for all of the models, both positive and negative, and CNN, the best model for the dataset was identified in comparison to the other models, with an accuracy rate of 81%.

• 정보보호학회논문지
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• 제28권5호
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• pp.1161-1167
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• 2018

기하급수적으로 증가하고 있는 변종 악성코드에 대응하기 위해 악성코드 분류 연구가 다양화되고 있다. 최근 연구에서는 기존 악성코드 분석 기술 (정적/동적)의 개별 사용 한계를 파악하고, 각 방식을 혼합한 하이브리드 분석으로 전환하는 추세이다. 나아가, 분류가 어려운 변종 악성코드를 더욱 정확하게 식별하기 위해 기계학습을 적용하기에 이르렀다. 하지만, 각 방식을 모두 활용했을 때 발생하는 정확성, 확장성 트레이드오프 문제는 여전히 해결되지 못했으며, 학계에서 중요한 연구 주제이다. 이에 따라, 본 연구에서는 기존 악성코드 분류 연구들의 문제점을 보완하기 위해 새로운 악성코드 분류 시스템을 연구 및 개발한다.

• 정보보호학회논문지
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• 제27권4호
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• pp.763-773
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• 2017

최근 몇 년 동안 지속적으로 개인정보유출, 기술유출 사고가 빈번하게 발생하고 있다. 조사에 따르면 이러한 유출 사고의 주체로 가장 많은 부분을 차지하고 있는 것이 조직 내부에 있는 '내부자'로, 내부자에 의한 기술유출은 조직에 막대한 피해를 주기 때문에 점점 더 중요한 문제로 여겨지고 있다. 본 논문에서는 내부자위협을 방지하기 위해 기계학습을 이용하여 직원들의 일반적인 정상행위를 학습하고, 이에 벗어나는 비정상 행위를 탐지하기 방법에 대한 연구를 하고자 한다. Neural Network 모델 중 시계열 데이터의 학습에 적합한 Recurrent Neural Network로 구성한 Autoencoder를 구현하여 비정상 행위를 탐지하는 방법에 대한 실험을 진행하였고, 이 방법에 대한 유효성을 검증하였다.

• 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.

• Journal of Information Processing Systems
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• 제18권1호
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• pp.146-158
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• 2022

With the success of the digital economy and the rapid development of its technology, network security has received increasing attention. Intrusion detection technology has always been a focus and hotspot of research. A hybrid model that combines particle swarm optimization (PSO) and kernel extreme learning machine (KELM) is presented in this work. Continuous-valued PSO and binary PSO (BPSO) are adopted together to determine the parameter combination and the feature subset. A fitness function based on the detection rate and the number of selected features is proposed. The results show that the method can simultaneously determine the parameter values and select features. Furthermore, competitive or better accuracy can be obtained using approximately one quarter of the raw input features. Experiments proved that our method is slightly better than the genetic algorithm-based KELM model.

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.155-162
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• 2022

Over the last 10 years, there has been rapid growth in the use of Machine Learning (ML) techniques to automate the process of intrusion threat detection at a scale never imagined before. This has prompted researchers, software engineers, and network specialists to rethink the applications of machine ML techniques particularly in the area of cybersecurity. As a result there exists numerous research documentations on the use ML techniques to detect and block cyber-attacks. This article is a systematic review involving the identification of published scholarly articles as found on IEEE Explore and Scopus databases. The articles exclusively related to the use of machine learning in Intrusion Detection Systems (IDS). Methods, concepts, results, and conclusions as found in the texts are analyzed. A description on the process taken in the identification of the research articles included: First, an introduction to the topic which is followed by a methodology section. A table is used to list identified research articles in the form of title, authors, methodology, and key findings.