Informatica (Slovenia)最新文献_第6页

Optimization of Personalized Recommendation Strategy for E-commerce Platform Based on Artificial Intelligence 基于人工智能的电子商务平台个性化推荐策略优化

Informatica (Slovenia) Pub Date : 2023-06-05 DOI: 10.31449/inf.v47i2.3981

F. Yang

引用次数: 0

A Deep Learning-Fuzzy based hybrid ensemble approach for Aspect level sentiment classification 面向方面级情感分类的深度学习-模糊混合集成方法

Informatica (Slovenia) Pub Date : 2023-05-31 DOI: 10.31449/inf.v47i6.4607

Tanu Sharma, Kamaldeep Kaur

引用次数: 0

A Survey of Using Machine Learning in IoT Security and the Challenges Faced by Researchers 在物联网安全中使用机器学习的调查以及研究人员面临的挑战

Informatica (Slovenia) Pub Date : 2023-05-31 DOI: 10.31449/inf.v47i6.4635

Khawlah M Harahsheh, Chung-Hao Chen

引用次数: 0

Secure and Low-Complexity Medical Image Exchange Based on Compressive Sensing and LSB Audio Steganography 基于压缩感知和LSB音频隐写的安全低复杂度医学图像交换

Informatica (Slovenia) Pub Date : 2023-05-31 DOI: 10.31449/inf.v47i6.4628

Wafaa Al-Chaab, Z. Abduljabbar, Enas Wahab Abood, V. O. Nyangaresi, Hussein M. Mohammed, Junchao Ma

引用次数: 3

Towards an efficient approach using graph-based evolutionary algorithm for IoT botnet detection 利用基于图的进化算法实现物联网僵尸网络检测的有效方法

Informatica (Slovenia) Pub Date : 2023-05-31 DOI: 10.31449/inf.v47i6.3714

Quoc-Dung Ngo, Huy-Trung Nguyen

{"title":"Towards an efficient approach using graph-based evolutionary algorithm for IoT botnet detection","authors":"Quoc-Dung Ngo, Huy-Trung Nguyen","doi":"10.31449/inf.v47i6.3714","DOIUrl":"https://doi.org/10.31449/inf.v47i6.3714","url":null,"abstract":"In recent years, a large number of Internet of Things devices are used in life, many of which are vulnerable to attacks from a security perspective. Botnet malware is one of the main threats to IoT devices. Hence detection of IoT botnet is one of the most important challenge for IoT devices. This paper proposes an IoT botnet detection approach based on PSI graph data combine with evolutionary algorithm-based technique. In recent years, a large number of Internet of Things devices are used in life, many of which are vulnerable to attacks from a security perspective. Botnet malware is one of the main threats to IoT devices. Hence detection of IoT botnets is one of the most important challenges for IoT devices. In the paper, a IoT botnet detection approach based on PSI graph analysis by using the evolutionary algorithmbased technique. It applies bacterial evolution algorithm (BEA) in the training process on PSI graph multi-architecture IoT Botnet data to detect IoT Botnet. The PSI graphs were extracted from executable files and transform into vectors to feed into the classical machine learning classifiers. The result of the classifiers is then combine using soft voting method with BEA. The proposed method has achieved good experimental results (i.e., Accuracy at 95.30%, F1 at 96.15%). The approach also achieves a relatively low false-positive rate at 4.59%.","PeriodicalId":35802,"journal":{"name":"Informatica (Slovenia)","volume":"143 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80321070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ASM-based Formal Model for Analysing Cloud Auto-Scaling Mechanisms 基于asm的云自动扩展机制形式化分析模型

Informatica (Slovenia) Pub Date : 2023-05-31 DOI: 10.31449/inf.v47i6.4622

E. K. Gavua, G. Kecskeméti

引用次数: 0

Detection of IoT Botnet Cyber Attacks using Machine Learning 利用机器学习检测物联网僵尸网络攻击

Informatica (Slovenia) Pub Date : 2023-05-31 DOI: 10.31449/inf.v47i6.4668

Alaa Dhahi Khaleefah, Haider M. Al-Mashhadi

{"title":"Detection of IoT Botnet Cyber Attacks using Machine Learning","authors":"Alaa Dhahi Khaleefah, Haider M. Al-Mashhadi","doi":"10.31449/inf.v47i6.4668","DOIUrl":"https://doi.org/10.31449/inf.v47i6.4668","url":null,"abstract":"Received: February 8, 2023 As of 2018, the number of online devices has outpaced the global human population, a trend expected to surge towards an estimated 80 billion devices by 2024. With the growing ubiquity of Internet of Things (IoT) devices, securing these systems and the data they exchange has become increasingly complex, especially with the escalating frequency of IoT botnet attacks (IBA). The extensive data quantity and pervasive availability provided by these devices present a lucrative prospect for potential hackers, further escalating cybersecurity risks. Hence, one of the paramount challenges concerning IoT is ensuring its security. The primary objective of this research project is the development of a robust, machine learning algorithm-based model capable of detecting and mitigating botnet-based intrusions within IoT networks. The proposed model tackles the prevalent security issue posed by malicious bot activities. To optimize the model's performance, it was trained using the BoT-IoT dataset, employing a diverse range of machine learning methodologies, including linear regression, logistic regression, KNearest Neighbor (KNN), and Support Vector Machine (SVM) models. The efficacy of these models was evaluated using the F-measure, yielding results of 98.0%, 99.0%, 99.0%, and 99.0% respectively. These outcomes substantiate the models' capacity to accurately distinguish between normal and malicious network activities.","PeriodicalId":35802,"journal":{"name":"Informatica (Slovenia)","volume":"539 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80212793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Provably-Secure LED Block Cipher Diffusion and Confusion based on Chaotic Maps 基于混沌映射的可证明安全LED分组密码扩散与混淆

Informatica (Slovenia) Pub Date : 2023-05-31 DOI: 10.31449/inf.v47i6.4596

Hussain M. Al-Saadi, I. Alshawi

引用次数: 2

An Adaptive Two-Factor Authentication Scheme Based on the Usage of Schnorr Signcryption Algorithm 基于Schnorr签名加密算法的自适应双因素认证方案

Informatica (Slovenia) Pub Date : 2023-05-29 DOI: 10.31449/inf.v47i5.4627

Hussein Albazar, A. Abdel-Wahab, Marwan Alshar'e, Abdallah M. Abualkishik

引用次数: 0

A Review: Investigation of Augmented Reality -BIM Benefits in Design Process in AEC Industry 增强现实bim在AEC行业设计过程中的效益研究综述

Informatica (Slovenia) Pub Date : 2023-05-26 DOI: 10.31449/inf.v47i5.4671

Sadeer Hei AL-Dhaimesh, Nooriati Taib Taib

引用次数: 1