International Journal of Advanced Trends in Computer Science and Engineering最新文献

Network Source Identification Mechanism for IoT Devices Using Machine Learning Techniques 使用机器学习技术的物联网设备网络源识别机制

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2023-12-07 DOI: 10.30534/ijatcse/2023/021262023

{"title":"Network Source Identification Mechanism for IoT Devices Using Machine Learning Techniques","authors":"","doi":"10.30534/ijatcse/2023/021262023","DOIUrl":"https://doi.org/10.30534/ijatcse/2023/021262023","url":null,"abstract":"The rapid progress and evolution of the Internet of Things (IoT) have led to a significant increase in the occurrence of security gaps. Pinpointing the source of network traffic coming from IoT devices can be challenging, but doing so can reduce security risks. This study proposes a network traffic source identification mechanism that leverages machine learning (ML) techniques to accurately determine the source of network traffic. The study utilizes a diverse dataset obtained from a purpose-built IoT/IIoT testbed and employs feature extraction, model development, and evaluation techniques. By utilizing network traffic features, a range of classifiers, including LGBMClassifier (LGBM), CatBoostClassifier (CB), RandomForestClassifier (RF), ExtraTreesClassifier (ET), KneighborsClassifier (KNN), and DecisionTreeClassifier (DT), were trained and evaluated. The results demonstrate exceptional performance across the classifiers, with high accuracy, precision, recall, and F1 scores achieved in identifying the source of network traffic. Among the classifier models, LGBM achieved the best accuracy value of 0.99999857, precision value of 0.99999859, and F1 score of 0.999998803, with CB achieving the best recall of 0.999997875. Some of these results are novel, and others performed better than existing systems. The findings of this study contribute to source identification, ensure the accountability of IoT network users, and provide insights into developing better defenses against security threats in the IoT domain","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"54 50","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138593206","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

A Hybrid Intrusion Detection Model to Alleviate Denial of Service and Distributed Denial of Service Attacks in Internet of Things 缓解物联网中拒绝服务和分布式拒绝服务攻击的混合入侵检测模型

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2023-12-07 DOI: 10.30534/ijatcse/2023/031262023

{"title":"A Hybrid Intrusion Detection Model to Alleviate Denial of Service and Distributed Denial of Service Attacks in Internet of Things","authors":"","doi":"10.30534/ijatcse/2023/031262023","DOIUrl":"https://doi.org/10.30534/ijatcse/2023/031262023","url":null,"abstract":"The Internet of Things (IoT) refers to a network of interconnected smart devices. The growth of IoT devices has increased the vulnerability of the network to attacks, such as Denial of Service (DoS) and Distributed Denial of Service (DDoS). Denial-of-Service (DoS) attacks are malicious activities aimed at rendering a computer network, system, or online service unavailable to legitimate users. This research addresses the growing vulnerability of IoT networks to DoS/DDoS attacks by developing a hybrid intrusion detection model to detect these attacks. The model integrates Kalman Filter (KF) with Artificial Neural Network (KF-ANN), Random Forest (KF-RF), Support Vector Machine (KF-SVM) and K-Nearest Neighbor (KF-KNN) machine learning models. The Kalman filter is an efficient tool for estimating the state of a system especially in the midst of uncertainty. Kalman filter was used to estimate the state of the system while the machine learning models were used to make predictions based on the estimated state to detect attacks in IoT. The model was tested using the DoS/DDoS Message Queueing Telemetry Protocol (MQTT) IoT dataset. Results shows Receiver Operative Curve Area Under the Curve (ROC-AUC) of 0.99% for KF-ANN and KF-RF, 0.98% and 0.97% for KF-KNN and KF-SVM. Detection accuracy of approximately 0.96%, 0.94% and 93% for KF-RF and KF-ANN, KF-KNN and KF-SVM respectively","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"31 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138594410","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

A Prototype of Smart Plastic Bottle Recycle Machine Using IoT 利用物联网的智能塑料瓶回收机原型

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2023-12-07 DOI: 10.30534/ijatcse/2023/011262023

{"title":"A Prototype of Smart Plastic Bottle Recycle Machine Using IoT","authors":"","doi":"10.30534/ijatcse/2023/011262023","DOIUrl":"https://doi.org/10.30534/ijatcse/2023/011262023","url":null,"abstract":"The growing concern over plastic pollution and its adverse impact on the environment has prompted the development of innovative solutions to address the issue effectively. Improper disposal of plastic bottles leads to environmental pollution. This paper presents the design and implementation of a prototype of smart plastic bottle recycle machine, integrating the Internet of Things (IoT). The Arduino board is used to control the machine. The smart plastic bottle recycling machine incorporates the ESP8266 and a Wi-Fi enabled system-on-chip (SoC) module used to develop IoT embedded applications. The utilization of IoT technology enables seamless real-time data transmission, allowing for efficient communication with the central system and simpliying remote monitoring and management processes. The IoT-enabled sensors and cameras capture information about the plastic bottles, such as their material, size, and condition. Thus, it makes recycling easy and satisfying, which motivates people to get involved in recycling activities. Moreover, implementing a point system that rewards individuals for their recycling efforts not only serves as an extra motivation for users, but also encourages them to actively participate in the preservation of the environment. Therefore, this prototype demonstrates the potential to foster sustainable recycling practices, which is a promising first step towards a future that prioritizes environmental consciousness","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"5 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138591295","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

Optimizing Subcontractor Work through ISO 31000 Risk Assessment Method 通过ISO 31000风险评估方法优化分包商的工作

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2023-10-15 DOI: 10.30534/ijatcse/2023/061252023

引用次数: 0

Predictive Modeling for Enhancing Academic Performance in Nigerian Polytechnic Education 提高尼日利亚理工教育学习成绩的预测模型

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2023-10-08 DOI: 10.30534/ijatcse/2023/041252023

{"title":"Predictive Modeling for Enhancing Academic Performance in Nigerian Polytechnic Education","authors":"","doi":"10.30534/ijatcse/2023/041252023","DOIUrl":"https://doi.org/10.30534/ijatcse/2023/041252023","url":null,"abstract":"This study presents a machine learning-based approach to enhance the classification and optimization of students’ academic performance within Nigeria’s polytechnic education system. The polytechnic system is pivotal in providing technical and vocational education, but challenges persist in nurturing students’ academic achievement. This article explores the complexities influencing academic performance and proposes strategies for improvement using machine learning algorithms. The research utilizes linear and support vector regression models to predict students’ cumulative grade point averages (CGPA). A dataset from Akwa Ibom State Polytechnic, Ikot Osurua, comprising total courses, credit units, department, and previous grade point average (GPA), is employed for model development and evaluation. Both models achieve similar predictive performance, but linear regression slightly outperforms support vector regression. The results highlight the significant role of variables like total courses, the type of academic department, and previous GPA in predicting CGPA. This study offers a valuable tool for assessing and improving students’ academic performance in Nigeria’s polytechnic education system, with potential for broader applications in higher education. Further research involves expanding the dataset and considering additional factors beyond result records to enhance the model’s robustness and applicability.","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135198253","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

Clustering of Feature Vectors and Recognition of Bodo Phoneme Using MLP Technique 基于MLP技术的Bodo音素特征向量聚类与识别

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2023-10-08 DOI: 10.30534/ijatcse/2023/051252023

引用次数: 0

Supporting Curriculum Designers in Developing Balanced Outcome-based Programs using Knowledge Graphs 支持课程设计者使用知识图谱开发平衡的基于结果的程序

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2023-10-08 DOI: 10.30534/ijatcse/2023/021252023

{"title":"Supporting Curriculum Designers in Developing Balanced Outcome-based Programs using Knowledge Graphs","authors":"","doi":"10.30534/ijatcse/2023/021252023","DOIUrl":"https://doi.org/10.30534/ijatcse/2023/021252023","url":null,"abstract":"It is essential for universities to accredit their programs to be recognized locally and globally and to maintain the quality and credibility of educational programs. For programs to become accredited, they must meet predefined quality standards for the intended accreditation institution. These quality standards are applied to program courses, and each course covers some requirements from the accreditation requirements, such as Student and Course Learning Outcomes, based on their specific accreditation commission. As programs and courses are added frequently at university programs, information about accreditation mapped to each course syllabus and program description is becoming more complicated. It must be maintained automatically since program accreditation must be renewed on a regular basis to guarantee the program's quality. In addition, supporting Curriculum Designers in finding gaps and imbalances in the proposed programs and courses is becoming necessary to alleviate the management of accreditation related tasks. In this paper, we propose a knowledge graph-based system that supports the development of balanced educational programs and helps detecting gaps and redundancies within the same university programs. The system provides an attractive graphical user interface that visualizes the curriculum components and accreditation requirements as interactive graphs. The system has been implemented using the graph database Neo4j and the results are analyzed using the Cypher query language","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135198094","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

Botnet Threat Intelligence in IoT-Edge 物联网边缘中的僵尸网络威胁情报

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2023-10-08 DOI: 10.30534/ijatcse/2023/011252023

{"title":"Botnet Threat Intelligence in IoT-Edge","authors":"","doi":"10.30534/ijatcse/2023/011252023","DOIUrl":"https://doi.org/10.30534/ijatcse/2023/011252023","url":null,"abstract":"Recently, deep learning has gotten progressively popular in the domain of security. However, Traditional machine learning models are not capable to discover zero-day botnet attacks with extraordinary privacy. For this purpose, researchers have utilized deep learning based computational framework for Botnet which can detect zero-day attacks, achieve data privacy and improve training time using machine learning techniques for the IoT-edge devices. However, it combines and integrates various models and contexts. As a result, the objective of this research was to incorporate the deep learning model which controls different operation of IoT devices and reduce the training time. In deep learning, there are numerous components that aspect the false positive rate of every detected attack type. These elements are F1 score, false-positive rate, and training time; reduce the time of detection, and Accuracy. Bashlite and Mirai are two examples of zero-day botnet attacks that pose a threat to IoT edge devices. The majority of cyber-attacks are executed by malware-infected devices that are remotely controlled by attackers. This malware is often referred to as a bot or botnet, and it enables attackers to control the device and perform malicious actions, such as spamming, stealing sensitive information, and launching DDoS attacks. The model was formulated in Python libraries and subsequently tested on real life data to assess whether the integrated model performs better than its counterparts. The outcomes show that the proposed model performs in a way that is better than existing models i.e. DDL, CDL and LDL as Botnet Attacks Intelligence (BAI) the purposed deep learning model.","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135198255","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

The Magic of Environmental Detection and Future Prediction by Satellite 卫星环境探测与未来预测的魔力

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2023-10-08 DOI: 10.30534/ijatcse/2023/031252023

引用次数: 0

Fair Rewards in Federated Learning: A Novel Approach with Adjusted OR-TMC Shapley Value Approximation Algorithm 联邦学习中的公平奖励:基于调整OR-TMC Shapley值近似算法的新方法

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2023-08-10 DOI: 10.30534/ijatcse/2023/081242023

引用次数: 0