2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA)最新文献_第2页

Malware analysis and multi-label category detection issues: Ensemble-based approaches 恶意软件分析和多标签类别检测问题:基于集成的方法

2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA) Pub Date : 2022-09-05 DOI: 10.1109/IDSTA55301.2022.9923057

I. Alsmadi, B. Al-Ahmad, Mohammad Alsmadi

{"title":"Malware analysis and multi-label category detection issues: Ensemble-based approaches","authors":"I. Alsmadi, B. Al-Ahmad, Mohammad Alsmadi","doi":"10.1109/IDSTA55301.2022.9923057","DOIUrl":"https://doi.org/10.1109/IDSTA55301.2022.9923057","url":null,"abstract":"Detection of malware and security attacks is a complex process that can vary in its details and analysis activities. As part of the detection process, malware scanners try to categorize a malware once it is detected under one of the known malware categories (e.g. worms, spywares, viruses, etc.). However, many studies and researches indicate problems with scanners categorizing or identifying a particular malware under more than one malware category. This paper, and several others, show that machine learning can be used for malware detection especially with ensemble base prediction methods. In this paper, we evaluated several custom-built ensemble models. We focused on multi-label malware classification as individual or classical classifiers showed low accuracy in such territory.This paper showed that recent machine models such as ensemble and deep learning can be used for malware detection with better performance in comparison with classical models. This is very critical in such a dynamic and yet important detection systems where challenges such as the detection of unknown or zero-day malware will continue to exist and evolve.","PeriodicalId":268343,"journal":{"name":"2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115616742","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

An Empirical Evaluation and Comparison of the Impact of MVVM and MVC GUI Driven Application Architectures on Maintainability and Testability MVVM和MVC GUI驱动应用程序架构对可维护性和可测试性影响的实证评估和比较

2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA) Pub Date : 2022-09-05 DOI: 10.1109/IDSTA55301.2022.9923083

Amy Wilson, Fadi Wedyan, Safwan Omari

引用次数: 0

A Feature Selection Technique for Network Intrusion Detection based on the Chaotic Crow Search Algorithm 基于混沌乌鸦搜索算法的网络入侵检测特征选择技术

2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA) Pub Date : 2022-09-05 DOI: 10.1109/IDSTA55301.2022.9923108

Hussein R. Al-Zoubi, Samah Altaamneh

{"title":"A Feature Selection Technique for Network Intrusion Detection based on the Chaotic Crow Search Algorithm","authors":"Hussein R. Al-Zoubi, Samah Altaamneh","doi":"10.1109/IDSTA55301.2022.9923108","DOIUrl":"https://doi.org/10.1109/IDSTA55301.2022.9923108","url":null,"abstract":"Network security is one of the main challenges faced by network administrators and owners, especially with the increasing numbers and types of attacks. This rapid increase results in a need to develop different protection techniques and methods. Network Intrusion Detection Systems (NIDS) are a method to detect and analyze network traffic to identify attacks and notify network administrators. Recently, machine learning (ML) techniques have been extensively applied in developing detection systems. Due to the high complexity of data exchanged over the networks, applying ML techniques will negatively impact system performance as many features need to be analyzed. To select the most relevant features subset from the input data, a feature selection technique is used, which results in enhancing the overall performance of the NIDS. In this paper, we propose a wrapper approach as a feature selection based on a Chaotic Crow Search Algorithm (CCSA) for anomaly network intrusion detection systems. Experiments were conducted on the LITNET-2020 dataset. To the best of our knowledge, our proposed method can be considered the first selection algorithm applied on this dataset based on swarm intelligence optimization to find a special subset of features for binary and multiclass classifications that optimizes the performance for all classes at the same time. The model was evaluated using several ML classifiers namely, K-nearest neighbors (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Multi-layer perceptron (MLP), and Long Short-Term Memory (LSTM). The results proved that the proposed algorithm is more efficient in improving the performance of NIDS in terms of accuracy, detection rate, precision, F-score, specificity, and false alarm rate, outperforming state-of-the-art feature selection techniques recently proposed in the literature.","PeriodicalId":268343,"journal":{"name":"2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121283068","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: 1

Differential Privacy Techniques for Healthcare Data 医疗保健数据的差异隐私技术

2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA) Pub Date : 2022-09-05 DOI: 10.1109/IDSTA55301.2022.9923037

Rishabh Subramanian

{"title":"Differential Privacy Techniques for Healthcare Data","authors":"Rishabh Subramanian","doi":"10.1109/IDSTA55301.2022.9923037","DOIUrl":"https://doi.org/10.1109/IDSTA55301.2022.9923037","url":null,"abstract":"This paper analyzes techniques to enable differential privacy by adding Laplace noise to healthcare data. First, as healthcare data contain natural constraints for data to take only integral values, we show that drawing only integral values does not provide differential privacy. In contrast, rounding randomly drawn values to the nearest integer provides differential privacy. Second, when a variable is constructed using two other variables, noise must be added to only one of them. Third, if the constructed variable is a fraction, then noise must be added to its constituent private variables, and not to the fraction directly. Fourth, the accuracy of analytics following noise addition increases with the privacy budget, $epsilon$, and the variance of the independent variable. Finally, the accuracy of analytics following noise addition increases disproportionately with an increase in the privacy budget when the variance of the independent variable is greater. Using actual healthcare data, we provide evidence supporting the two predictions on the accuracy of data analytics. Crucially, to enable accuracy of data analytics with differential privacy, we derive a relationship to extract the slope parameter in the original dataset using the slope parameter in the noisy dataset.","PeriodicalId":268343,"journal":{"name":"2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133516572","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: 1

On the Development of Mobile Application Breathing Analyzer to Detect Breathing Abnormalities 检测呼吸异常的移动应用呼吸分析仪的开发

2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA) Pub Date : 2022-09-05 DOI: 10.1109/IDSTA55301.2022.9923202

Dylan Hall, Anthony Gladdney, Yasimine Labriny, Y. Alqudah

引用次数: 0

Pseudonym Tracking Certificateless Aggregate Signature Scheme for Preventing Replay Attacks in a Platoon of Vehicles 防止车队重放攻击的假名跟踪无证书聚合签名方案

2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA) Pub Date : 2022-09-05 DOI: 10.1109/IDSTA55301.2022.9923034

Yunpeng Zhang, Daniel Egwede, Guohui Zhang, Xuqing Wu

{"title":"Pseudonym Tracking Certificateless Aggregate Signature Scheme for Preventing Replay Attacks in a Platoon of Vehicles","authors":"Yunpeng Zhang, Daniel Egwede, Guohui Zhang, Xuqing Wu","doi":"10.1109/IDSTA55301.2022.9923034","DOIUrl":"https://doi.org/10.1109/IDSTA55301.2022.9923034","url":null,"abstract":"Cooperative Adaptive Cruise Control (CACC) is an optimistic innovation which allows connected automated vehicles to share parameters with other Connected vehicles in the advanced traffic management system in a distributed manner, thus increasing driving safety and roadway capacity and reducing energy consumption. However, CACC technology is very susceptible to replay attacks because of the connected nature of its technology, so in this technology, when a car within this platoon gets compromised the rest of the vehicles in that platoon under the CACC technology become compromised as well and so it is very important to take steps in order to mitigate such attacks in CACC. This research utilizes the pseudonym tracking certificateless aggregate signature PT-CAS scheme based on existing certificateless signature schemes which ensure user privacy as well as detect and mitigate replay attacks during and after an attack using the pseudonym tracking algorithm. For experimental results we implement algorithm after analysis of our experimental results, we incorporate a pseudonym tracking algorithm to the conventional signature schemes in order to ensure user privacy, after experimental implementation we observe that our proposed PT-CAS scheme is performatively better in terms of computational costs, length of the aggregate signature and privacy preservation than the existing signature schemes, having 97.1% accuracy in detecting and mitigating well-known forms of replay attacks in a platoon of vehicles.","PeriodicalId":268343,"journal":{"name":"2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124604832","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: 1

Analysis Of Cyber Threat Detection And Emulation Using MITRE Attack Framework 基于MITRE攻击框架的网络威胁检测与仿真分析

2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA) Pub Date : 2022-09-05 DOI: 10.1109/IDSTA55301.2022.9923170

P. Rajesh, Mansoor Alam, M. Tahernezhadi, A. Monika, G. Chanakya

{"title":"Analysis Of Cyber Threat Detection And Emulation Using MITRE Attack Framework","authors":"P. Rajesh, Mansoor Alam, M. Tahernezhadi, A. Monika, G. Chanakya","doi":"10.1109/IDSTA55301.2022.9923170","DOIUrl":"https://doi.org/10.1109/IDSTA55301.2022.9923170","url":null,"abstract":"With a rapid increase in Cyber-attacks, Threat hunters such as Cyber Threat Intelligence (CTI) and their teams requires to analyze different techniques being employed by adversaries to hit a target objective. The attacker objectives can be from entering in your network, accessing system files and folders remotely, getting higher system privileges, stealing confidential passwords etc. to destroying systems and network. Pre attack patterns defined in enterprise knowledge base can play a major role to track adversary techniques and procedures in order to defend and response from such attacks. Anomalous and intrusion activities need to be unfolded by the approach adversaries are adopting to demolish secure enterprise networks. An appropriate system is required to better handle modern attack approaches and strategies used by attackers in order to identify vulnerabilities and successfully defend network channels. In this paper, we present an in-depth analysis of different threat detection methods and how to mitigate their impacts using MITRE ATT&CK framework. This framework is an extensively and freely accessible knowledge repository of tactics, techniques and procedures (TTPs) to gain an insight into what techniques adversaries are using in real time applications which aids in developing robust threat controlling programs in private sector, government, and in cybersecurity community.","PeriodicalId":268343,"journal":{"name":"2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122036930","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: 5

Rules-Based Distracted Driving Detection System Using Facial Keypoints 基于规则的面部关键点分心驾驶检测系统

2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA) Pub Date : 2022-09-05 DOI: 10.1109/IDSTA55301.2022.9923045

Evan Lowhorn, Rami J. Haddad

{"title":"Rules-Based Distracted Driving Detection System Using Facial Keypoints","authors":"Evan Lowhorn, Rami J. Haddad","doi":"10.1109/IDSTA55301.2022.9923045","DOIUrl":"https://doi.org/10.1109/IDSTA55301.2022.9923045","url":null,"abstract":"Feasible distracted driving detection systems must be intuitive and non-invasive. Computer vision, a subset of deep learning, provides methods for computer systems to mimic humans in perceiving data from digital imaging. Previous work in distracted driving detection with computer vision has primarily focused on the classification of the entire image, which allows for detection based on body positions and objects in the frame. However, this does not fully isolate the human subject from the background and has possibilities for false positives in certain situations. Keypoint detection is a type of computer vision model capable of plotting points on prominent features of the human body using only a digital camera image. In this work, a rules-based algorithm with Euclidean distance normalization between facial keypoints was developed to determine if driver focus deviates from looking forward while driving. This algorithm also incorporates the steering angle to eliminate false positive detections when looking left and right in acceptable turning situations. This algorithm resulted in 100% accuracy in detecting distracted driving within the testing parameters used. However, future work will incorporate additional vehicle data, different camera types, new visual perception forms, and more practical testing scenarios for increased robustness.","PeriodicalId":268343,"journal":{"name":"2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129660552","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

Using K-means Clustering Ensemble to Improve the Performance in Recommender Systems 利用k -均值聚类集成提高推荐系统的性能

2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA) Pub Date : 2022-09-05 DOI: 10.1109/IDSTA55301.2022.9923070

Hafed Zarzour, Faiz Maazouzi, Mohammad Al-Zinati, Amjad Nusayr, M. Alsmirat, M. Al-Ayyoub, Y. Jararweh

{"title":"Using K-means Clustering Ensemble to Improve the Performance in Recommender Systems","authors":"Hafed Zarzour, Faiz Maazouzi, Mohammad Al-Zinati, Amjad Nusayr, M. Alsmirat, M. Al-Ayyoub, Y. Jararweh","doi":"10.1109/IDSTA55301.2022.9923070","DOIUrl":"https://doi.org/10.1109/IDSTA55301.2022.9923070","url":null,"abstract":"Collaborative filtering methods are often utilized in the industry of recommender systems. They work by identifying users with similar tastes and recommending items for each active user. Besides, clustering techniques are extensively utilized to create systems based on collaborative filtering recommendation in the context of big data. Nevertheless, the cluster ensemble has emerged in last years as a powerful technique that can replace single clustering algorithms in enhancing the performance of recommendation and prediction. This paper presents a k-means clustering ensemble-based method to improve the performance in recommender systems. The proposed system incorporates the Cosine Similarity and the Pearson Correlation Coefficient as similarity metrics to form clusters. Moreover, it uses the HyperGraph Partitioning Algorithm (HGPA) to combine the results of the k-means clustering technique. The recommendation algorithm constructs the recommendations based on the clusters obtained earlier by the HGPA ensemble clustering. To this end, it finds the nearest cluster for each active user and selects its top N items. Finally, it recommends these top items to the user‘s favorite list. The experiments on two well-known datasets demonstrate that cluster ensembles by HGPA outperform the baseline methods.","PeriodicalId":268343,"journal":{"name":"2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA)","volume":"174 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123555057","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: 1

A Machine-Learning Based Approach for Detecting Phishing URLs 基于机器学习的网络钓鱼url检测方法

2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA) Pub Date : 2022-09-05 DOI: 10.1109/IDSTA55301.2022.9923050

Mahmoud Atari, Amjed Al-mousa

{"title":"A Machine-Learning Based Approach for Detecting Phishing URLs","authors":"Mahmoud Atari, Amjed Al-mousa","doi":"10.1109/IDSTA55301.2022.9923050","DOIUrl":"https://doi.org/10.1109/IDSTA55301.2022.9923050","url":null,"abstract":"This research’s focus is to utilize different machine learning classification models to predict whether a given URL is a legitimate or a phishing URL. A legitimate URL directs users to a benign authentic webpage and typically serves the user’s request. In contrast, a phishing URL directs users to a fraudulent website, usually impersonating another entity, luring visitors to believe otherwise, and eventually allowing the attacker to perform limitless post-exploitation attacks. Given the little-to-no internet safety awareness of average individuals, this paper aims to take an adaptive approach to detect phishing URLs on the client-side, which can significantly protect users from falling victims to cyber-attacks such as stealing important personal credentials. The proposed approach is to build a machine-learning powered tool that can help individuals stay safe and assist security researchers in identifying patterns and relations that correlate to these attacks, which will help maintain high-security standards for everyday internet users. Finally, the proposed model yielded a 97% detection accuracy using the XGBoost classifier and the random forest classifier.","PeriodicalId":268343,"journal":{"name":"2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132756018","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: 2