Pub Date : 2024-07-01DOI: 10.58245/ipsi.tir.2402.03
Muhammad Nabeel Asghar, Muhammad Asif Raza, Zara Murad, Ahmed Alyahya
The widespread use of the Internet of Things (IoT) has led to a rise in botnet attacks, with the Mirai botnet being a major source of Distributed Denial of Service (DDOS) attacks. Mirai gained notoriety for its involvement in large-scale attacks that compromised numerous IoT devices through weak authentication credentials. Similarly, Bashlite, also known as Gafgyt or Lizkebab, targets vulnerable IoT devices by exploiting the Shellshock vulnerability in Linux-based systems. These botnets leverage compromised devices to carry out malicious activities and the propagation of malware. While Machine Learning (ML) based approaches have been proposed to identify botnets, however, detecting both Mirai and Bashlite botnets simultaneously is challenging as their attack characteristics are not very similar. In this study, we apply ML techniques like Logistic Regression, Support Vector Machine and Random Forest to classify the malicious traffic from Mirai and Bashlite botnets. The publicly available NBaIoT dataset is used for the training of algorithms to identify the most informative features to detect botnet traffic targeting IoT devices. The dataset contains traffic data from nine infected devices against five protocols. The employed machine learning algorithms achieved test validation accuracy above 99%, with Random Forest performing the best. Our analysis shows that devices generating combo floods share common characteristics like weight or variance calculated within a certain time window.
{"title":"Detecting Malicious Botnets in IoT Networks Using Machine Learning Techniques","authors":"Muhammad Nabeel Asghar, Muhammad Asif Raza, Zara Murad, Ahmed Alyahya","doi":"10.58245/ipsi.tir.2402.03","DOIUrl":"https://doi.org/10.58245/ipsi.tir.2402.03","url":null,"abstract":"The widespread use of the Internet of Things (IoT) has led to a rise in botnet attacks, with the Mirai botnet being a major source of Distributed Denial of Service (DDOS) attacks. Mirai gained notoriety for its involvement in large-scale attacks that compromised numerous IoT devices through weak authentication credentials. Similarly, Bashlite, also known as Gafgyt or Lizkebab, targets vulnerable IoT devices by exploiting the Shellshock vulnerability in Linux-based systems. These botnets leverage compromised devices to carry out malicious activities and the propagation of malware. While Machine Learning (ML) based approaches have been proposed to identify botnets, however, detecting both Mirai and Bashlite botnets simultaneously is challenging as their attack characteristics are not very similar. In this study, we apply ML techniques like Logistic Regression, Support Vector Machine and Random Forest to classify the malicious traffic from Mirai and Bashlite botnets. The publicly available NBaIoT dataset is used for the training of algorithms to identify the most informative features to detect botnet traffic targeting IoT devices. The dataset contains traffic data from nine infected devices against five protocols. The employed machine learning algorithms achieved test validation accuracy above 99%, with Random Forest performing the best. Our analysis shows that devices generating combo floods share common characteristics like weight or variance calculated within a certain time window.","PeriodicalId":516644,"journal":{"name":"IPSI Transactions on Internet Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141689990","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}
Pub Date : 2024-07-01DOI: 10.58245/ipsi.tir.2402.09
Daliborka Macinkovic, Vidan Marković
This work proposes the model of the event processing network for the systematic reduction of interoperability deviations in a business ecosystem. Complex event processing technology supports real-time events monitoring in collaborative business processes for the behaviors specified as interoperability deviations, generating alerts when such situations occur. As a reaction, alerts on interoperability deviations are delivered as personalized information to the right consumer as a designated collaboration partner. The event processing network enables collaboration partners to be proactive in interoperability deviations and to eliminate the impact of interoperability deviations on the business process objectives in the business ecosystem. In observing the data as events about realized collaborative business processes during this research, characteristic patterns of events were discovered. The logic for event processing was specified for the systematic reduction of interoperability deviations in the business ecosystem. This work proposes two processes with phases to facilitate the development of the proposed event processing network in a business ecosystem. The logic for event processing was applied through data streams from the public administration and health care systems in collaboration.
{"title":"The Event Processing Network for Systematic Reduction of Interoperability Deviations in a Business Ecosystem","authors":"Daliborka Macinkovic, Vidan Marković","doi":"10.58245/ipsi.tir.2402.09","DOIUrl":"https://doi.org/10.58245/ipsi.tir.2402.09","url":null,"abstract":"This work proposes the model of the event processing network for the systematic reduction of interoperability deviations in a business ecosystem. Complex event processing technology supports real-time events monitoring in collaborative business processes for the behaviors specified as interoperability deviations, generating alerts when such situations occur. As a reaction, alerts on interoperability deviations are delivered as personalized information to the right consumer as a designated collaboration partner. The event processing network enables collaboration partners to be proactive in interoperability deviations and to eliminate the impact of interoperability deviations on the business process objectives in the business ecosystem. In observing the data as events about realized collaborative business processes during this research, characteristic patterns of events were discovered. The logic for event processing was specified for the systematic reduction of interoperability deviations in the business ecosystem. This work proposes two processes with phases to facilitate the development of the proposed event processing network in a business ecosystem. The logic for event processing was applied through data streams from the public administration and health care systems in collaboration.","PeriodicalId":516644,"journal":{"name":"IPSI Transactions on Internet Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141709916","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}
Pub Date : 2024-07-01DOI: 10.58245/ipsi.tir.2402.02
Anjum, Muhammad Adeel Anjum, Ahmad Alanzi
The intelligent transportation system needs to accurately assess the volume of traffic in the environment in which it operates to ensure that people are moved in a timely and hassle-free manner. Forecasting systems allow drivers to identify the route that will take them to their destination with the slightest difficulty and the least time spent in congested regions. At present, both the corporate sector and government organizations require accurate and timely traffic flow information. There have been no significant efforts to enhance road traffic prediction by utilizing air pollution data. This paper aims to present a new method for predicting road traffic using data related to pollution. Our contribution to this research is twofold. Firstly, we compared ten regression approaches to determine which technique provides better results and accuracy. Secondly, we present a technique based on regression analysis approaches in which we choose those base learners who give better results on Level 1. These predictions are combined as an input to a Level 2 meta regressor. A method is proposed to show that it generates more satisfactory results than any of the regression procedures discussed previously. Compared with the various regression methodologies, the proposed method successfully lowers the mean square error, the relative absolute error, and the root mean square error and improves the R-squared value.
智能交通系统需要准确评估其运行环境中的交通流量，以确保及时、无障碍地运送人员。通过预测系统，驾驶员可以确定到达目的地的路线，在拥堵地区行驶的难度最小，花费的时间最少。目前，企业部门和政府机构都需要准确及时的交通流量信息。目前还没有利用空气污染数据来加强道路交通预测的重大努力。本文旨在提出一种利用污染相关数据预测道路交通的新方法。我们对这项研究有两方面的贡献。首先，我们比较了十种回归方法，以确定哪种技术能提供更好的结果和准确性。其次，我们提出了一种基于回归分析方法的技术，在这种技术中，我们选择那些在第 1 级中结果较好的基础学习者。这些预测结果将作为第二级元回归器的输入。我们提出了一种方法，证明它比之前讨论的任何回归程序都能产生更令人满意的结果。与各种回归方法相比，所提出的方法成功地降低了均方误差、相对绝对误差和均方根误差，并提高了 R 平方值。
{"title":"\"Smart Urban Planning: An Intelligent Framework to Predict Traffic Using Stack Ensembling Approach \"","authors":"Anjum, Muhammad Adeel Anjum, Ahmad Alanzi","doi":"10.58245/ipsi.tir.2402.02","DOIUrl":"https://doi.org/10.58245/ipsi.tir.2402.02","url":null,"abstract":"The intelligent transportation system needs to accurately assess the volume of traffic in the environment in which it operates to ensure that people are moved in a timely and hassle-free manner. Forecasting systems allow drivers to identify the route that will take them to their destination with the slightest difficulty and the least time spent in congested regions. At present, both the corporate sector and government organizations require accurate and timely traffic flow information. There have been no significant efforts to enhance road traffic prediction by utilizing air pollution data. This paper aims to present a new method for predicting road traffic using data related to pollution. Our contribution to this research is twofold. Firstly, we compared ten regression approaches to determine which technique provides better results and accuracy. Secondly, we present a technique based on regression analysis approaches in which we choose those base learners who give better results on Level 1. These predictions are combined as an input to a Level 2 meta regressor. A method is proposed to show that it generates more satisfactory results than any of the regression procedures discussed previously. Compared with the various regression methodologies, the proposed method successfully lowers the mean square error, the relative absolute error, and the root mean square error and improves the R-squared value.","PeriodicalId":516644,"journal":{"name":"IPSI Transactions on Internet Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141704998","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}
Pub Date : 2024-07-01DOI: 10.58245/ipsi.tir.2402.07
Muhammad Aleem Shakeel, Hasan Ali Khattak, Numan Khurshid
The Holy Quran has the utmost importance for the Muslim community, and to get a full reward, the Quran should be read according to the rules mentioned. In the past few years, this field has gained a lot of importance in the eyes of researchers who aim to automate the Quranic reading and understanding process with the help of Machine Learning and Deep Learning, knowing it has a lot of challenges. To date, there are a lot of research categories explored. However, still, there lacks a few holistic, including one detailed survey of all the categories and methodologies used to solve problems. We focused the paper on being a one-stop-shop for the people interested so they could find (i) all related information and (ii) future gaps in research. This paper provides a detailed survey on Deep Modeling for Quranic Recitation to address these challenges. We discussed all possible categories of speech analysis, including the most advanced feature extraction techniques, mispronunciation detection using Tajweed rules, Reciters and speech dialect classification, and implementation of Automatic Speech Recognition (ASR) on Quranic Recitations. We also discussed research challenges in this domain and identified possible future gaps.
古兰经》对穆斯林社区至关重要，要想获得充分的回报，就必须按照所述规则阅读《古兰经》。在过去的几年中，这一领域在研究人员眼中获得了极大的重视，他们希望借助机器学习和深度学习实现古兰经阅读和理解过程的自动化，因为他们深知这其中存在着许多挑战。迄今为止，已探索出很多研究类别。但是，仍然缺乏一些整体性的研究，包括对所有类别和用于解决问题的方法的详细调查。我们将本文的重点放在为感兴趣的人提供一站式服务上，这样他们就能找到 (i) 所有相关信息和 (ii) 未来的研究空白。本文对古兰经背诵的深度建模进行了详细调查，以应对这些挑战。我们讨论了所有可能的语音分析类别，包括最先进的特征提取技术、使用 Tajweed 规则的错误发音检测、朗诵者和语音方言分类，以及古兰经朗诵自动语音识别 (ASR) 的实现。我们还讨论了这一领域的研究挑战，并确定了未来可能存在的差距。
{"title":"Deep Acoustic Modelling for Quranic Recitation – Current Solutions and Future Directions","authors":"Muhammad Aleem Shakeel, Hasan Ali Khattak, Numan Khurshid","doi":"10.58245/ipsi.tir.2402.07","DOIUrl":"https://doi.org/10.58245/ipsi.tir.2402.07","url":null,"abstract":"The Holy Quran has the utmost importance for the Muslim community, and to get a full reward, the Quran should be read according to the rules mentioned. In the past few years, this field has gained a lot of importance in the eyes of researchers who aim to automate the Quranic reading and understanding process with the help of Machine Learning and Deep Learning, knowing it has a lot of challenges. To date, there are a lot of research categories explored. However, still, there lacks a few holistic, including one detailed survey of all the categories and methodologies used to solve problems. We focused the paper on being a one-stop-shop for the people interested so they could find (i) all related information and (ii) future gaps in research. This paper provides a detailed survey on Deep Modeling for Quranic Recitation to address these challenges. We discussed all possible categories of speech analysis, including the most advanced feature extraction techniques, mispronunciation detection using Tajweed rules, Reciters and speech dialect classification, and implementation of Automatic Speech Recognition (ASR) on Quranic Recitations. We also discussed research challenges in this domain and identified possible future gaps.","PeriodicalId":516644,"journal":{"name":"IPSI Transactions on Internet Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141712381","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}
Pub Date : 2024-07-01DOI: 10.58245/ipsi.tir.2402.08
Ljiljana Kašćelan, Sunčica Vuković
The main challenge in machine learning-based customer response models is the class imbalance problem, i.e. small number of respondents, compared to non-respondents. Aiming to overcome this issue, the approach of preprocessing training data using a Support Vector Machine (SVM), trained on a balanced sample obtained by random undersampling (B-SVM), as well as on a balanced sample obtained by clustering-based undersampling (CB-SVM) was tested. Several classifiers are tested on such a balanced dataset, to compare their predictive performances. The results of this paper demonstrate that the approach effectively preprocesses the training data, and, in turn, reduces noise and overcomes the class imbalance problem. Better predictive performance was achieved compared to standard training data balancing techniques such as undersampling and SMOTE. CB-SVM gives a better sensitivity, while B-SVM gives a better ratio of sensitivity and specificity. Organizations can utilize this approach to balance training data automatically and simply and more efficiently select customers that should be targeted in the next direct marketing campaigns.
{"title":"Addressing Class Imbalance in Customer Response Modeling Using Random and Clustering-Based Undersampling and SVM","authors":"Ljiljana Kašćelan, Sunčica Vuković","doi":"10.58245/ipsi.tir.2402.08","DOIUrl":"https://doi.org/10.58245/ipsi.tir.2402.08","url":null,"abstract":"The main challenge in machine learning-based customer response models is the class imbalance problem, i.e. small number of respondents, compared to non-respondents. Aiming to overcome this issue, the approach of preprocessing training data using a Support Vector Machine (SVM), trained on a balanced sample obtained by random undersampling (B-SVM), as well as on a balanced sample obtained by clustering-based undersampling (CB-SVM) was tested. Several classifiers are tested on such a balanced dataset, to compare their predictive performances. The results of this paper demonstrate that the approach effectively preprocesses the training data, and, in turn, reduces noise and overcomes the class imbalance problem. Better predictive performance was achieved compared to standard training data balancing techniques such as undersampling and SMOTE. CB-SVM gives a better sensitivity, while B-SVM gives a better ratio of sensitivity and specificity. Organizations can utilize this approach to balance training data automatically and simply and more efficiently select customers that should be targeted in the next direct marketing campaigns.","PeriodicalId":516644,"journal":{"name":"IPSI Transactions on Internet Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141693139","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}
Pub Date : 2024-07-01DOI: 10.58245/ipsi.tir.2402.04
Jabeen Mehmoona, Maple Carsten
In the contemporary digital landscape, the escalating reliance on diverse social media platforms for textual communication necessitates the establishment of secure and trustworthy channels to thwart the threats of theft or hacking. Most of these messages contain highly confidential data, underscoring the critical need for robust security measures, primarily through the deployment of encryption techniques. While existing algorithms predominantly employ text-to-text encryption (TOTET) methods, this paper introduces an innovative hybrid approach that amalgamates TOTET with text-to-image encryption (TOIET), thereby fortifying the privacy of transmitted messages. This novel method undergoes rigorous testing using various parameters, including privacy levels and encryption time, to evaluate its effectiveness. Comparative analyses are conducted against established techniques such as DES, 3DES, and AES. The experimental results conclusively demonstrate the superior privacy capabilities of the proposed scheme in comparison to its predecessors. This advancement in encryption technology not only bolsters the security of confidential messages but also positions itself as a noteworthy improvement over existing methods, marking a pivotal step towards ensuring the integrity of digital communications.
{"title":"Enhancing Security of Text Using Affine Cipher and Image Cryptography","authors":"Jabeen Mehmoona, Maple Carsten","doi":"10.58245/ipsi.tir.2402.04","DOIUrl":"https://doi.org/10.58245/ipsi.tir.2402.04","url":null,"abstract":"In the contemporary digital landscape, the escalating reliance on diverse social media platforms for textual communication necessitates the establishment of secure and trustworthy channels to thwart the threats of theft or hacking. Most of these messages contain highly confidential data, underscoring the critical need for robust security measures, primarily through the deployment of encryption techniques. While existing algorithms predominantly employ text-to-text encryption (TOTET) methods, this paper introduces an innovative hybrid approach that amalgamates TOTET with text-to-image encryption (TOIET), thereby fortifying the privacy of transmitted messages. This novel method undergoes rigorous testing using various parameters, including privacy levels and encryption time, to evaluate its effectiveness. Comparative analyses are conducted against established techniques such as DES, 3DES, and AES. The experimental results conclusively demonstrate the superior privacy capabilities of the proposed scheme in comparison to its predecessors. This advancement in encryption technology not only bolsters the security of confidential messages but also positions itself as a noteworthy improvement over existing methods, marking a pivotal step towards ensuring the integrity of digital communications.","PeriodicalId":516644,"journal":{"name":"IPSI Transactions on Internet Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141711261","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}
Pub Date : 2024-07-01DOI: 10.58245/ipsi.tir.2402.11
Norbert Ferenčík, Veronika Sedláková, Petra Kolembusova, B. Štefanovič, Radovan Hudák, William Steingartner
Spinal cord injury (SCI) poses a significant medical challenge, affecting both hand dexterity and locomotor abilities. Ongoing advancements in medical technologies, spanning a spectrum of wearable devices, coupled with concurrent progress in rehabilitation treatments, aim to enhance hand function among individuals affected by SCI. The emergence of three-dimensional (3D) printing provides a cost-effective avenue for crafting personalized devices, fostering a surge of interest in integrating this technology with rehabilitation equipment, thereby complementing advancements in scientific research. Myoelectric control plays a pivotal role in achieving enhanced rehabilitation outcomes. It involves the detection and processing of weak electromyographic signals (EMG) from affected limb muscles to activate orthotic motors. A novel 3D-printed hand orthosis, responsive to electromyography signals, has been developed to facilitate grasping functionality in cervical SCI patients.
脊髓损伤（SCI）是一项重大的医疗挑战，会影响手部灵活性和运动能力。医疗技术的不断进步涵盖了一系列可穿戴设备，再加上康复治疗的同步进展，旨在增强脊髓损伤患者的手部功能。三维（3D）打印技术的出现为制作个性化设备提供了一条具有成本效益的途径，激发了人们将这项技术与康复设备相结合的兴趣，从而与科学研究的进步相辅相成。肌电控制在提高康复效果方面发挥着举足轻重的作用。它包括检测和处理来自受影响肢体肌肉的微弱肌电信号（EMG），以激活矫形电机。目前已开发出一种新型 3D 打印手部矫形器，可对肌电信号做出反应，以促进颈椎 SCI 患者的抓握功能。
{"title":"Innovative Solutions for Tetraplegia: A Smart Hand Orthosis Design","authors":"Norbert Ferenčík, Veronika Sedláková, Petra Kolembusova, B. Štefanovič, Radovan Hudák, William Steingartner","doi":"10.58245/ipsi.tir.2402.11","DOIUrl":"https://doi.org/10.58245/ipsi.tir.2402.11","url":null,"abstract":"Spinal cord injury (SCI) poses a significant medical challenge, affecting both hand dexterity and locomotor abilities. Ongoing advancements in medical technologies, spanning a spectrum of wearable devices, coupled with concurrent progress in rehabilitation treatments, aim to enhance hand function among individuals affected by SCI. The emergence of three-dimensional (3D) printing provides a cost-effective avenue for crafting personalized devices, fostering a surge of interest in integrating this technology with rehabilitation equipment, thereby complementing advancements in scientific research. Myoelectric control plays a pivotal role in achieving enhanced rehabilitation outcomes. It involves the detection and processing of weak electromyographic signals (EMG) from affected limb muscles to activate orthotic motors. A novel 3D-printed hand orthosis, responsive to electromyography signals, has been developed to facilitate grasping functionality in cervical SCI patients.","PeriodicalId":516644,"journal":{"name":"IPSI Transactions on Internet Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141705894","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}
Pub Date : 2024-07-01DOI: 10.58245/ipsi.tir.2402.06
Azhar Ghafoor, M. A. Shah, Bilal Zaka, Muhammad Nawaz
Phishing remains a pervasive cybersecurity threat, leveraging social engineering and technological deception to obtain sensitive information and credentials. This research explores novel attack paths employed by sophisticated adversaries, focusing on the identification and analysis of emerging tactics to enhance understanding and awareness of evolving phishing threats. The study uncovers various attack vectors, including the impersonation of reputable entities and the exploitation of legitimate platforms for malicious purposes. Notably, it highlights the increasing prevalence of documentbased and social media-based phishing campaigns, underscoring the adaptability of attackers in exploiting diverse channels to deceive users. Furthermore, the research evaluates the effectiveness of current countermeasures and proposes actionable strategies to mitigate phishing risks for organizations. Recommendations include strengthening email protection measures, implementing robust web filtering systems, and conducting simulated phishing campaigns to enhance employee awareness. By providing insights into emerging attack paths and practical recommendations, this research contributes to the ongoing efforts to combat phishing threats and strengthen cybersecurity resilience. The findings underscore the critical importance of proactive measures and continuous vigilance in safeguarding against evolving cyber threats in today's dynamic digital landscape.
{"title":"Discover and Automate New Adversarial Attack Paths to Reduce Threat Risks for The Security of Organizations","authors":"Azhar Ghafoor, M. A. Shah, Bilal Zaka, Muhammad Nawaz","doi":"10.58245/ipsi.tir.2402.06","DOIUrl":"https://doi.org/10.58245/ipsi.tir.2402.06","url":null,"abstract":"Phishing remains a pervasive cybersecurity threat, leveraging social engineering and technological deception to obtain sensitive information and credentials. This research explores novel attack paths employed by sophisticated adversaries, focusing on the identification and analysis of emerging tactics to enhance understanding and awareness of evolving phishing threats. The study uncovers various attack vectors, including the impersonation of reputable entities and the exploitation of legitimate platforms for malicious purposes. Notably, it highlights the increasing prevalence of documentbased and social media-based phishing campaigns, underscoring the adaptability of attackers in exploiting diverse channels to deceive users. Furthermore, the research evaluates the effectiveness of current countermeasures and proposes actionable strategies to mitigate phishing risks for organizations. Recommendations include strengthening email protection measures, implementing robust web filtering systems, and conducting simulated phishing campaigns to enhance employee awareness. By providing insights into emerging attack paths and practical recommendations, this research contributes to the ongoing efforts to combat phishing threats and strengthen cybersecurity resilience. The findings underscore the critical importance of proactive measures and continuous vigilance in safeguarding against evolving cyber threats in today's dynamic digital landscape.","PeriodicalId":516644,"journal":{"name":"IPSI Transactions on Internet Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141717141","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}
Pub Date : 2024-07-01DOI: 10.58245/ipsi.tir.2402.05
Usama Arshad, Y. Faheem, Shaheen Reema
In the current era, drones are being used more often for surveillance and gathering information in many areas. However, as we start using drones more, we face important issues like whether we can trust the data they collect, how safe that data is from hackers and concerns about invading people's privacy. This paper introduces a new idea that uses blockchain technology, which is known for being secure and hard to tamper with, together with drone surveillance to tackle these problems. We used blockchain's key features, such as its ability to work across many places, its security against changes, and the need for agreement, to make sure the data we get is reliable and trustworthy. Our proposed system also makes it easy to tell which drone collected which data, improving how we can track drones and protect the data from being hacked or changed without permission. A big part of our approach is using smart contracts, which are a clear way to manage, apply, and check who can access certain data. We also put a lot of effort into keeping private information safe, only sharing what's absolutely necessary. Our goal with this system is to make people trust the data collected by drones more, leading to a new phase of accepted and reliable drone surveillance.
{"title":"Futuristic Blockchain-based Secure and Verifiable Drone Surveillance System: Chain in the Sky","authors":"Usama Arshad, Y. Faheem, Shaheen Reema","doi":"10.58245/ipsi.tir.2402.05","DOIUrl":"https://doi.org/10.58245/ipsi.tir.2402.05","url":null,"abstract":"In the current era, drones are being used more often for surveillance and gathering information in many areas. However, as we start using drones more, we face important issues like whether we can trust the data they collect, how safe that data is from hackers and concerns about invading people's privacy. This paper introduces a new idea that uses blockchain technology, which is known for being secure and hard to tamper with, together with drone surveillance to tackle these problems. We used blockchain's key features, such as its ability to work across many places, its security against changes, and the need for agreement, to make sure the data we get is reliable and trustworthy. Our proposed system also makes it easy to tell which drone collected which data, improving how we can track drones and protect the data from being hacked or changed without permission. A big part of our approach is using smart contracts, which are a clear way to manage, apply, and check who can access certain data. We also put a lot of effort into keeping private information safe, only sharing what's absolutely necessary. Our goal with this system is to make people trust the data collected by drones more, leading to a new phase of accepted and reliable drone surveillance.","PeriodicalId":516644,"journal":{"name":"IPSI Transactions on Internet Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141690339","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}
Pub Date : 2024-07-01DOI: 10.58245/ipsi.tir.2402.10
William Steingartner, Igor Sivý
The abstract implementation of the language on some abstract machine is a logical step in the definition of its operational semantics and in the definition of its (partially) correct implementation. An abstract machine for operational semantics is a well-known formalism in proving the correctness of programming languages. There are several ways to define an abstract machine for a given language specification. In our article, we focus on an abstract machine for structural operational semantics as a stack machine with two different model representations of memory, and we present a complex tool enabling compilation from a higher imperative (toy) language into an abstract machine allowing, in addition, the visualization of individual computational steps, interactive memory manipulation and feedback by compiling back to a higher language. This work presents an abstract machine designed primarily for educational purposes, enabling the visualization and interaction with the compilation process of a simple imperative language.
{"title":"Enhancing Semantics Learning: A Dynamic Environment for Abstract Language Implementation Education","authors":"William Steingartner, Igor Sivý","doi":"10.58245/ipsi.tir.2402.10","DOIUrl":"https://doi.org/10.58245/ipsi.tir.2402.10","url":null,"abstract":"The abstract implementation of the language on some abstract machine is a logical step in the definition of its operational semantics and in the definition of its (partially) correct implementation. An abstract machine for operational semantics is a well-known formalism in proving the correctness of programming languages. There are several ways to define an abstract machine for a given language specification. In our article, we focus on an abstract machine for structural operational semantics as a stack machine with two different model representations of memory, and we present a complex tool enabling compilation from a higher imperative (toy) language into an abstract machine allowing, in addition, the visualization of individual computational steps, interactive memory manipulation and feedback by compiling back to a higher language. This work presents an abstract machine designed primarily for educational purposes, enabling the visualization and interaction with the compilation process of a simple imperative language.","PeriodicalId":516644,"journal":{"name":"IPSI Transactions on Internet Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141689037","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：