The Internet of Things (IoT) is a new model that connects physical objects and the Internet and has become one of the most important technological developments in computing. It is estimated that by 2022, one trillion physical objects will be connected to the Internet. The poor accessibility and lack of interoperability of many of these devices in a vast heterogeneous landscape make it difficult to design specific security measures and implement specific defences mechanism in addition, IoT networks are still open and vulnerable to network disruption attacks. Therefore, there is a need for additional security tools related to IoT. Intrusion Detection System could serve this purpose. Intrusion detection is the process of monitoring and analyzing network traffic in order to detect potential security breaches and unauthorized access to a IOT network. It involves the use of various technologies and techniques to identify and respond to potential threats in real-time. Network intrusion detection helps organizations protect their valuable assets, including sensitive data, intellectual property, and financial resources, from cyberattacks. By detecting and responding to potential security breaches in a timely manner, network intrusion detection systems can help organizations prevent or mitigate the impact of security incidents, minimize downtime and financial losses, and maintain the integrity of their operations and reputation. Weighted soft voting is a technique used in network intrusion detection to improve the accuracy and reliability of the detection process. It involves combining the results of multiple intrusion detection systems (IDS) based on decision tree, random forest and XGBoost using a weighted approach that assigns different levels of importance to each system based on its performance and reliability. The basic idea behind weighted soft voting is to give more weight to the predictions of IDS that have higher accuracy and lower false positive rates, and less weight to those that have lower accuracy and higher false positive rates. The proposed approach can help reduce the impact of false alarms and increase the sensitivity and specificity of the intrusion detection process.
{"title":"Network intrusion detection using ensemble weighted voting classifier based honeypot framework","authors":"Parvathi Pothumani, Sreenivasa Reddy","doi":"10.32629/jai.v7i3.1081","DOIUrl":"https://doi.org/10.32629/jai.v7i3.1081","url":null,"abstract":"The Internet of Things (IoT) is a new model that connects physical objects and the Internet and has become one of the most important technological developments in computing. It is estimated that by 2022, one trillion physical objects will be connected to the Internet. The poor accessibility and lack of interoperability of many of these devices in a vast heterogeneous landscape make it difficult to design specific security measures and implement specific defences mechanism in addition, IoT networks are still open and vulnerable to network disruption attacks. Therefore, there is a need for additional security tools related to IoT. Intrusion Detection System could serve this purpose. Intrusion detection is the process of monitoring and analyzing network traffic in order to detect potential security breaches and unauthorized access to a IOT network. It involves the use of various technologies and techniques to identify and respond to potential threats in real-time. Network intrusion detection helps organizations protect their valuable assets, including sensitive data, intellectual property, and financial resources, from cyberattacks. By detecting and responding to potential security breaches in a timely manner, network intrusion detection systems can help organizations prevent or mitigate the impact of security incidents, minimize downtime and financial losses, and maintain the integrity of their operations and reputation. Weighted soft voting is a technique used in network intrusion detection to improve the accuracy and reliability of the detection process. It involves combining the results of multiple intrusion detection systems (IDS) based on decision tree, random forest and XGBoost using a weighted approach that assigns different levels of importance to each system based on its performance and reliability. The basic idea behind weighted soft voting is to give more weight to the predictions of IDS that have higher accuracy and lower false positive rates, and less weight to those that have lower accuracy and higher false positive rates. The proposed approach can help reduce the impact of false alarms and increase the sensitivity and specificity of the intrusion detection process.","PeriodicalId":508223,"journal":{"name":"Journal of Autonomous Intelligence","volume":"11 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139445847","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}
Autonomous vehicles have been a recent trend and active research area from the onset of machine learning and deep learning algorithms. Computer vision and deep learning techniques have simplified the operations of continuous monitoring and decision-making capabilities of autonomous vehicles. A navigation system is facilitated by a visual system, where sensors and collectors process input in form of images or videos, and the navigation system will be making certain decisions to adhere to the safety of drivers and passers-by. This research article contemplates the model of obstacle detection, lane detection, and how the vehicle is supposed to act in terms of autonomous driving situation. This situation should resemble human driving conditions and should ensure maximum safety to both the stakeholders. A unified neural network for detecting lanes, objects, obstacles and to advise the driving speed is defined in this architecture. As far as autonomous driving is considered, these target elements are considered to be the predominant areas of focus for autonomous driving vehicles. Since capturing the images or videos have to be performed in real-time scenarios and processing them for relevant decision making have to be completed at a swift pace, a concept of context tensors is introduced in the decoders for discriminating the tasks based on priority. Every task is associated with the other tasks and also the decision-making process and hence this architecture will continue to learn every day. From the obtained results, it is evident that multitask networks can be improved using the proposed method in terms of accuracy, decision-making capability and reduced computational time. This model investigates the performance using Berkeley deep drive datasets which are considered to be a challenging dataset.
{"title":"Detection of lanes, obstacles and drivable areas for self-driving cars using multifusion perception metrics","authors":"A. Kishore Kumar, Venkatesh Palanisamy","doi":"10.32629/jai.v7i3.1059","DOIUrl":"https://doi.org/10.32629/jai.v7i3.1059","url":null,"abstract":"Autonomous vehicles have been a recent trend and active research area from the onset of machine learning and deep learning algorithms. Computer vision and deep learning techniques have simplified the operations of continuous monitoring and decision-making capabilities of autonomous vehicles. A navigation system is facilitated by a visual system, where sensors and collectors process input in form of images or videos, and the navigation system will be making certain decisions to adhere to the safety of drivers and passers-by. This research article contemplates the model of obstacle detection, lane detection, and how the vehicle is supposed to act in terms of autonomous driving situation. This situation should resemble human driving conditions and should ensure maximum safety to both the stakeholders. A unified neural network for detecting lanes, objects, obstacles and to advise the driving speed is defined in this architecture. As far as autonomous driving is considered, these target elements are considered to be the predominant areas of focus for autonomous driving vehicles. Since capturing the images or videos have to be performed in real-time scenarios and processing them for relevant decision making have to be completed at a swift pace, a concept of context tensors is introduced in the decoders for discriminating the tasks based on priority. Every task is associated with the other tasks and also the decision-making process and hence this architecture will continue to learn every day. From the obtained results, it is evident that multitask networks can be improved using the proposed method in terms of accuracy, decision-making capability and reduced computational time. This model investigates the performance using Berkeley deep drive datasets which are considered to be a challenging dataset.","PeriodicalId":508223,"journal":{"name":"Journal of Autonomous Intelligence","volume":"40 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139446555","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}
In the metaverse environment, establish an immersive human-computer interaction system for Chinese traditional music based on virtual reality technology. Design the system’s functionality according to the Y model, and construct a four-layered system architecture. Collect high-quality instructional audio and utilize polygon modeling technology to create contextualized scenes of Chinese traditional music, as well as high-fidelity models of characters and instruments. Implement motion capture through inertial sensor technology for performance action data mapping. Utilize a metaverse engine platform to realize interactive functions and conduct performance optimization. The system is capable of eliciting learners’ intrinsic experiences, enabling interactive self-directed learning and creative exploration of Chinese traditional music performance, demonstrating significant practical value.
在元宇宙环境中，建立基于虚拟现实技术的中国传统音乐沉浸式人机交互系统。根据 Y 模型设计系统功能，构建四层系统架构。收集高质量的教学音频，利用多边形建模技术创建中国传统音乐的情境化场景，以及高保真的人物和乐器模型。通过惯性传感器技术实现动作捕捉，绘制表演动作数据图。利用元数据引擎平台实现交互功能，并进行性能优化。该系统能够激发学习者的内在体验，实现交互式自主学习和对中国传统音乐表演的创造性探索，具有重要的实用价值。
{"title":"Research on the visualization of information of Chinese traditional music with human-computer interaction from the perspective of metaverse","authors":"Yujing Cao, Jinwan Park","doi":"10.32629/jai.v7i3.1361","DOIUrl":"https://doi.org/10.32629/jai.v7i3.1361","url":null,"abstract":"In the metaverse environment, establish an immersive human-computer interaction system for Chinese traditional music based on virtual reality technology. Design the system’s functionality according to the Y model, and construct a four-layered system architecture. Collect high-quality instructional audio and utilize polygon modeling technology to create contextualized scenes of Chinese traditional music, as well as high-fidelity models of characters and instruments. Implement motion capture through inertial sensor technology for performance action data mapping. Utilize a metaverse engine platform to realize interactive functions and conduct performance optimization. The system is capable of eliciting learners’ intrinsic experiences, enabling interactive self-directed learning and creative exploration of Chinese traditional music performance, demonstrating significant practical value.","PeriodicalId":508223,"journal":{"name":"Journal of Autonomous Intelligence","volume":"52 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139451926","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}
R. J. Anandhi, V. S. A. Devi, B. S. K. Devi, Balasubramanian Prabhu kavin, Gan Hong Seng
Online hate speech has flourished on social networking sites due to the widespread availability of mobile computers and other Web knowledge. Extensive research has shown that online exposure to hate speech has real-world effects on marginalized communities. Research into methods of automatically identifying hate speech has garnered significant attention. Hate speech can affect any demographic, while some populations are more vulnerable than others. Relying solely on progressive learning is insufficient for achieving the goal of automatic hate speech identification. It need access to large amounts of labelled data to train a model. Inaccurate statistics on hate speech and preconceived notions have been the biggest obstacles in the field of hate speech research for a long time. This research provides a novel strategy for meeting these needs by combining a transfer-learning attitude-based BERT (Bidirectional Encoder Representations from Transformers) with a coral reef optimization-based approach (CROA). A feature selection (FC) optimization strategy for coral reefs, a coral reefs optimization method mimics coral behaviours for reef location and development. We might think of each potential answer to the problem as a coral trying to establish itself in the reefs. The results are refined at each stage by applying specialized operators from the coral reefs optimization algorithm. When everything is said and done, the optimal solution is chosen. We also use a cutting-edge fine-tuning method based on transfer learning to assess BERT’s ability to recognize hostile contexts in social media communications. The paper evaluates the proposed approach using Twitter datasets tagged for racist, sexist, homophobic, or otherwise offensive content. The numbers show that our strategy achieves 5%–10% higher precision and recall compared to other approaches.
{"title":"CROA-based feature selection with BERT model for detecting the offensive speech in Twitter data","authors":"R. J. Anandhi, V. S. A. Devi, B. S. K. Devi, Balasubramanian Prabhu kavin, Gan Hong Seng","doi":"10.32629/jai.v7i3.1122","DOIUrl":"https://doi.org/10.32629/jai.v7i3.1122","url":null,"abstract":"Online hate speech has flourished on social networking sites due to the widespread availability of mobile computers and other Web knowledge. Extensive research has shown that online exposure to hate speech has real-world effects on marginalized communities. Research into methods of automatically identifying hate speech has garnered significant attention. Hate speech can affect any demographic, while some populations are more vulnerable than others. Relying solely on progressive learning is insufficient for achieving the goal of automatic hate speech identification. It need access to large amounts of labelled data to train a model. Inaccurate statistics on hate speech and preconceived notions have been the biggest obstacles in the field of hate speech research for a long time. This research provides a novel strategy for meeting these needs by combining a transfer-learning attitude-based BERT (Bidirectional Encoder Representations from Transformers) with a coral reef optimization-based approach (CROA). A feature selection (FC) optimization strategy for coral reefs, a coral reefs optimization method mimics coral behaviours for reef location and development. We might think of each potential answer to the problem as a coral trying to establish itself in the reefs. The results are refined at each stage by applying specialized operators from the coral reefs optimization algorithm. When everything is said and done, the optimal solution is chosen. We also use a cutting-edge fine-tuning method based on transfer learning to assess BERT’s ability to recognize hostile contexts in social media communications. The paper evaluates the proposed approach using Twitter datasets tagged for racist, sexist, homophobic, or otherwise offensive content. The numbers show that our strategy achieves 5%–10% higher precision and recall compared to other approaches.","PeriodicalId":508223,"journal":{"name":"Journal of Autonomous Intelligence","volume":"16 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139451453","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}
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