[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118804-en":3,"doc-seo-118804-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118804,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Securing IoT Networks for Detection of Cyber Attacks using Automated Machine Learning","Cybercriminals continuously evolve tactics that undermine the availability, confidentiality, and integrity of digital systems, making ongoing defense essential. Machine learning enables proactive cyber analysis by learning recurring successful patterns, yet it faces practical limits: computational overhead and the need for specialized frameworks for broad deployment. This study numerically evaluates how a hub improves the security of a smart-house IoT ecosystem, validating the hub with typical cyberattacks and assessing IDS robustness against adversarial machine learning via adversarial samples.","Securing IoT Networks for Detection of Cyber Attacks using Automated Machine Learning  \nUrvashi Sangwan1, Dr. Rajender Singh Chhillar2  \n1Ph.D Scholar, Department of Computer Science and Applications  \nMaharshi Dayanand University, Rohtak-124001  \nHaryana, India  \n[usangwan@gmail.com](usangwan@gmail.com)  \n2Professor, Department of Computer Science and Applications  \nMaharshi Dayanand University, Rohtak-124001  \nHaryana, India  \n[chhillar02@gmail.com](chhillar02@gmail.com)  \nAbstract— Cybercriminals are always developing innovative strategies to confound and frustrate their victims. Therefore, maintaining constant vigilance is essential if one wishes to protect the availability, confidentiality, and integrity of digital systems. Machine learning (ML) is becoming an increasingly powerful technique for doing intelligent cyber analysis, which enables proactive defenses. Machine learning (ML) has the potential to thwart future assaults by studying the recurring patterns that have already been successful. Nevertheless, there are two significant drawbacks associated with the utilization of ML in security analysis. To begin, the most advanced machine learning systems have significant problems with their computing overheads. Because of this constraint, firms are unable to completely embrace ML-based cyber strategies. Second, in order for security analysts to make advantage of ML for a wide variety of applications, they will need to develop specialized frameworks. In this study, we aim to put a numerical value on the degree to which a hub can improve the safety of an ecosystem. Typical cyberattacks were carried out on an Internet of Things (IoT) network located within a smart house in order to validate the hub. Further investigation of the intrusion detection system's (IDS) resistance to adversarial machine learning (AML) assaults was carried out. In this method, models can be attacked by supplying adversarial samples that attempt to take advantage of the defects in the detector that are present in the pretrained model.  \nKeywords-Intrusion Detection Systems, Adversarial Machine, Internet of Things. Cyber Physical System  \nI. INTRODUCTION  \nA number of other terms, such as \"Internet of Things,\"\"Cyber Physical System,\" \"Ubiquitous computing,\" and\"Pervasive Computing,\" are frequently used to allude to the ongoing automation movement. There is one thing that all of these nouns have in common, and that is the fact that they describe a part of the automation of the system. In the field of automation, the implementation of Cyber Physical Systems (CPS) is quickly becoming the standard practice [1] . A CPStakes an existing physical system and transforms it into a computerized one by employing various pieces of hardware and software as well as a predetermined set of operating procedures. With the help of CPS, even the most basic instrument can function just like a sophisticated piece of technology. These electronic devices, in general, are not very useful due to the limited amount of data that they are able to process, the large amount of power that they require, and the limited amount of room that they have for storing data. A new generation of electronic systems is currently in the process of being developed. The integration of computational processes with physical systems is what this word alludes to.  \nComputational algorithms are a type of computer programmes that, when executed on a computer, can carry out a variety of functions. Computers that are talking with a network are controlling and monitoring a wide variety of distinct physical processes at the same time. As a consequence of this, it makes the creation of automated technologies that require a smaller number of operators much easier [2] . It lessens the likelihood of system failures being brought on by individual users. Some examples of smart technology include \"smart\" devices, \"smart\"buildings, and \"smart\" automobiles. With regard to CPS, the Internet of Things serves as the engi","cbCairH1MqxjzT4E","https://ap.wps.com/l/cbCairH1MqxjzT4E","pdf",483956,1,6,"English","en",105,"# Introduction\n## Internet of Things and Cyber Physical Systems\n## Security needs for CPS and critical infrastructure\n## Applications and smart systems context","[{\"question\":\"Why is protecting IoT and cyber physical systems critical?\",\"answer\":\"CPS and IoT devices are widely used across infrastructure and services, so cyber attacks can cause disruptions with economic, health, and data-security impacts, potentially even beyond borders.\"},{\"question\":\"What limitations restrict using machine learning in security analysis?\",\"answer\":\"Machine learning systems can have high computing overhead, preventing full adoption, and analysts often need specialized frameworks to apply ML effectively across many security use cases.\"},{\"question\":\"How does the study evaluate security and adversarial robustness?\",\"answer\":\"The work tests typical cyberattacks on an IoT network in a smart house to validate the hub, then examines IDS resistance to adversarial machine learning by attacking models with adversarial samples that exploit weaknesses in pretrained detectors.\"}]","Securing IoT Networks for Detection of Cyber Attacks using Automated Machine Learning | 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is protecting IoT and cyber physical systems critical?","Question",{"text":76,"@type":77},"CPS and IoT devices are widely used across infrastructure and services, so cyber attacks can cause disruptions with economic, health, and data-security impacts, potentially even beyond borders.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What limitations restrict using machine learning in security analysis?",{"text":81,"@type":77},"Machine learning systems can have high computing overhead, preventing full adoption, and analysts often need specialized frameworks to apply ML effectively across many security use cases.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the study evaluate security and adversarial robustness?",{"text":85,"@type":77},"The work tests typical cyberattacks on an IoT network in a smart house to validate the hub, then examines IDS resistance to adversarial machine learning by attacking models with adversarial samples that exploit weaknesses in pretrained 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