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HVAC Technology Trends to Watch in 2024

HVAC contractors who are prepared to embrace these advances will realize more opportunities to meet their customers needs and ultimately become more successful in an increasingly competitive field. ....

Ductless Solutions , New Year , Iot Market , Internet Of Things Iot , Technology And Hvacr , Trends In Hvacr , Geothermal Market , Vrf Systems Market ,

"A Cost-Sensitive Machine Learning Model With Multitask Learning for In" by Akbar Telikani, Nima Esmi Rudbardeh et al.

A problem with machine learning (ML) techniques for detecting intrusions in the Internet of Things (IoT) is that they are ineffective in the detection of low-frequency intrusions. In addition, as ML models are trained using specific attack categories, they cannot recognize unknown attacks. This article integrates strategies of cost-sensitive learning and multitask learning into a hybrid ML model to address these two challenges. The hybrid model consists of an autoencoder for feature extraction and a support vector machine (SVM) for detecting intrusions. In the cost-sensitive learning phase for the class imbalance problem, the hinge loss layer is enhanced to make a classifier strong against low-distributed intrusions. Moreover, to detect unknown attacks, we formulate the SVM as a multitask problem. Experiments on the UNSW-NB15 and BoT-IoT datasets demonstrate the superiority of our model in terms of recall, precision, and F1-score averagely 92.2%, 96.2%, and 94.3%, respectively, over ot ....

Eep Learning Dl , Nternet Of Things , Internet Of Things Iot , Intrusion Detection , Mathematical Models , Ultitask Learning , Upport Vector Machine Svm , Support Vector Machines , Task Analysis ,

"Enhancing the Security and Privacy in the IoT Supply Chain Using Block" by Linkai Zhu, Shanwen Hu et al.

Federated learning has emerged as a promising technique for the Internet of Things (IoT) in various domains, including supply chain management. It enables IoT devices to collaboratively learn without exposing their raw data, ensuring data privacy. However, federated learning faces the threats of local data tampering and upload process attacks. This paper proposes an innovative framework that leverages Trusted Execution Environment (TEE) and blockchain technology to address the data security and privacy challenges in federated learning for IoT supply chain management. Our framework achieves the security of local data computation and the tampering resistance of data update uploads using TEE and the blockchain. We adopt Intel Software Guard Extensions (SGXs) as the specific implementation of TEE, which can guarantee the secure execution of local models on SGX-enabled processors. We also use consortium blockchain technology to build a verification network and consensus mechanism, ensuring ....

Intel Software Guard Extensions Sgxs , Trusted Execution Environment , Intel Software Guard Extensions , Federated Learning , Internet Of Things Iot , Supply Chain , Rusted Execution Environment Tee ,

IoT malware attacks up by 400 per cent this year: Report

Malware attacks against Internet of Things (IoT) and Operational Technology (OT) devices have increased by 400 per cent this year ....

New Delhi , Head Of Security Research , Operational Technology , Deepen Desai , Security Research , Internet Of Things Iot , Malware Attack , Operational Technology Ot , Technology News ,

"Adoption of big data analytics for energy pipeline condition assessmen" by Muhammad Hussain, Tieling Zhang et al.

Due to complexity, the oil and gas industry use various sensors to collect data for analysis to maintain the safety and integrity of pipelines and associated infrastructures. There is an enormous amount of data available to conceal crucial information, including precursor data on failure modes and knowledge that may be analyzed. The availability of large amounts of data has enabled the development of analytical tools that integrate methods like predictive analytics using different decision-making models, artificial intelligence (AI), and machine learning. These tools are crucial for managing pipeline conditions, preventing unwarranted failures, enhancing asset performance, availability, and decision-making. Big data analytics enables energy companies to implement a proactive approach to pipeline condition assessment. By integrating real-time data from sensors embedded in pipelines, weather conditions, and maintenance records, it becomes possible to detect potential issues and predict a ....

Artificial Intelligence Ai , Asset Management , Big Data , Energy Pipeline , Ndustry 4 0 , Internet Of Things Iot , Machine Learning Ml , Oil And Gas , Pipeline Condition Assessment , Pipeline Integrity Management , Predictive Analytics ,