In recent years, the botnets have been the most common threats to network security since it exploits multiple malicious codes like a worm, Trojans, Rootkit, etc. The botnets have been used to carry phishing links, to perform attacks and provide malicious services on the internet. It is challenging to identify Peer-to-peer (P2P) botnets as compared to Internet Relay Chat (IRC), Hypertext Transfer Protocol (HTTP) and other types of botnets because P2P traffic has typical features of the centralization and distribution. To resolve the issues of P2P botnet identification, we propose an effective multi-layer traffic classification method by applying machine learning classifiers on features of network traffic. Our work presents a framework based on decision trees which effectively detects P2P botnets. A decision tree algorithm is applied for feature selection to extract the most relevant features and ignore the irrelevant features. At the first layer, we filter non-P2P packets to reduce the amount of network traffic through well-known ports, Domain Name System (DNS). query, and flow counting. The second layer further characterized the captured network traffic into non-P2P and P2P. At the third layer of our model, we reduced the features which may marginally affect the classification. At the final layer, we successfully detected P2P botnets using decision tree Classifier by extracting network communication features. Furthermore, our experimental evaluations show the significance of the proposed method in P2P botnets detection and demonstrate an average accuracy of 98.7%.
The radio frequency identification (RFID) has emerged Internet of things (IoT) into the identification of things. This paper presents, a low-cost smart refrigerator system for future internet of things applications. The proposed smart refrigerator is used for automatic billing and restoring of beverage metallic cans. The metallic cans can be restored by generating a product shortage alert message to a nearby retailer. To design a low-cost and lowprofile tag antenna for metallic items is very challenging, especially when mass production is required for item-level tagging. Therefore, a novel ultra-high frequency (UHF) radio frequency identification (RFID) tag antenna is designed for metallic cans by exploiting the metallic structure as the main radiator. Applying Characteristics mode analysis (CMA), we observed that some characteristic modes associated with the metallic structure could be exploited to radiate more effectively by placing a suitable inductive load. Moreover, a low cost, printed (using conductive ink) small loop integrated with meandered dipole used as an inductive load, which was also connected with RFID chip. The 3-dB bandwidth of the proposed tag covers the whole UHF band ranging from 860-960 MHz when embedded with metal cans. The measured read range of the RFID tag is more than 2.5 meters in all directions to check the robustness of the proposed solution. To prove the concept, a case study was performed by placing the tagged metallic cans inside a refrigerator for automatic billing, 97.5 % tags are read and billed successfully. This study paves the way for tagging metallic bodies for tracking applications in domains ranging from consumer devices to infotainment solutions, which enlights a vital aspect for the Internet of Things (IoT). Index Terms-Characteristic mode theory (CMT), Internet of Things (IoT), radio frequency identification (RFID) tag antenna, smart refrigerator I. INTRODUCTION he Internet of Things foresees a future in which physical and virtual things can be uniquely identified on a global scale by means of emerging integrated wireless technologies.
This paper presents a block-chain enabled inkjet-printed ultrahigh frequency radiofrequency identification (UHF RFID) system for the supply chain management, traceability and authentication of hard to tag bottled consumer products containing fluids such as water, oil, juice, and wine. In this context, we propose a novel low-cost, compact inkjet-printed UHF RFID tag antenna design for liquid bottles, with 2.5 m read range improvement over existing designs along with robust performance on different liquid bottle products. The tag antenna is based on a nested slot-based configuration that achieves good impedance matching around high permittivity surfaces. The tag was designed and optimized using the characteristic mode analysis. Moreover, the proposed RFID tag was commercially tested for tagging and billing of liquid bottle products in a conveyer belt and smart refrigerator for automatic billing applications. With the help of block-chain based product tracking and a mobile application, we demonstrate a real-time, secure and smart supply chain process in which items can be monitored using the proposed RFID technology. We believe the standalone system presented in this paper can be deployed to create smart contracts that benefit both the suppliers and consumers through the development of trust. Furthermore, the proposed system will paves the way towards authentic and contact-less delivery of food, drinks and medicine in recent Corona virus pandemic.
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