The spread of Covid-19 has resulted in worldwide health concerns. Social media is increasingly used to share news and opinions about it. A realistic assessment of the situation is necessary to utilize resources optimally and appropriately. In this research, we perform Covid-19 tweets sentiment analysis using a supervised machine learning approach. Identification of Covid-19 sentiments from tweets would allow informed decisions for better handling the current pandemic situation. The used dataset is extracted from Twitter using IDs as provided by the IEEE data port. Tweets are extracted by an in-house built crawler that uses the Tweepy library. The dataset is cleaned using the preprocessing techniques and sentiments are extracted using the TextBlob library. The contribution of this work is the performance evaluation of various machine learning classifiers using our proposed feature set. This set is formed by concatenating the bag-of-words and the term frequency-inverse document frequency. Tweets are classified as positive, neutral, or negative. Performance of classifiers is evaluated on the accuracy, precision, recall, and F1 score. For completeness, further investigation is made on the dataset using the Long Short-Term Memory (LSTM) architecture of the deep learning model. The results show that Extra Trees Classifiers outperform all other models by achieving a 0.93 accuracy score using our proposed concatenated features set. The LSTM achieves low accuracy as compared to machine learning classifiers. To demonstrate the effectiveness of our proposed feature set, the results are compared with the Vader sentiment analysis technique based on the GloVe feature extraction approach.

User reviews on social networking platforms like Twitter, Facebook, and Google+, etc. have been gaining growing interest on account of their wide usage in sentiment analysis which serves as the feedback to both public and private companies, as well as, governments. The analysis of such reviews not only plays a noteworthy role to improve the quality of such services and products but helps to devise marketing and financial strategies to increase the profit for companies and customer satisfaction. Although many analysis models have been proposed, yet, there is still room for improving the processing, classification, and analysis of user reviews which can assist managers to interpret customers feedback and elevate the quality of products. This study first evaluates the performance of a few machine learning models which are among the most widely used models and then presents a voting classifier Gradient Boosted Support Vector Machine (GBSVM) which is constituted of gradient boosting and support vector machines. The proposed model has been evaluated on two different datasets with term frequency and three variants of term frequency-inverse document frequency including uni-, bi-, and tri-gram as features. The performance is compared with other state-of-the-art techniques which prove that GBSVM outperforms these models.

The field of pervasive computing especially the Internet of Things (IoT) network is evolving due to high network speed and increased capacity offered by the 5G communication system. The IoT network identifies each device before giving it access to the network. The RFID system is one of the most prominent enabling technologies for the node identification. Since the communication between the node and the network takes place over an insecure wireless channel, an authentication mechanism is required to avoid the malicious devices from entering the network. This paper presents a brief survey on the authentication protocols along with the prominent cryptanalysis models for the EPC C1G2 RFID systems. A comparative analysis is provided to highlight the common weaknesses of the existing authentication algorithms and to emphasize on the lack of security standardization for the resource constraint IoT network perception layer. This paper is concluded by proposing an ultralightweight protocol that provides Extremely Good Privacy (EGP). The proposed EGP protocol avoids all the pitfalls highlighted by the cryptanalysis of the existing authentication protocols. The incorporation of the novel ultralightweight primitives, Per-XOR ( ) and Inverse Per-XOR ( −1 ), makes the protocol messages more robust and irreversible for all types of adversaries. A comprehensive security analysis illustrates that the proposed protocol proves to be highly resistive against all possible attack scenarios and ensures the security optimally.Radio Frequency Identification (RFID) system is emerging as an enabling technology for the node discovery due to the features such as high speed, long range, and nonline of sight scanning [4]. The RFID enabled IoT networks are being preferred in various surveillance, monitoring, and healthcare applications. Table 1 highlights some of the prominent applications reported in the literature.The architecture of the RFID enabled IoT network is composed of three components: the RFID system, the IoT middleware, and the Internet [15]. The RFID system facilitates the node identification and the data collection. The data gathered from the environment under observation is processed by the IoT middleware. The IoT middleware also acts as a gateway to the external Internet [16].The architecture of the RFID system embedded in an IoT network consists of three main components; the Electronic Product Code (EPC) tag, the reader, and the database. The tag is a low-cost electronic chip with the unique identification number ( ). The reader identifies each tag associated with

Internet of Things is one of the most important components of modern technological systems. It allows the real time synchronization and connectivity of devices with each other and with the rest of the world. The radio frequency identification system is used as node identification mechanism in the Internet of Thing networks. Since Internet of Things involve wireless channel for communication that is open for all types of malicious adversaries, therefore many security protocols have been proposed to ensure encryption over wireless channel. To reduce the overall cost of radio frequency identification enabled Internet of Thing network security, the researchers use simple bitwise logical operations such as XOR, AND, OR, and Rot and have proposed many ultralightweight mutual authentication protocols. However, almost all the previously proposed protocols were later found to be vulnerable against several attack models. Recently, a new ultralightweight mutual authentication protocol has been proposed which involves only XOR and Rotation functions in its design and claimed to be robust against all possible attack models. In this article, we have performed cryptanalysis of this recently proposed ultralightweight mutual authentication protocol and found many pitfalls and vulnerabilities in the protocol design. We have exploited weak structure of the protocol messages and proposed three attacks against the said protocol: one desynchronization and two full disclosure attacks.

Internet of Things (IoTs) are becoming one of the integral parts of our lives, as all of the modern devices including pervasive systems use internet for its connectivity with the rest of the world. The Radio Frequency IDentification (RFID) provides unique identification and nonline of sight capabilities, therefore plays a very important role in development of IoTs. However, the RFID systems incorporate wireless channel for communication, therefore have some allied risks to the system from threat agents. In order to prevent the system from malicious activities in a cost effective way, numerous Ultralightweight Mutual Authentication Protocols (UMAPs) have been proposed since last decade. These UMAPs mainly involve simple bitwise logical operators such as XOR, AND, OR, etc., in their designs and can be implemented with extremely low cost RFID tags. However, most of the UMAP designers didn’t provide the proper hardware approximations of their UMAPs and presented only theoretical results which mostly mislead the reader. In this paper, we have addressed this problem by reporting our experiences with FPGA and ASIC-based implementation of UMAP named psuedo Kasami code-based Mutual Authentication Protocol (KMAP[Formula: see text]. Further, we have also improved the structure of the KMAP protocol to overcome the previously highlighted attack model. The hardware implementation results show that KMAP[Formula: see text] successfully conform to EPC-C1G2 tags and can be implemented using less than 4[Formula: see text]K GE (for 32-bit word length).