Internet of Things (IoT) is a paradigm shifting technology that enables many innovative applications in the near future. Proactive measures are required to protect such architecture from cyber attacks. One of the most important security issues in this architecture is the authentication of edge nodes, which can be resolved through the deployment of digital signatures. However, existing standardized digital signatures are vulnerable to attacks from quantum computers, which can be unsafe in the near future. In this article, we propose a new signature scheme based on multivariate polynomials with efficient key and signature sizes, which is resistant to quantum computer attacks. The proposed scheme is also very friendly to parallel implementation, enabling efficient deployment of edge nodes authentication at high throughput. When implemented on a GPU device, the proposed scheme can generate 113 signatures/s and verify 120 signatures/s, which is 12.56× and 10.00× faster than a serial implementation in CPU.

The proliferation of unsolicited short messages (SMS spam) poses persistent challenges to mobile communication security and user privacy. This study presents a systematic benchmarking and analytical investigation of classical machine learning approaches for SMS spam detection, focusing on the impact of text feature representation under imbalanced short-text conditions.In practical SMS filtering systems, minimizing false positives (i.e., incorrectly blocking legitimate messages) is a critical operational constraint. Therefore, beyond overall accuracy, precision and specificity are emphasized to ensure reliable preservation of legitimate communication. Using the SMSSpamCollection dataset (5574 messages: 747 spam and 4827 ham), seven feature representation techniques were evaluated in combination with six widely adopted classifiers, resulting in 42 configurations assessed under 10-fold cross-validation. The results demonstrate that feature representation plays a more critical role than classifier complexity. Character-level 3-grams combined with Logistic Regression achieved the best overall performance, reaching 98.55% accuracy, with 98.55% precision and 90.50% recall for the spam class (F1-score = 94.32%), and 0.9893 AUC. Linear SVM produced comparable results, highlighting the effectiveness of linear models when paired with expressive representations. Beyond reporting performance metrics, this study analyzes feature–classifier interaction patterns and clarifies practical trade-offs between precision, recall, and computational efficiency. The findings provide reproducible baselines and structured guidance for designing efficient SMS spam filtering systems.

Identification schemes based on multivariate polynomials have been receiving attraction in different areas due to the quantum secure property. Identification is one of the most important elements for the IoT to achieve communication between objects, gather and share information with each other. Thus, identification schemes which are post-quantum secure are significant for Internet-of-Things (IoT) devices. Various polar forms of multivariate quadratic and cubic polynomial systems have been proposed for these identification schemes. There is a need to define polar form for multivariate dth degree polynomials, where $d\ge 4$ . In this paper, we propose a solution to this need by defining constructions for multivariate polynomials of degree $d\ge 4$ . We give a generic framework to construct the identification scheme for IoT and RFID applications. In addition, we compare identification schemes and curve-based cryptoGPS which is currently used in RFID applications.