In this paper, an approach referred to as IoTsecM is proposed. This proposal is a UML/SysML extension for security requirements modeling within the analysis stage in a waterfall development life cycle in a Model-Based Systems Engineering Approach. IoTsecM allows the security requirements representation in two very well-known modeling languages, UML and SysML. With the utilization of this extension, IoT developers can consider the security requirements from the analysis stage in the design process of IoT systems. IoTsecM allows IoT systems to be designed considering possible threats and the corresponding security requirements analysis. The applicability of IoTsecM is demonstrated through applying it to analyze and represent the security requirements in an IoT real-life system in the context of collaborative autonomous vehicles in smart cities. In this use case, IoTsecM was able to represent the security requirements identified within the system architecture elements, in which all countermeasures identified were depicted using the proposed IoTsecM profile.

In blockchain-based systems the privacy use to be a concern and the key management is not widely revisited. In this work we present a threshold blind signature scheme designed on the modified version of the Paillier cryptosystem presented by Yi et al. Our scheme guarantees four main characteristics of interest: Independence meaning that our scheme could be applied to any blockchain-based transaction system. Secure key storage which improves the key management security under the idea that one single key is always an easier target for an attack or a failure, in such way that our threshold scheme recovers the signature instead of the private key, therefore nobody can access to the private key. Anonymity, which is one of the best-evaluated features on online transactions; and independence from mixers or trusted third parties, which most of the time they are used to reach anonymity but there are inherent risks to take in count about having a transaction in a third party. We achieve such a four characteristics without increase costs in comparison with related work.

While data scarcity is often cited as a major challenge in machine learning, the opposite problem—having too much data or an unlimited stream of incoming data—is equally relevant. In such cases, a key question arises: which examples are the most useful for training a model while avoiding excessive computational cost? This work addresses the problem by proposing a strategy to automatically select useful examples while preserving model effectiveness and training efficiency. We propose and evaluate a new strategy—Adding Only Errors (AOE)—to avoid using the full training set for classifier training. Our results show that when the training-validation split is properly sampled, the size of the training set can be reduced by more than three orders of magnitude without compromising performance. Although this reduction may initially incur higher computational costs, in real-world scenarios involving continuous streams of labeled data requiring frequent model retraining, AOE amortizes this cost by significantly decreasing dataset size.

During a network scanning, identifying the operating system (OS) running on each network attached host has been a research topic for a long time. Researchers have developed different approaches through network analysis using either passive or active techniques, such techniques are commonly called “OS fingerprinting”. According to best security practices, a set of security mechanisms should be applied to prevent OS fingerprinting by penetration testers. This paper proposes a strategy to identify obfuscation network devices during a black-box security assessment, using machine learning algorithms to offer a near approximation to the target architecture.

Since intercepted sensor data can reveal user activities, lightweight cryptography is essential for protecting information in resource-constrained devices. Although ARX-based lightweight schemes offer high computational efficiency, their lack of inherent non-linear substitution and permutation layers motivates their integration into secure cryptographic designs. This paper proposes LSEPI (Lightweight Sensor Encryption Using PI), a lightweight stream cipher for protecting electrical sensor data. LSEPI employs a single-round substitution-permutation design that combines the AES substitution box with a key-dependent dynamic permutation and a dynamically selected XOR mask derived from the secret key and the digits of the number $\pi$. To mitigate the computational cost of pseudorandom sequence generation, the first 20 KB of the decimal expansion of $\pi$ are stored on both the ESP32 and Android device. Encryption is performed on the ESP32 during the simultaneous acquisition and computation of voltage, current, power, and energy, while decryption is performed on an Android application for user-selected time intervals. Statistical evaluation shows that LSEPI achieves an entropy of 7.9971 with a single encryption round, comparable to Speck-CTR with 32 rounds (7.9970) and Ascon (7.9969), and higher than the approximately 7.18 reported for recent lightweight proposals. The encrypted data also satisfies the chi-square goodness-of-fit criterion, exhibits correlation coefficients close to zero, and achieves average avalanche effects of 50.005% for one-bit plaintext changes and 50.08% for one-bit key changes. GLCM-based texture metrics are comparable to those of Speck-CTR and Ascon. The known-plaintext analysis indicates that recovering the key-dependent masking sequence requires determining or inverting the key-dependent permutation; thus, despite the use of XOR operations, the attack does not directly succeed. From a computational perspective, LSEPI requires 135 μs per sensor record after initialization, adding only 0.04% overhead to the computation of the electrical variables on the ESP32.