Public-key cryptography is too slow for general purpose encryption, with most applications limiting its use as much as possible. Some secure protocols, especially those that enable forward secrecy, make a much heavier use of public-key cryptography, increasing the demand for lightweight cryptosystems that can be implemented in low powered or mobile devices. This performance requirements are even more significant in critical infrastructure and emergency scenarios where peer-to-peer networks are deployed for increased availability and resiliency. We benchmark several public-key key-exchange algorithms, determining those that are better for the requirements of critical infrastructure and emergency applications and propose a security framework based on these algorithms and study its application to decentralized node or sensor networks.

Smart-locks have become increasingly popular for access to homes and businesses in many countries, because of their ease of use and adaptability. These locks offer a simple and secure alternative to traditional key-based entry, making them an attractive choice for both residential and commercial properties. Nevertheless, it is essential to acknowledge the potential security threats that come with any new technology. The security of smart-locks is particularly critical, as a breach could result in unauthorized entry. Since the smart-locks can connect, there are different ways to check if vulnerabilities can be found easily or on the contrary, if the security level is high. Two of the main ways of checking the security level of this kind of IoT device are the information that can be obtained from the Android application and the security level of the Bluetooth connection. Many vulnerabilities can be found in the Android smart lock management application. This application is very useful to perform all the configurations with such a lock, but if it is not properly implemented and secured, it can provide clues for malicious users to perform unauthorized access to the system. Another security factor is the Bluetooth connection. This ensures that only authorized users have access to the property. In this work, we have analyzed the security level of different parts of smart-locks. In particular, we have analyzed the security of the applications for the most important smart-locks on the market. This study reveals relevant information such as whether the application is obfuscated or not, the encryption algorithm for the Bluetooth connection, or relevant URLs that applications use to connect to the cloud. The security of the Bluetooth connection between the smartphone application and two selected smart-locks was also analyzed. It was demonstrated that if no encryption is used for the Bluetooth connection, the smart-lock is not secure, but if AES encryption is used, the security level is high.

Cybercriminals are constantly developing new techniques to circumvent the security measures implemented by experts and researchers, so malware is able to evolve very rapidly. In addition, detecting malware across multiple systems is a challenging problem because each computing environment has its own unique characteristics. Traditional techniques, such as signature-based malware detection, have been largely replaced by more modern approaches, such as machine learning and robust cross-platform behavior-based threat detection, as they have become less effective. Researchers employ these techniques across a variety of data sources, including network traffic, binaries, and behavioral data, to extract relevant features and feed them to models for accurate prediction. The aim of this research is to provide a novel dataset comprised of a substantial number of high-quality samples based on software behavior. Due to the lack of a standard representational format for malware behavior in current research, we also present an innovative method for representing malware behavior by converting API calls into 2D images, which builds on previous work. Additionally, we propose and describe the implementation of a new machine learning model based on binary classification (malware or benign software) using the previously mentioned novel dataset as its data source, thereby establishing an evaluation baseline. We have conducted extensive experimentation, validating the proposed model with both our novel dataset and real-world data. In terms of metrics, our proposed model outperforms a well-known model that is also based on behavior analysis and has a similar architecture.

Pseudo random and true random sequence generators are important components in many scientific and technical fields, playing a fundamental role in the application of the Monte Carlo methods and stochastic simulation. Unfortunately, the quality of the sequences produced by these generators are not always ideal in terms of randomness for many applications. We present a new nonlinear filter design that improves the output sequences of common pseudo random generators in terms of statistical randomness. Taking inspiration from techniques employed in symmetric ciphers, it is based on four seed-dependent substitution boxes, an evolving internal state register, and the combination of different types of operations with the aim of diffusing nonrandom patterns in the input sequence. For statistical analysis we employ a custom initial battery of tests and well-regarded comprehensive packages such as TestU01 and PractRand. Analysis results show that our proposal achieves excellent randomness characteristics and can even transform nonrandom sources (such as a simple counter generator) into perfectly usable pseudo random sequences. Furthermore, performance is excellent while storage consumption is moderate, enabling its implementation in embedded or low power computational platforms.