TL;DR: An overview of convolutional neural network-based static malware analysis techniques acknowledges the recent trend of conceptualizing malware as a sequential structure with both local and long-term dependencies, the need to reconsider the notion of dataset balance, and the need for consistent and transparent application of the F1-score.
: This paper provides an overview of convolutional neural network-based static malware analysis techniques. Three research questions are considered: Which architectures based on or related to CNNs are used in static malware analysis? Which datasets are used to support research in this field, and what are the associated challenges? To what extent are the obtained models evaluated? Three scientific databases (Scopus, Web of Science, and MDPI) are searched, and the PRISMA framework is used to conduct and transparently present 70 papers selected according to dedicated inclusion and exclusion criteria. The overview recognizes the recent trend of conceptualizing malware as a sequential structure with both local and long-term dependencies, the need to reconsider the notion of dataset balance, and the need for consistent and transparent application of the F1-score.
TL;DR: This study provides a thorough examination of parametric and generative design processes for 3D printing applications, evaluating their techniques, industrial uses, benefits, problems and future potential.
: The integration of parametric and generative design approaches into cloud-based computer-aided design (CAD) and computer-aided manufacturing (CAM) platforms is transforming contemporary product development, especially in 3D printing applications. Parametric design prioritizes c…
TL;DR: A comparison of dataset versions with and without the entropy feature showed that the proposed entropy calculation method improves classification performance, even though the number of features was reduced compared to the original dataset.
: In machine learning and classification, entropy holds significant potential. This paper introduces a method to calculate Shannon entropy across all features within individual records in four IDS datasets: CSE-CIC-IDS2018, CIC-IDS2017, UNSW-NB15, and LUFlow. Each dataset is resh…
TL;DR: A Reliable Resource Placement with Migration Function (MF) method to reduce the outage in SC communications is proposed and reduces outage time by 13.79%, network overload by 14.04% and improves the response ratio by 13.41% for the maximum network load.
: Smart City (SC) development with technological aspects depends on wireless communication and intelligent networks such as the Internet of Things (IoT). Wireless networks and IoT interconnect resources and projects them to be ubiquitous for various applications and user services…
: This paper presents the design of a compact tri-band monopole antenna for wireless applications operating below 8 GHz, employing split-ring resonators (SRRs) to enhance performance. The antenna is realized in two phases, resulting in an offset-fed monopole structure with strate…
TL;DR: A conditional generative adversarial networks method that integrates the machine learning with the deep learning to detect the hardware Trojans injected in Register-Transfer Level code and it contributes to enhancing the security and trustworthiness of ICs against hardware Trojan attacks.
: Hardware Trojan (HT) can compromise the security of a system by changing the integrated circuit (IC) functionality and reducing the system ꞌ s reliability. To handle this issue, machine learning has been widely used to analyze the datasets extracted from circuits to detect hard…
TL;DR: An Adaptive Binary Genetic Algorithm (A-BGA) is developed that introduces population-diversity-driven dynamic crossover and mutation rates, and reformulates the fitness as a bi-objective trade-off between prediction RMSE and feature cardinality to form a comprehensive framework that enhances prediction efficiency and uncertainty modeling.
: As the share of renewable energy in power systems continues to grow, improving prediction accuracy has become critical for enhancing system flexibility and reducing operational costs. In this paper, we propose two novel optimization methods tailored for renewable energy predict…