# Deep-Learning-Based Approach for IoT Attack and Malware Detection
###### Abstract  
The Internet of Things (IoT), introduced by [PERSON] in the late 1990s, has transformed technology usage globally, enhancing efficiency and convenience but also posing significant security challenges. With the proliferation of IoT devices expected to exceed 29 billion by 2030, securing these devices is crucial. This study proposes an optimized 1D convolutional neural network (1D CNN) model for effectively classifying IoT security data. The model architecture includes input, convolutional, self-attention, and output layers, utilizing GELU activation, dropout, and normalization techniques to improve performance and prevent overfitting. The model was evaluated using the CIC IoT 2023, CIC-MalMem-2022, and CIC-IDS2017 datasets, achieving impressive results: 98.36% accuracy, 100% precision, 99.96% recall, and 99.95% F1-score for CIC IoT 2023; 99.90% accuracy, 99.98% precision, 99.97% recall, and 99.96% F1-score for CIC-MalMem-2022; and 99.99% accuracy, 99.99% precision, 99.98% recall, and 99.98% F1-score for CIC-IDS2017. These outcomes demonstrate the model's effectiveness in detecting and classifying various IoT-related attacks and malware. The study highlights the potential of deep-learning techniques to enhance IoT security, with the developed model showing high performance and low computational overhead, making it suitable for real-time applications and resource-constrained devices. Future research should aim at testing the model on larger datasets and incorporating adaptive learning capabilities to further enhance its robustness. This research significantly contributes to IoT security by providing advanced insights into deploying deep-learning models, encouraging further exploration in this dynamic field. IoT security; 1 DCNN; deep learning; malware detection; network attack detection; CIC IoT 2023; CIC-MalMem-2022; CIC-IDS2017; low computational load; real-time applications +
Footnote †: journal: Medical  
reducing energy consumption. In the industrial sector, IIoT technologies make production lines more efficient, improve maintenance processes, and reduce operational costs. While IoT and IIoT systems provide these transformations, they also introduce serious security challenges [11]. Due to the limited processing capabilities and minimal security measures of IoT devices, they are highly vulnerable to attacks [12]. In recent years, attacks such as DDoS, MITM, and malware on IoT networks, have become increasingly widespread [13; 14]. Deep learning has emerged as a powerful solution for IoT security, owing to its capacity to learn complex attack patterns and process large datasets [15; 16]. For example, botnets controlled by cyber attackers can execute widespread DDoS attacks, causing extensive internet outages and crippling critical infrastructure systems [17]. Securing the IoT ecosystem is not just a technological necessity but a social and economic imperative. Security researchers, industry leaders, and policymakers are continually developing new strategies and solutions to enhance IoT system security, encouraging research in this area and establishing international collaborations [18]. Through extensive simulations and experiments, this study analyzes the attack vectors targeting IoT devices and networks and proposes effective methods to mitigate these threats [19]. The Internet of Things (IoT) environments are vulnerable to various cyber-attacks, often posing serious security risks. The primary attacks that IoT systems face include DoS/DDoS attacks, information gathering attacks, Man-in-the-Middle (MITM) attacks, injection attacks, and malware attacks [20]. DoS/DDoS attacks, which cause service interruptions by overloading network resources, are critically severe. Information-gathering attacks aim to obtain sensitive information about the system and are highly severe. MITM attacks, known for compromising the confidentiality and integrity of communication, are high-severity attacks.