# Lightweight Model for Botnet Attack Detection in Software Defined Network-Orchestrated IoT
###### Abstract  
The Internet of things (IoT) is being used in a variety of industries, including agriculture, the military, smart cities and smart grids, and personalized health care. It is also being used to control critical infrastructure. Nevertheless, because the IoT lacks security procedures and lack the processing power to execute computationally costly antimalware apps, they are susceptible to malware attacks. In addition, the conventional method by which malware-detection mechanisms identify a threat is through known malware fingerprints stored in their database. However, with the ever-evolving and drastic increase in malware threats in the IoT, it is not enough to have traditional antimalware software in place, which solely defends against known threats. Consequently, in this paper, a lightweight deep learning model for an SDN-enabled IoT framework that leverages the underlying IoT resource-constrained devices by provisioning computing resources to deploy instant protection against botnet malware attacks is proposed. The proposed model can achieve 99% precision, recall, and F1 score and 99.4% accuracy. The execution time of the model is 0.108 milliseconds with 118 KB size and 19,414 parameters. The proposed model can achieve performance with high accuracy while utilizing fewer computational resources and addressing resource-limitation issues. b ontet; IoT; SDN; SDN-enabled IoT: detection; lightweight model; deep learning; traditional machine learning +
Footnote †: journal: Applied  
sionally process, and exchange data, according to [PERSON] [12; 13]. The administration of vital infrastructure, agriculture, the military, smart grids and cities, and personal healthcare are just a few of the many applications and services for which the IoT can be used [13]. However, the IoT is vulnerable to malware attacks because it lacks security mechanisms due to limited computational resources to run antimalware applications, low storage capacity, and the fact that it is always connected to the Internet [14]. The IoT is impacted by botnet malware assaults, including Mirai and Prowli attacks. As an alternative, some botnets have been developed to launch a variety of cyberattacks, including identity/data theft (data exfiltration), in which infected machines are used to create and send phony emails, email spam, log keys, and propagate malware. IoT orchestration with SDN leverages the underlying IoT resource-constrained devices through the provision of computing resources to deploy instant protection for IoT gadgets [15]. Software-defined networking [16] (SDN) is anticipated to be a crucial enabler for next-generation networks, the so-called 5G, which will need to combine both IoT applications and conventional human-based services. In this context, SDN enables a global orchestration of IoT to (a) transport the huge amount of data generated at the terminals, sensors, machines, nodes, etc., to any distributed computing node, edge, or core data center, (b) allocate computing, storage, and network resources, and (c) process the collected data (big data) and make the proper decisions (cognition). The conventional way by which malware-detection mechanisms identify a threat is through known malware fingerprints stored in their database. For instance, an antiviral engine checks the presence of the malware fingerprint in a file against known malware fingerprints stored in the antivirus database. However, with the ever-evolving and drastic increase in malware threats, it is not enough to have traditional antimalware software in place, which solely defends against known threats. Active protection must be integrated into IoT systems to effectively defend against evolving strains of malware, also known as zero-day threats. Accordingly, nowadays, frameworks that integrate machine learning have been proposed [17; 18; 19; 20]. [PERSON] et al. [17], in their survey paper, indicated that cybersecurity has massively shifted the technology and its operation. They also claimed that machine learning is paving the way toward a better solution.