Ahsan Ali et al., International Journal of Advanced Trends in Computer Science and Engineering, 12(5), September - October 2023, 226 - 232 226 ABSTRACT Recently, deep learning has gotten progressively popular in the domain of security. However, Traditional machine learning models are not capable to discover zero-day botnet attacks with extraordinary privacy. For this purpose, researchers have utilized deep learning based computational framework for Botnet which can detect zero-day attacks, achieve data privacy and improve training time using machine learning techniques for the IoT-edge devices. However, it combines and integrates various models and contexts. As a result, the objective of this research was to incorporate the deep learning model which controls different operation of IoT devices and reduce the training time. In deep learning, there are numerous components that aspect the false positive rate of every detected attack type. These elements are F1 score, false-positive rate, and training time; reduce the time of detection, and Accuracy. Bashlite and Mirai are two examples of zero-day botnet attacks that pose a threat to IoT edge devices. The majority of cyber-attacks are executed by malware-infected devices that are remotely controlled by attackers. This malware is often referred to as a bot or botnet, and it enables attackers to control the device and perform malicious actions, such as spamming, stealing sensitive information, and launching DDoS attacks. The model was formulated in Python libraries and subsequently tested on real life data to assess whether the integrated model performs better than its counterparts. The outcomes show that the proposed model performs in a way that is better than existing models i.e. DDL, CDL and LDL as Botnet Attacks Intelligence (BAI) the purposed deep learning model. Key words: Botnet detection, Deep learning (DL), Deep neural network (DNN), Federated learning 1. INTRODUCTION IoT-Edge devices, which include a different connected devices such as sensors, cameras, and smart devices, are becoming increasingly ubiquitous in our daily lives. While these devices offer a wide range of benefits, such as increased convenience and efficiency, they also pose a significant security risk. One of the biggest threats to IoT-Edge devices are botnets [1]. Botnets are collections of compromised devices that can be remotely managed by an attacker, frequently without the owner's knowledge or agreement [3]. Once a device has been compromised, a variety of criminal actions, including DDoS attacks, spamming, phishing, and crypto currency mining, can be performed on it. Many of these devices are designed to be low-cost and energy-efficient, which means that they often lack the necessary security features to protect against botnet attacks [2]. To mitigate the risks of botnet attacks on IoT-Edge devices, threat intelligence can play a critical role. This information can then be used to develop proactive strategies and solutions for identifying and mitigating botnet attacks. For example, threat intelligence can help organizations identify patterns of botnet activity and develop targeted responses to prevent or mitigate attacks [1]. It can also help identify vulnerabilities in IoT-Edge devices and inform the development of more secure devices in the future. In summary, botnets pose a significant threat to IoT-Edge devices, but threat intelligence can play a critical role in mitigating these risks. By providing timely and accurate information about potential threats and vulnerabilities, threat intelligence can help organizations develop proactive strategies and solutions to protect against botnet attacks [17]. It changed the way we communicate with technology in our daily routine. IoT-Edge devices, such as sensors, cameras, and smart home devices, are now an integral part of our homes, workplaces, and public spaces [1]. However, as the number of devices upsurges, so does the risk of cyber threats, particularly botnets. Botnets are collections of compromised devices that a cybercriminal can remotely operate without the owner's knowledge or permission [4]. Botnets can be used to carry out a range of malicious activities, including Distributed Denial of Service attacks, spamming, phishing, and crypto currency mining. IoT-Edge devices are particularly vulnerable to botnet attacks due to their limited processing power, memory, and security mechanisms [3]. To mitigate the risks of botnet attacks on IoT-Edge devices, threat intelligence plays a critical role. Threat intelligence involves collecting, analyzing, and sharing information about potential threats and vulnerabilities [2]. This information can then be used to develop proactive strategies and solutions for identifying and mitigating botnet attacks. In this thesis, we will discuss the importance of botnet threat intelligence in IoT-Edge devices, the challenges and Ahsan Ali 1 , Nabeel Aslam 2 , Arfan Shahzad 3 , Muhammad Junaid Arshad 4 1 Department of Computer Science, University of Engineering and Technology, Pakistan, ahsanmsuet@gmail.com 2 Department of Computer Science, University of Engineering and Technology, Pakistan, nabeelaslam2000@gmail.com 3 Department of Computer Science, University of Engineering and Technology, Pakistan, arfanskp@gmail.com 4 Faculty of Computer Science, University of Engineering and Technology, Pakistan, mjunaiduet@gmail.com Received Date : August 27, 2023 Accepted Date: September 23, 2023 Published Date: October 06, 2023 Botnet Threat Intelligence in IoT-Edge Devices ISSN 2278-3091 Volume 12, No.5, September - October 2023 International Journal of Advanced Trends in Computer Science and Engineering Available Online at http://www.warse.org/IJATCSE/static/pdf/file/ijatcse011252023.pdf https://doi.org/10.30534/ijatcse/2023/011252023