-74813b3e17f460286df2564 20220709051205060 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 07 30 2022 11 8 Department of Computer Engineering, College of Engineering, Pune (Maharashtra), India. Akshata Deshmukh Dr. Tanuja Pattanshetti Department of Computer Engineering, College of Engineering, Pune (Maharashtra), India. The network of devices known as the Internet of Things (IoT) consists of hardware with sensors and software. These devices communicate and exchange data through the internet. IoT device-based data exchanges are often processed at cloud servers. Since the number of edge devices and quantity of data exchanged is increasing, massive latency-related concerns are observed. The answer to these issues is fog computing technology. Fog computing layer is introduced between the edge devices and cloud servers. Edge devices can conveniently access data from the fog servers. Security of fog layer devices is a major concern. As it provides easy access to different resources, it is more vulnerable to different attacks. In this paper, a deep learning-based intrusion detection approach called Multi-LSTM Aggregate Classifier is proposed to identify malicious traffic for the fog-based IoT network. The MLAC approach contains a set of long short-term memory (LSTM) modules. The final outcomes of these modules are aggregated using a Random Forest to produce the final outcome. Network intrusion dataset UNSW-NB15 is used to evaluate performance of the MLAC technique. For binary classification accuracy of 89.40% has been achieved using the proposed deep learning-based MLAC model. 07 30 2022 59 66 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.H9179.0711822 https://www.ijitee.org/portfolio-item/h91790711822/ Department of Computer Engineering, College of Engineering, Pune (Maharashtra), India. Darshan Thakur Dr. Tanuja Pattanshetti Department of Computer Engineering, College of Engineering, Pune (Maharashtra), India In all technologies, including traditional computing and cloud computing, security has always been the primary concern. In recent years, cloud computing has become widely accepted on a global scale. Cyber attacks aimed at it have increased along with its widespread acceptance. Although ample research is done in the security domain and cloud computing is based on rigid security fundamentals, the advancing network security attacks create the need for an advanced security mechanism. Also, the multiclass classification strategy has received very little attention, and classification accuracy can yet be improved. Hence, this work proposes an Ensemble Filter-based Intrusion Detection System (EFIDS) to address the limitations of previous research work. It not only identifies malicious traffic but also categorizes the attempted attacks (multiclass classification). The famous intrusion detection benchmark dataset, NSL KDD, is used to evaluate the model. Using the model, it was possible to enhance the classification accuracy of both binary and multiclass approaches up to 99.85 percent and 99.63 percent, respectively. Additionally, both forms of classification have shown a 65–70% improvement in training time. 07 30 2022 67 72 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.H9180.0711822 https://www.ijitee.org/portfolio-item/h91800711822/