Abstract
Cyber-attack is one of the most challenging aspects of information technology. After the emergence of the Internet of Things, which is a vast network of sensors, technology started moving towards the Internet of Things (IoT), many IoT based devices interplay in most of the application wings like defence, healthcare, home automation etc., As the technology escalates, it gives an open platform for raiders to hack the network devices. Even though many traditional methods and Machine Learning algorithms are designed hot, still it "Have a Screw Loose" in detecting the cyber-attacks. To "Pull the Plug on" an effective "Intrusion Detection System (IDS)" is designed with "Deep Learning" technique. This research work elucidates the importance in detecting the cyber-attacks as "Anomaly" and "Normal". Fast Region-Based Convolution Neural Network (Fast R-CNN), a deep convolution network is implemented to develop an efficient and adaptable IDS. After hunting many research papers and articles, "Gradient Boosting" is found to be a powerful optimizer algorithm that gives us a best results when compared to other existing methods. This algorithm uses "Regression" tactics, a statistical technique to predict the continuous target variable that correlates between the variables. To create a structured valid dataset, a stacked model is made by implementing the two most popular dimensionality reduction techniques Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) algorithms. The brainwaves made us to hybridize Fast R-CNN and Gradient Boost Regression (GBR) which reduces the loss function, processing time and boosts the model's performance. All the above said methods are trained and tested with NIDS dataset V.10 2017. Finally, the "Decision Making" model decides the best result by giving an alert message. Our proposed model attains a high accuracy of 99.5% in detecting the "Cyber Attacks". The experiment results revealed that the effectiveness of our proposed model surpasses other deep neural network and machine learning techniques which have less accuracy.