Abstract
In today’s digital world, the information systems are revolutionizing the way we connect. As the people are trying to adopt and integrate intelligent systems into daily lives, the risks around cyberattacks on user-specific information have significantly grown. To ensure safe communication, the Intrusion Detection Systems (IDS) were developed often by using machine learning (ML) algorithms that have the unique ability to detect malware against network security violations. Recently, it was reported that the IDS are prone to carefully crafted perturbations known as adversaries. With the aim to understand the impact of such attacks, in this paper, we have proposed a novel random neural network-based adversarial intrusion detection system (RNN-ADV). The NSL-KDD dataset is utilized for training. For adversarial attack crafting, the Jacobian Saliency Map Attack (JSMA) algorithm is used, which identifies the feature which can cause maximum change to the benign samples with minimum added perturbation. To check the effectiveness of the proposed adversarial scheme, the results are compared with a deep neural network which indicates that RNN-ADV performs better in terms of accuracy, precision, recall, F1 score and training epochs.
Highlights
Internet services are responsible for the creation of a digital world of connectivity with the help of sensors and actuators which are widely used for the automation of smart grids, homes, health monitoring equipment, and several other systems [1]
We have explained the experimental results and presented an analysis of the effects of adversarial attacks on random neural networks. Several performance matrices such as Precision, Recall, F1 Score, False Detection Rate and Accuracy are used to elaborate on the results where are denoted as τ, ψ, υ, and ω the True Positives (TP), True Negatives (TN), False Positives (FP) and False
Precision and recall values are declining with the increase in misclassification, which results in a higher false detection rate and low F1 score
Summary
Internet services are responsible for the creation of a digital world of connectivity with the help of sensors and actuators which are widely used for the automation of smart grids, homes, health monitoring equipment, and several other systems [1] Even though such systems are often considered intelligent and secure, but network proliferation makes them vulnerable to many external threats. A significant extension of intelligent services to the users has raised strong concerns among the research communities to design and develop efficient intrusion detection systems (IDS) [3]. Does it enforce the safety of the communication system, but it could be useful as an alternative to traditional firewalls. A harder approach is utilized using the random forest (RF) machine learning algorithm due to its effectiveness
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