A Hybrid Intelligent Framework to Combat Sophisticated Threats in Secure Industries

Abstract

With the new advancements in Internet of Things (IoT) and its applications in different sectors, such as the industrial sector, by connecting billions of devices and instruments, IoT has evolved as a new paradigm known as the Industrial Internet of Things (IIoT). Nonetheless, its benefits and applications have been approved in different areas, but there are possibilities for various cyberattacks because of its extensive connectivity and diverse nature. Such attacks result in financial loss and data breaches, which urge a consequential need to secure IIoT infrastructure. To combat the threats in the IIoT environment, we proposed a deep-learning SDN-enabled intelligent framework. A hybrid classifier is used for threat detection purposes, i.e., Cu-LSTMGRU + Cu-BLSTM. The proposed model achieved a better detection accuracy with low false-positive rate. We have conducted 10-fold cross-validation to show the unbiasdness of the results. The proposed scheme results are compared with Cu-DNNLSTM and Cu-DNNGRU classifiers, which were tested and trained on the same dataset. We have further compared the proposed model with other existing standard classifiers for a thorough performance evaluation. Results achieved by our proposed scheme are impressive with respect to speed efficiency, F1 score, accuracy, precision, and other evaluation metrics.

Artificial Intelligence Based Threat Detection in Industrial Internet of燭hings燛nvironment

“…In order to validate the supremacy of the proposed AITDC-IIoT model, a detailed comparative analysis was performed against existing models and the results are shown in Tab. 3 [28]. Based on the results and discussion made above, it is apparent that the proposed AITDC-IIoT model is an excellent performer in terms of threat detection and classification compared to the existing techniques.…”

Detection of Distributed Denial of Service (DDoS) Attacks in IOT Based Monitoring System of Banking Sector Using Machine Learning Models

“…It was found that the proposed technique reduces false positives by 35% while maintaining an extremely high detection rate. Yet to be identified and described are the ideal prerequisites for a protective solution despite this work's goal of identifying and describing those criteria [34,35]. All HTTP-based DoS and DDoS assaults have been thoroughly examined in this study, which aims to define and describe the ideal criteria for a protective architecture against these attacks in-depth.…”

“…The author's proposed algorithms outperform those of other researchers in detecting DDoS attacks when tested on real-world datasets. If the system's security defenses are deceived by someone with valid access to the system, an insider attack is more likely [35]. Anti-DDoS assaults can be prevented by using EDIP (Early Detection and Isolation).…”

“…Using the load balancing approach, proxies can avoid being overloaded. Researchers have developed spectral gene set filtering (SGSF), a new method for filtering gene set collections prior to gene set testing in order to overcome the statistical power challenge posed by large gene set collections [4,[35][36][37].…”

A Novel Decentralized Blockchain Architecture for the Preservation of Privacy and Data Security against Cyberattacks in Healthcare

“…Data sharing between organizations proves to be another vital challenge [ 40 , 41 ] that demands the health and medical dataset created by one healthcare personnel be shared securely with other healthcare-requesting entities such as a doctor or a healthcare research entity. The summary of various applications in the field of healthcare using the blockchain is summarized in Table 1 .…”