Cyber Threats Detection in Smart Environments Using SDN-Enabled DNN-LSTM Hybrid Framework

Abstract

Internet of Things (IoT) is an instantly exacerbated communication technology that is manifesting miraculous effectuation to revolutionize conventional means of network communication. The applications of IoT are compendiously encompassing our prevalent lifestyle and the integration of IoT with other technologies makes this application spectrum even more latitudinous. However, this admissibility also introduces IoT with a pervasive array of imperative security hazards that demands noteworthy solutions to be swamped. In this scientific study, we proposed Deep Learning (DL) driven Software Defined Networking (SDN) enabled Intrusion Detection System (IDS) to combat emerging cyber threats in IoT. Our proposed model (DNNLSTM) is capable to encounter a tremendous class of common as well as less frequently occurring cyber threats in IoT communications. The proposed model is trained on CICIDS 2018 dataset, and its performance is evaluated on several decisive parameters i.e Accuracy, Precision, Recall, and F1-Score. Furthermore, the designed framework is analytically compared with relevant classifiers, i.e., DNNGRU, and BLSTM for appropriate validation. An exhaustive performance comparison is also conducted between the proposed system and a few preeminent solutions from the literature. The proposed design has circumvented the existing literature with unprecedented performance repercussions such as 99.55% accuracy, 99.36% precision, 99.44% recall, and 99.42% F1-score.

An AI-Driven Hybrid Framework for Intrusion Detection in IoT-Enabled E-Health

“…Cyber threats could cost up to $ 90 trillion by 2030 if no reasonable alternative is given before then [10,11]. IoMT environments pose three issues as follows: e rst is the heterogeneous network and dynamic nature, the second is its hugely scattered design, and the last is the protocols that the IoT use to address concerns like computing limits and power consumption in network sensors [12,13]. e most common issue in IoMT setups is keylogging, botnet attacks, and zero-day exploits [14][15][16].…”

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

“…This design is ideal for resource-constrained DDoS attackers due to its negligible management costs, high detectability, and excellent robustness. It is simplified to a variation problem where the objective functional represents the predicted impact of the DDoS attack associated with a DDoS attack method in this architecture and is based on a novel botnet growth model [38]. Finally, the variation problem for three alternative DDoS defense systems has been solved.…”

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

“…References [ 17 , 27 ] mentioned the limitations and issues faced in adopting blockchain in electronic health record systems, including performance and scalability issues, secure and authorized identification, lack of incentive, and over-and under- usability. Reference [ 5 ] presented a survey in data management for patients that searches the self-sovereign aspect using blockchain. Reference [ 28 ] reviewed the healthcare sector using a blockchain approach from the highest-layer architecture to the lowest-layer algorithms.…”