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.

The Internet of Things (IoT) has emerged as a new technological world connecting billions of devices. Despite providing several benefits, the heterogeneous nature and the extensive connectivity of the devices make it a target of different cyberattacks that result in data breach and financial loss. There is a severe need to secure the IoT environment from such attacks. In this paper, an SDN-enabled deep-learning-driven framework is proposed for threats detection in an IoT environment. The state-of-the-art Cuda-deep neural network, gated recurrent unit (Cu- DNNGRU), and Cuda-bidirectional long short-term memory (Cu-BLSTM) classifiers are adopted for effective threat detection. We have performed 10 folds cross-validation to show the unbiasedness of results. The up-to-date publicly available CICIDS2018 data set is introduced to train our hybrid model. The achieved accuracy of the proposed scheme is 99.87%, with a recall of 99.96%. Furthermore, we compare the proposed hybrid model with Cuda-Gated Recurrent Unit, Long short term memory (Cu-GRULSTM) and Cuda-Deep Neural Network, Long short term memory (Cu- DNNLSTM), as well as with existing benchmark classifiers. Our proposed mechanism achieves impressive results in terms of accuracy, F1-score, precision, speed efficiency, and other evaluation metrics.

The Internet of Things (IoT) has proven to be a billion-dollar industry. Despite offering numerous benefits, the prevalent nature of IoT makes it vulnerable and a possible target for the development of cyber-attacks. The diversity of the IoT, on the one hand, leads to the benefits of the integration of devices into a smart ecosystem, but the heterogeneous nature of the IoT makes it difficult to come up with a single security solution. However, the centralized intelligence and programmability of software-defined networks (SDNs) have made it possible to compose a single and effective security solution to cope with cyber threats and attacks. We present an SDN-enabled architecture leveraging hybrid deep learning detection algorithms for the efficient detection of cyber threats and attacks while considering the resource-constrained IoT devices so that no burden is placed on them. We use a state-of-the-art dataset, CICDDoS 2019, to train our algorithm. The results evaluated by this algorithm achieve high accuracy with a minimal false positive rate (FPR) and testing time. We also perform 10-fold cross-validation, proving our results to be unbiased, and compare our results with current benchmark algorithms.

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.

The recent innovations in the network communication domain have entirely revolutionized the conventional industrial sector by introducing a new era of automatic communication. The Industrial Internet of Things (IIoT) is acknowledged as an exclusive space where Internet of Things (IoT) seems to divulge expressive manifestations. However, the expanded connectivity, more openness, and the widespread use of low-power communication devices of IIoT makes them vulnerable to malicious attacks, and criminal activities. Moreover, the heterogeneous and prevalent nature of the IIoT devices makes it very difficult to come up with a centralized threat detection mechanism, thus, its security remains a major concern. Motivated by this, we propose a fog-empowered augmented intelligence (IA)-based defensive mechanism to ensure secure communication in IoT-enabled smart industries. The designed framework incorporates the aggregated potentials of two highly acclaimed deep learning (DL) classifiers: 1) gated recurrent unit (GRU) and 2) bidirectional long short-term memory (BiLSTM), where the DL-based scheme detects anomalies in such industrial networks and the FOG-based scenario promotes the routing flexibility and interoperability of the heterogeneous devices of the IoT-enabled smart industrial network. The validity of the proposed mechanism is analytically investigated by conducting a comparison with the benchmark threat detection techniques. The proposed solution is also generically examined parallel to some state-of-the-art approaches. The Cu-GRU-BiLSTM framework achieved up to 99.91% accuracy with a low-false alarm rate and outperforms the baseline and recent threat detection approaches. The simulation and comparison results validate the effectiveness of the proposed mechanism and advocate it as a phenomenal choice to ensure efficacious and secure communication in IoT-based smart industries.

The rapid evolution of Smart Infrastructure (SI) on a global scale has revolutionized our daily lives, empowering us with unprecedented connectivity and convenience. However, this evolution has also exposed smart devices to increasingly sophisticated cyber-threats, endangering the integrity of entire smart networks. In response to these challenges, this paper proposes a novel approach utilizing Deep Learning (DL) models for multi-class threat detection in SI environments. Specifically, we introduce the Cu-GRULSTM model, which leverages CUDA-enabled Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) architecture. Additionally, we employ the Cu-GRUDNN model for comparative analysis. Both models are trained and evaluated using the efficient and publicly available CICIDS2018 dataset. Our evaluation results demonstrate the superior performance of the proposed Cu-GRULSTM model, achieving an exceptional accuracy rate of 99.62% with a minimal False Alarms Rate (FAR) of 0.0003. This significant improvement over existing models underscores the efficacy of our approach in mitigating cyber-threats in smart infrastructure environments.