Software defined network (SDN) centralized control intelligence and network abstraction aims to facilitate applications, service deployment, programmability, innovation and ease in configuration management of the underlying networks. However, the centralized control intelligence and programmability is primarily a potential target for the evolving cyber threats and attacks to throw the entire network into chaos. The authors propose a control plane-based orchestration for varied sophisticated threats and attacks. The proposed mechanism comprises of a hybrid Cuda-enabled DL-driven architecture that utilizes the predictive power of Long short-term memory (LSTM) and Convolutional Neural Network (CNN) for an efficient and timely detection of multi-vector threats and attacks. A current state of the art dataset CICIDS2017 and standard performance evaluation metrics have been employed to thoroughly evaluate the proposed mechanism. We rigorously compared our proposed technique with our constructed hybrid DL-architectures and current benchmark algorithms. Our analysis shows that the proposed approach out-performs in terms of detection accuracy with a trivial trade-off speed efficiency. We also performed a 10-fold cross validation to explicitly show unbiased results.

The predominant Android operating system has captured enormous attention globally not only in smart phone industry but also for varied smart devices. The open architecture and application programming interfaces (APIs) while hosting third party applications has led to explosive growth of varied pervasive sophisticated Android malware production. In this study, we propose a robust, scalable and efficient Cuda-empowered multi-class malware detection technique leveraging Gated Recurrent Unit (GRU) to identify sophisticated Android malware. Experimentation of the proposed technique has been carried out using current state-of-the-art datasets of Android applications (i.e., Android Malware Dataset (AMD), Androzoo). Moreover, to rigorously evaluate the performance of the proposed technique, we have employed standard performance evaluation metrics (e.g., accuracy, precision, recall, F1-score etc.) and compared it with our constructed DL-driven architectures and benchmark algorithms. The GRU-based malware detection system outperforms with 98.99% detection accuracy for malware identification with a trivial trade off in speed efficiency.

Distributed Industrial Internet of Things (IIoT) has entirely revolutionized the industrial sector that varies from autonomous industrial processes to automation of processes without human intervention. However, threat hunting and intelligence is the most complex task in distributed IIoT. Besides, there exist no standard architectures for hunting micro services orchestration in distributed IIoT systems. The authors propose an efficient and self-learning autonomous multi-vector threat intelligence and detection mechanism to proactively defend IIoT systems/networks. Our proposed novel Cuda-empowered Convolutional LSTM2D (ConvLSTM2D) mechanism is highly scalable with self-optimizing capabilities to proficiently tackle diverse dynamic variants of emerging IIoT sophisticated threats and attacks. For a comprehensive evaluation, the authors employed a current state-of-the-art dataset with 21 million instances comprised of varying attack patterns and prevalent threat vectors. Moreover, the proposed technique is compared with our constructed contemporary Deep Learning (DL)-driven architectures and benchmark algorithms. The proposed mechanism outperforms in terms of detection accuracy with a trivial trade-off in speed efficiency.

The Industrial Internet of things (IIoT) is the main driving force behind smart manufacturing, industrial automation, and industry 4.0. Conversely, industrial IoT as the evolving technological paradigm is also becoming a compelling target for cyber adversaries. Particularly, advanced persistent threats (APT) and especially botnets are the foremost promising and potential attacks that may throw the complete industrial IoT network into chaos. IIoT-enabled botnets are highly scalable, technologically diverse, and highly resilient to classical and conventional detection mechanisms. Subsequently, we propose a deep learning (DL)-enabled novel hybrid architecture that can efficiently and timely tackle distributed, multivariant, lethal botnet attacks in industrial IoT. The proposed approach is thoroughly evaluated on a current state-of-the-art, publicly available dataset using standard performance evaluation metrics. Moreover, our proposed technique has been precisely verified with our constructed hybrid DL-enabled architectures and current benchmark DL algorithms. Our devised mechanism shows promising results in terms of high detection accuracy with a trivial trade-off in speed efficiency, assuring the proposed scheme as an optimal and legitimate cyber defense in prevalent IIoTs. Besides, we have cross-validated our results to show utterly unbiased performance.

Intelligent transportation systems have been envisioned to bring more intelligence and cooperative sensing to meet the imminent demands of overall improved autonomous transportation. However, dynamic era of modern applications and fixed architecture of legacy Internet needs flexible, innovative, adaptive, and programmable software defined intelligent transportation systems (SD-ITS). The centralized control intelligence of SD-ITS can be a potential primary target of the prevalent cyber threats and attacks that can simply throw the entire network into chaos. The authors propose a DL-driven multi-vector scalable attack detection framework leveraging graphical processing unit (GPU) empowered Bidirectional Long Short-Term Memory (BLSTM) to efficiently tackle exponentially growing diverse sophisticated attacks that primarily target the control unit of the SD-ITS. The proposed technique has been rigorously evaluated with current state-of-the-art publicly available Flow-based dataset (i.e., CICIDS2017) using standard performance metrics. Further, the proposed mechanism is compared with contemporary benchmarks (i.e., DL algorithms). Extensive experimental results exhibit out-performance of the proposed technique in term of detection accuracy with a trivial trade-off computational complexity. Finally, the study also employed 10-fold cross validation to explicitly show unbiased results.

Automatic modulation recognition (AMR) is used in various domains—from general-purpose communication to many military applications—thanks to the growing popularity of the Internet of Things (IoT) and related communication technologies. In this research article, we propose an innovative idea of combining the classical mathematical technique of computing linear combinations (LCs) of cumulants with a genetic algorithm (GA) to create super-cumulants. These super-cumulants are further used to classify five digital modulation schemes on fading channels using the K-nearest neighbor (KNN). Our proposed classifier significantly improves the percentage recognition accuracy at lower SNRs when using smaller sample sizes. A comparison with existing techniques manifests the supremacy of our proposed classifier.