Industrial Internet of Things (IIoT) and Industry 4.0 empower interrelation among manufacturing processes, industrial machines, and utility services. The time-critical data collected from heterogeneous sensing devices are usually communicated to processing points for analysis and aggregation as the basis of IIoT. The IIoTs’ service quality typically depends on data integrity and accuracy, which could be exploited by injecting malicious events, such as false data injection and data poisoning attacks. Thus, effective anomaly recognition and explanation are critical for ensuring quality services and empowering security administrators to interpret the causal reasoning of prediction decisions and underlying data evidence. This study proposes an autoencoder-based detection framework using convolutional and recurrent networks to discover cyber threats in IIoT networks and explain the model. A two-step sliding window (SW) is applied to learn the latent representations of data features better. Malicious points from the raw time series are transformed into fixed-length series through the first-step SW. Every series is converted into continuous-time-reliant subseries via another smaller SW to learn latent representations of malicious events. Fully connected networks use the extracted temporal and spatial features for the classification and explanation of attack events. The empirical results revealed that this framework effectively extracts features that include contexts of malicious patterns. This demonstrated that the proposed framework is robust in detecting malicious events using multiple evaluation metrics and outperforming the contemporary state-of-the-art methods, indicating its suitability as an operative application method in real-world IIoT-based networks.

Intelligent Transportation Systems (ITS), particularly Autonomous Vehicles (AVs), are susceptible to safety and security concerns that impend people's lives. Nothing like manually controlled vehicles, the safekeeping of communications and computing constituents of AVs can be threatened using sophisticated hacking techniques, consequently disrupting AVs from the operative usage in our daily life routines. Once manually controlled vehicles are linked to the Internet, so-called the Internet of Vehicles (IoVs), they would be misused by cyberattacks. In this paper, we present a multi-stage intrusion detection framework to identify intrusions from ITSs and produce low rate of false alarms. The proposed framework can automatically distinguish intrusions in real-time. The proposed framework is based on normal state-based and a deep learning-centered bidirectional Long Short Term Memory (LSTM) architecture to efficiently discover intrusions from the fundamental network gateways and communication networks of AVs. The designed framework is evaluated through two benchmark datasources, that is, the UNSWNB-15 datasource for exterior network communications and the car hacking datasource for in-vehicle communications. The outcomes indicated that the proposed framework achieves high performance that outperforms various current state-of-the-art systems with an accuracy rate of 98.88% for the UNSWNB-15 dataset and 99.11% for the car hacking dataset. Besides, the proposed framework is furthermore capable to detect zero-day (concealed) outbreaks from IoVs networks.

Influenced by the economic globalization, the distributed manufacturing has been a common production mode. This paper considers a multiobjective distributed no-wait flow-shop scheduling problem with sequence-dependent setup time (MDNWFSP-SDST). This scheduling problem exists in many real productions such as baker production, parallel computer system, and surgery scheduling. The performance criteria are the makespan and the total weight tardiness. In the MDNWFSP-SDST, several identical factories are considered with the related flow-shop scheduling problem with no-wait constraints. For solving the MDNWFSP-SDST, a Pareto-based estimation of distribution algorithm (PEDA) is presented. Three probabilistic models including the probability of jobs in empty factory, two jobs in the same factory, and the adjacent jobs are constructed. The PWQ heuristic is extended to the distributed environment to generate initial individuals. A sampling method with the referenced template is presented to generate offspring individuals. Several multiobjective neighborhood search methods are developed to optimize the quality of solutions. The comparison results show that the PEDA obviously outperforms other considered multiobjective optimization algorithms for addressing MDNWFSP-SDST.

This article studies a distributed heterogeneous hybrid flow shop scheduling problem under nonidentical time-of-use electricity tariffs (DHHFSP-NTOU). The makespan and the total electricity charge are considered as the optimization objectives from the view of production and management. The DHHFSP-NTOU considers different processing capabilitie and time-of-use electricity tariffs for each factory. The mixed-integer linear programming (MILP) model of DHHFSP-NTOU is established. To solve the DHHFSP-NTOU, this article proposes an ant colony optimization behavior-based multiobjective evolutionary algorithm based on decomposition (ACO_MOEA/D). A problem-specific ant colony behavior is presented to construct offspring individuals. Eight neighborhoods within the factory and between factories are adopted to improve the quality of the individuals in the archive set. A right-shift movement is used to reduce the electricity charge. A large number of numerical experiments and comprehensive investigations are carried out to test the efficiency and effectiveness of ACO_MOEA/D. The experimental results show that each component (e.g., ant colony behavior, neighborhoods move operators, right-shift movement) contributes to the performance of ACO_MOEA/D. The comparisons with several related algorithms show the superiority of ACO_MOEA/D for solving the DHHFSP-NTOU. Note to Practitioners—From the managers’ insights, the electricity charge is a large cost in the production. The scheduling is an economical approach to reduce the electricity charge. For the time-of-use (TOU) tariffs, the managers can adjust the schedule to reduce the idle time or move some operations to the interval period with a lower electric price. This article studies a distributed heterogeneous hybrid flow shop scheduling problem under nonidentical TOU (UTOU) electricity. This model can be used in many manufacturing enterprises that have several heterogeneous factories. This article proposes an ant colony optimization behavior-based multiobjective evolutionary algorithm based on decomposition (ACO_MOEA/D) to minimize the makespan and the total electricity charge. The ACO_MOEA/D can provide the economy and high-efficiency schedules for practitioners. The computational results confirm its effectiveness and efficiency.

The security of industrial control systems (ICSs) against cyber-attacks is essential in modern era since ICSs are vital constituent of modern societies and smart cities. However, the augmentation of legacy ICS networks with smart computing and networking technologies [such as Internet of Things (IoT)] has intensely enlarged the surface of attacks against these critical infrastructures. This augmentation makes these networks more vulnerable to cyber-attacks and despite the current security solutions, attackers still find ways to proliferate these networks. The intrusion detection system (IDS) is one of the key security aspect to prevent these networks from contemporary cyber-attacks. Therefore, this article proposes a new IDS model named federated-simple recurrent units (SRUs) for the security of IoT-based ICSs. Specifically, the federated-SRUs IDS model uses an improved simple recurrent units architecture to reduce computational cost and alleviate the gradient vanishing issue in recurrent networks. Then, it performs data aggregation through several communication rounds in the federated architecture which allows multiple ICS networks and stakeholders to build a comprehensive IDS model in a privacy-preserving manner. The performance of the federated-SRUs IDS model is validated through experiments using real-world gas pipeline-based ICS network data, which indicates that it is able to accurately detect intrusions in real time without compromising privacy and security. Experiments also verify that the federated-SRUs model outperforms existing state-of-the-art approaches and thus can serve as a viable IDS method in IoT-based ICS networks.