Advanced manufacturing is one of the core national strategies in the US (AMP), Germany (Industry 4.0) and China (Made-in China 2025). The emergence of the concept of Cyber Physical System (CPS) and big data imperatively enable manufacturing to become smarter and more competitive among nations. Many researchers have proposed new solutions with big data enabling tools for manufacturing applications in three directions: product, production and business. Big data has been a fast-changing research area with many new opportunities for applications in manufacturing. This paper presents a systematic literature review of the state-of-the-art of big data in manufacturing. Six key drivers of big data applications in manufacturing have been identified. The key drivers are system integration, data, prediction, sustainability, resource sharing and hardware. Based on the requirements of manufacturing, nine essential components of big data ecosystem are captured. They are data ingestion, storage, computing, analytics, visualization, management, workflow, infrastructure and security. Several research domains are identified that are driven by available capabilities of big data ecosystem. Five future directions of big data applications in manufacturing are presented from modelling and simulation to realtime big data analytics and cybersecurity.
Keywords: Smart manufacturing | Big data | Cloud computing | Cloud manufacturing | Internet of things | NoSQL

Attack and anomaly detection in the Internet of Things (IoT) infrastructure is a rising concern in the domain of IoT. With the increased use of IoT infrastructure in every do- main, threats and attacks in these infrastructures are also growing commensurately. De- nial of Service, Data Type Probing, Malicious Control, Malicious Operation, Scan, Spying and Wrong Setup are such attacks and anomalies which can cause an IoT system failure. In this paper, performances of several machine learning models have been compared to predict attacks and anomalies on the IoT systems accurately. The machine learning (ML) algorithms that have been used here are Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). The evaluation metrics used in the comparison of performance are accuracy, precision, recall, f1 score, and area under the Receiver Operating Characteristic Curve. The system obtained 99.4% test accuracy for Decision Tree, Random Forest, and ANN. Though these techniques have the same accuracy, other metrics prove that Random Forest performs comparatively better.
Keywords: Internet of Things (IoT) | Machine Learning | Cybersecurity | Anomaly detection

In this moment of rising nationalism worldwide, governments, civil society groups, transnational companies, and web users all complain of increasing regional fragmentation online. While prior work in this area has primarily focused on issues of government censorship and regulatory compliance, we use an inductive and qualitative approach to examine targeted blocking by corporate entities of entire regions motivated by concerns about fraud, abuse, and theft. Through participant-observation at relevant events and intensive interviews with experts, we document the quest by professionals tasked with preserving online security to use new machine-learning based techniques to develop a ‘‘fairer’’ system to determine patterns of ‘‘good’’ and ‘‘bad’’ usage. However, we argue that without understanding the systematic social and political conditions that produce differential behaviors online, these systems may continue to embed unequal treatments, and troublingly may further disguise such discrimination behind more complex and less transparent automated assessment. In order to support this claim, we analyze how current forms of regional blocking incentivize users in blocked regions to behave in ways that are commonly flagged as problematic by dominant security and identification systems. To realize truly global, non-Eurocentric cybersecurity techniques would mean incorporating the ecosystems of service utilization developed by marginalized users rather than reasserting norms of an imagined (Western) user that casts aberrations as suspect.
Keywords: Regional blocking | machine learning | classification | inequality | discrimination | security

The Cognitive Packet Network (CPN) bases its routing decisions and flow control on the Random Neural Network (RNN) Reinforcement Learning algorithm; this paper proposes the addition of a Deep Learning (DL) Cluster management structure to the CPN for Quality of Service metrics (Delay Loss and Bandwidth), Cyber Security keys (User, Packet and Node) and Management decisions (QoS, Cyber and CEO). The RNN already models how neurons transmit information using positive and negative impulsive signals whereas the proposed additional Deep Learning structure emulates the way the brain learns and takes decisions; this paper presents a brain model as the combination of both learning algorithms, RNN and DL. The pro- posed model has been simulated under different network sizes and scenarios and it has been validated against the CPN itself without DL clusters. The simulation results are promising; the presented CPN with DL clusters as a mechanism to transmit, learn and make packet routing decisions is a step closer to em- ulate the way the brain transmits information, learns the environment and takes decisions.
Keywords: Random Neural Network | Deep Learning Clusters | Cognitive Packet Network | QoS | Cybersecurity | Routing