Survey on the Family of the Recursive-Rule Extraction Algorithm

In this paper, we first review the theoretical and historical backgrounds on rule extraction from neural network ensembles. Because the structures of previous neural network ensembles were quite complicated, research on an efficient rule extraction algorithm from neural network ensembles has been sparse, even though a practical need exists for rule extraction in Big Data datasets. We describe the Recursive-Rule extraction (Re-RX) algorithm, which is an important step toward handling large datasets. Then we survey the family of the Recursive-Rule extraction algorithm, i.e. the Multiple-MLP Ensemble Re-RX algorithm, and present concrete applications in financial and medical domains that require extremely high accuracy for classification rules. Finally, we mention two promising ideas to considerably enhance the accuracy of the Multiple-MLP Ensemble Re-RX algorithm. We also discuss developments in the near future that will make the Multiple-MLP Ensemble Re-RX algorithm much more accurate, concise, and comprehensible rule extraction from mixed datasets

Cloud-based cyber-physical intrusion detection for vehicles using Deep Learning

Detection of cyber attacks against vehicles is of growing interest. As vehicles typically afford limited processing resources, proposed solutions are rule-based or lightweight machine learning techniques. We argue that this limitation can be lifted with computational offloading commonly used for resource-constrained mobile devices. The increased processing resources available in this manner allow access to more advanced techniques. Using as case study a small four-wheel robotic land vehicle, we demonstrate the practicality and benefits of offloading the continuous task of intrusion detection that is based on deep learning. This approach achieves high accuracy much more consistently than with standard machine learning techniques and is not limited to a single type of attack or the in-vehicle CAN bus as previous work. As input, it uses data captured in real-time that relate to both cyber and physical processes, which it feeds as time series data to a neural network architecture. We use both a deep multilayer perceptron and a recurrent neural network architecture, with the latter benefitting from a long-short term memory hidden layer, which proves very useful for learning the temporal context of different attacks. We employ denial of service, command injection and malware as examples of cyber attacks that are meaningful for a robotic vehicle. The practicality of the latter depends on the resources afforded onboard and remotely, as well as the reliability of the communication means between them. Using detection latency as the criterion, we have developed a mathematical model to determine when computation offloading is beneficial given parameters related to the operation of the network and the processing demands of the deep learning model. The more reliable the network and the greater the processing demands, the greater the reduction in detection latency achieved through offloading

Cyber Security and Critical Infrastructures 2nd Volume

The second volume of the book contains the manuscripts that were accepted for publication in the MDPI Special Topic "Cyber Security and Critical Infrastructure" after a rigorous peer-review process. Authors from academia, government and industry contributed their innovative solutions, consistent with the interdisciplinary nature of cybersecurity. The book contains 16 articles, including an editorial that explains the current challenges, innovative solutions and real-world experiences that include critical infrastructure and 15 original papers that present state-of-the-art innovative solutions to attacks on critical systems

Data-driven decision support for perishable goods

Retailers offering perishable consumer goods such as baked goods have to make hundreds of ordering decisions every day because they typically operate numerous stores and offer a wide range of products. Daily decisions or even intraday decisions are necessary as perishable goods deteriorate quickly and can usually only be sold on one day. Obviously, decision making concerning ordering quantities is a challenging but important task for each retailer as it affects its operational performance. Ordering too little leads to unsatisfied customers while ordering too much leads to discarded goods, which is a major cost factor. In practice, store managers are typically responsible for decisions related to perishable goods, which is not optimal for various reasons. Most importantly, the task is time consuming and some store managers may not have the necessary skills, which results in poor decisions. Hence, our goal is to develop and evaluate methods to support the decision-making process, which is made possible by advances in information technology and data analysis. In particular, we investigate how to exploit large datasets to make better decisions. For daily ordering decisions, we prose data-driven solution approaches for inventory management models that capture the trade-off of ordering too much or ordering too little such that the profits are maximized. First, we optimize the order quantity for each product independently. Second, we consider demand substitution and jointly optimize the order quantities of substitutable products. For intraday decisions, we formulate a scheduling problem for the optimization of baking plans based on hourly forecasts. Demand forecasts are an essential input for operational decisions. However, retail forecasting research is mainly devoted to weekly data using statistical time series models or linear regression models, whereas large-scale forecasting on daily data is understudied. We phrase the forecasting problem as a supervised Machine Learning task and conduct a comprehensive empirical evaluation to illustrate the suitability of Machine Learning methods. We empirically evaluate our solution approaches on real-world datasets from the bakery domain that are enriched with explanatory feature data. We find that our approaches perform competitive to state-of-the-art methods. Data-driven approaches substantially outperform traditional methods if the dataset is large enough. We also find that the benefit of improved forecasting dominates other potential benefits of data-driven solution methods for decision optimization. Overall, we conclude that data-driven decision support for perishable goods is feasible and superior to alternatives that are based on unreasonable assumptions or established time series models