Securing IoT-based Critical Infrastructures (CI) necessitates a cross-domain management of IT/OT systems encompassing hardware, software, data, and models along with their application scenarios. There is no feasible way to manually secure such heterogeneous, weakly-protected and physically distributed systems. In this work, we propose an adaptive security framework that uses Federated Learning (FL) for IoT data monitoring and analysis at the edge. We use Deep Neural Network (DNN) model selection and switching inside FL to address performance and security problems. We began prototyping the Edge-FL system using single board computers (a Raspberry Pi-5 cluster). We measure and compare the impacts of hardware heterogeneity. The framework aims to provide continuum of security and intelligence from IoT device to Edge to Cloud.

Motivated by real customer problems, we investigated utilization of cloud services at different layers including infrastructure (IaaS), application services (PaaS) and databases (DaaS). We found several issues such as forgetting about unused resources, bursty workloads and service dependencies causing under-utilization (a.k.a. over- provisioning) problem. Cloud advisory tools offered by the public providers either lack the fine-grained analysis needed for actionable recommendations or can’t see the correlations among services that are used by the same customers’ resource groups. We proposed an automated, near real-time advisor that utilizes historical usage data and machine learning (ML) models to recommend cost saving opportunities. We demonstrated significant cost savings averaging around 20%, which can accumulate as thousands of Dollars for large and active systems. Since our advisory models depend on time-series data, we compared several forecasting algorithms including ARIMA, LSTM and Prophet. We found LSTM model to deliver the most accurate results for our workloads.

Complex Event Processing (CEP) has become increasingly important for tracking and monitoring anomalies and trends in event streams emitted from business processes such as supply chain management to online stores in e-commerce. These monitoring applications submit complex event queries to track sequences of events that match a given pattern. While the state-of-the-art CEP systems mostly focus on the execution of flat sequence queries, we instead support the execution of nested CEP queries specified by the (NEsted Event Language) NEEL. However the iterative execution often results in the repeated recomputation of similar or even identical results for nested subexpressions as the window slides over the event stream. In this work we thus propose to optimize NEEL execution performance by caching intermediate results. In particular we design two methods of applying selective caching of intermediate results. The first is the Continuous Sliding Caching technique. The second is a further optimization of the previous technique which we call the Interval-Driven Semantic Caching. Techniques for incrementally loading, purging and exploiting the cache content are described. Our experimental study using real-world stock trades evaluates the performance of our proposed caching strategies for different query types.

Complex Event Processing (CEP) over event streams has become increasingly important for real-time applications ranging from healthcare to supply chain management. In such applications, arbitrarily complex sequence patterns as well as non existence of such complex situations must be detected in real time. To assure real-time responsiveness for detection of such complex pattern over high volume high-speed streams, efficient processing techniques must be designed. Unfortunately the efficient processing of complex sequence queries with negations remains a largely open problem to date. To tackle this shortcoming, we designed optimized strategies for handling nested CEP query. In this demonstration, we propose to showcase these techniques for processing and optimizing nested pattern queries on streams. In particular our demonstration showcases a platform for specifying complex nested queries, and selecting one of the alternative optimized techniques including sub-expression sharing and intermediate result caching to process them. We demonstrate the efficiency of our optimized strategies by graphically comparing the execution time of the optimized solution against that of the default processing strategy of nested CEP queries. We also demonstrate the usage of the proposed technology in several healthcare services.

Complex event processing (CEP) over event streams has become increasingly important for real-time applications ranging from health care, supply chain management to business intelligence. These monitoring applications submit complex queries to track sequences of events that match a given pattern. As these systems mature the need for increasingly complex nested sequence query support arises, while the state-of-art CEP systems mostly support the execution of flat sequence queries only. To assure real-time responsiveness and scalability for pattern detection even on huge volume high-speed streams, efficient processing techniques must be designed. In this paper, we first analyze the prevailing nested pattern query processing strategy and identify several serious shortcomings. Not only are substantial subsequences first constructed just to be subsequently discarded, but also opportunities for shared execution of nested subexpressions are overlooked. As foundation, we introduce NEEL, a CEP query language for expressing nested CEP pattern queries composed of sequence, negation, AND and OR operators. To overcome deficiencies, we design rewriting rules for pushing negation into inner subexpressions. Next, we devise a normalization procedure that employs these rules for flattening a nested complex event expression. To conserve CPU and memory consumption, we propose several strategies for efficient shared processing of groups of normalized NEEL subexpressions. These strategies include prefix caching, suffix clustering and customized “bit-marking” execution strategies. We design an optimizer to partition the set of all CEP subexpressions in a NEEL normal form into groups, each of which can then be mapped to one of our shared execution operators. Lastly, we evaluate our technologies by conducting a performance study to assess the CPU processing time using real-world stock trades data. Our results confirm that our NEEL execution in many cases performs 100 fold faster than the traditional iterative nested execution strategy for real stock market query workloads.

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Many modern applications including tag based mass transit systems, RFID-based supply chain management systems and online financial feeds require special purpose event stream processing technology to analyze vast amounts of sequential multi-dimensional data available in real-time data feeds. Traditional online analytical processing (OLAP) systems are not designed for real-time pattern-based operations, while Complex Event Processing (CEP) systems are designed for sequence detection and do not support OLAP operations. We will demonstrate a novel E-Cube model that combines CEP and OLAP techniques for multi-dimensional event pattern analysis at different abstraction levels. A London transit scenario will be given to demonstrate the utility and performance of this proposed technology.

It is extremely hard for a global organization with services over multiple channels to capture a consistent and unified view of its data, services, and interactions. While SOA and web services are addressing integration and interoperability problems, it is painful for an operational organization with legacy systems to quickly switch to service-based methods. We need methods to combine advantages of traditional (i.e. web, desktop, or mobile) application development environments and service-based deployments.In this paper, we focus on the design and implementation of session management as a core service to support business processes and go beyond application-specific sessions and web sessions. We develop local session components for different platforms and complement them with a remote "session service" that is independent of applications and platforms. We aim to close the gap between the two worlds by combining their performance, availability and interoperability advantages.