Although the field of eXplainable Artificial Intelligence (XAI) has a significant interest these days, its implementation within cyber security applications still needs further investigation to understand its effectiveness in discovering attack surfaces and vectors. In cyber defence, especially anomaly-based Intrusion Detection Systems (IDS), the emerging applications of machine/deep learning models require the interpretation of the models' architecture and the explanation of models' prediction to examine how cyberattacks would occur. This paper proposes a novel explainable intrusion detection framework in the Internet of Things (IoT) networks. We have developed an IDS using a Short-Term Long Memory (LSTM) model to identify cyberattacks and explain the model's decisions. This uses a novel set of input features extracted by a novel SPIP (S: Shapley Additive exPlanations, P: Permutation Feature Importance, I: Individual Conditional Expectation, P: Partial Dependence Plot) framework to train and evaluate the LSTM model. The framework was validated using the NSL-KDD, UNSW-NB15 and TON_IoT datasets. The SPIP framework achieved high detection accuracy, processing time, and high interpretability of data features and model outputs compared with other peer techniques. The proposed framework has the potential to assist administrators and decision-makers in understanding complex attack behaviour.

Accurate estimation of reservoir parameters (e.g., permeability and porosity) helps to understand the movement of underground fluids. However, reservoir parameters are usually expensive and time-consuming to obtain through petrophysical experiments of core samples, which makes a fast and reliable prediction method highly demanded. In this article, we propose a deep learning model that combines the 1-D convo- lutional layer and the bidirectional long short-term memory network to predict reservoir permeability and porosity. The mapping relationship between logging data and reservoir parameters is established by training a network with a combination of nonlinear and linear modules. Optimization algorithms, such as layer normalization, recurrent dropout, and early stopping, can help obtain a more accurate training model. Besides, the self-attention mechanism enables the network to better allocate weights to improve the prediction accuracy. The testing results of the well-trained network in blind wells of three different regions show that our proposed method is accurate and robust in the reservoir parameters prediction task.

Total energy minimization in data centers (including both computing and cooling energy) requires modeling the interactions between computing decisions (such as load distribution) and heat transfer in the room, since load acts as heat sources whose distribution in space affects cooling energy. This paper presents the first closed-form analytic optimal solution for load distribution in a machine rack that minimizes the sum of computing and cooling energy. We show that by considering actuation knobs on both computing and cooling sides, it is possible to reduce energy cost comparing to state of the art solutions that do not offer holistic energy optimization. The above can be achieved while meeting both throughput requirements and maximum CPU temperature constraints. Using a thorough evaluation on a real test bed of 20 machines, we demonstrate that our simple model adequately captures the thermal behavior and energy consumption of the system. We further show that our approach saves more energy compared to the state of the art in the field.

Derivative free optimization algorithms are often used when it is difficult to find function derivatives, or if finding such derivatives are time consuming. The Nelder Mead's simplex method is one of the most popular derivative free optimization algorithms in the fields of engineering, statistics, and sciences. This algorithm is favored and widely used because of its fast convergence and simplicity. The simplex method converges really well with small scale problems of some variables. However, it does not have much success with large-scale problems of multiple variables. This factor has reduced its popularity in optimization sciences significantly. Two solutions of quasi-gradients are introduced to improve it in terms of the convergence rate and the convergence speed. The improved algorithm with higher success rate and faster convergence which still maintains the simplicity is the key feature of this paper. This algorithm will be compared on several benchmark functions with other popular optimization algorithms such as the genetic algorithm, the differential evolution algorithm, the particle swarm algorithm, and the original simplex method. Then, the comparing results will be reported and discussed.

This paper introduces a neural network training tool through computer networks. The following algorithms, such as neuron by neuron (NBN) , error back propagation (EBP), Levenberg Marquardt (LM) and its improved versions are implemented in two different computing methods, traditional forward-backward computation and newly developed forward-only computation. The training tool can handle not only conventional multilayer perceptron (MLP) networks, but also arbitrarily connected neuron (ACN) networks. There are several benefits that make this network training tool desirable. Network training process can be used remotely through any network connection or any operating system can be used to access the network training tool, making the application operating system independent. Also much less installation time and configuration time is required because the training tool locates on one central machine. Many users can access at the same time. Users can train and see the training results directly through networks. And the most important thing is the software producers can protect their intellectual property when network browsers are used as user interface.

This paper develops a navigation service, called GreenGPS, that uses participatory sensing data to map fuel consumption on city streets, allowing drivers to find the most fuel efficient routes for their vehicles between arbitrary end-points. The service exploits measurements of vehicular fuel consumption sensors, available via the OBD-II interface standardized in all vehicles sold in the US since 1996. The interface gives access to most gauges and engine instrumentation. The most fuel-efficient route does not always coincide with the shortest or fastest routes, and may be a function of vehicle type. Our experimental study shows that a participatory sensing system can influence routing decisions of individual users and also answers two questions related to the viability of the new service. First, can it survive conditions of sparse deployment? Second, how much fuel can it save? A challenge in participatory sensing is to generalize from sparse sampling of high-dimensional spaces to produce compact descriptions of complex phenomena. We illustrate this by developing models that can predict fuel consumption of a set of sixteen different cars on the streets of the city of Urbana-Champaign. We provide experimental results from data collection suggesting that a 1% average prediction error is attainable and that an average 10% savings in fuel can be achieved by choosing the right route.

Many participatory sensing applications use data collected by participants to construct a public model of a system or phenomenon. For example, a health application might compute a model relating exercise and diet to amount of weight loss. While the ultimately computed model could be public, the individual input and output data traces used to construct it may be private data of participants (e.g., their individual food intake, lifestyle choices, and resulting weight). This paper proposes and experimentally studies a technique that attempts to keep such input and output data traces private, while allowing accurate model construction. This is significantly different from perturbation-based techniques in that no noise is added. The main contribution of the paper is to show a certain data transformation at the client side that helps keeping the client data private while not introducing any additional error to model construction. We particularly focus on linear regression models which are widely used in participatory sensing applications. We use the data set from a map-based participatory sensing service to evaluate our scheme. The service in question is a green navigation service that constructs regression models from participant data to predict the fuel consumption of vehicles on road segments. We evaluate our proposed mechanism by providing empirical evidence that: i) an individual data trace is generally hard to reconstruct with any reasonable accuracy, and ii) the regression model constructed using the transformed traces has a much smaller error than one based on additive data-perturbation schemes.

The great increase in online transactions and the thousands of online retailers has created a great demand for companies to gain competitive advantage. An easy way for a company to gain customer advantage is through the use of data mining. Due to this high demand we have developed a prototypical tool to help with the analysis of these online transactions. From the raw data generated by running these transactions we are able to find consumer trends and shopping patterns by using hierarchical clustering and association rules mining algorithm. The focus of this research is to demonstrate how this development can be useful and effective in a business situation for companies to gain competitive advantage.

This paper introduces the abstraction of energy reserves for sensor networks that virtualizes energy sources. It gives each of several applications sharing a platform the illusion of having its own private energy source. Energy virtualization is the next logical step in embedded systems after visualizing communication links and CPU capacity. Energy virtualization has not been addressed in past sensor network literature because most current wireless sensor networks feature single-user applications. To amortize deployment costs, future sensor networks, deployed in remote or hard- to-access areas, will likely be leveraged by scientists from different disciplines, each having their independent application for their individual research purposes. Platforms, planned for such deployment, will befitted with the union of sensors needed, but independent applications will share the remaining resources such as in-field storage and communication bandwidth, calling for quotas and isolation mechanisms. The most expensive resource shared in sensor networks is energy. This paper provides an energy isolation mechanism, called the virtual battery, that logically divides energy among applications to provide each its private energy reserve. An application can manage its private energy independently as if it were running alone on the platform. The application is terminated when its reserve is depleted. We implement and evaluate this abstraction on MicaZ motes running LiteOS. Our results show that the virtual battery mechanism succeeds at exporting the private reserve abstraction accurately and at a low overhead.

This paper develops mathematical foundations and architectural components for providing privacy guarantees on stream data in grassroots participatory sensing applications, where groups of participants use privately-owned sensors to collectively measure aggregate phenomena of mutual interest. Grassroots applications refer to those initiated by members of the community themselves as opposed to by some governing or official entities. The potential lack of a hierarchical trust structure in such applications makes it harder to enforce privacy. To address this problem, we develop a privacy-preserving architecture, called PoolView, that relies on data perturbation on the client-side to ensure individuals' privacy and uses community-wide reconstruction techniques to compute the aggregate information of interest. PoolView allows arbitrary parties to start new services, called pools, to compute new types of aggregate information for their clients. Both the client-side and server-side components of PoolView are implemented and available for download, including the data perturbation and reconstruction components. Two simple sensing services are developed for illustration; one computes traffic statistics from subscriber GPS data and the other computes weight statistics for a particular diet. Evaluation, using actual data traces collected by the authors, demonstrates the privacy-preserving aggregation functionality in PoolView.