Abstract The detection of Hardware Trojans (HTs) in electronic components is critical to ensuring the security and integrity of electronic systems, as these malicious modifications can leak sensitive information, alter device functionality, or completely disable the device. This study introduces a novel detection method combining deep learning techniques with dual-channel image transformations to identify HTs via Side-Channel Analysis (SCA). Specifically, our approach converts side-channel time series signals, such as power consumption, electromagnetic emissions, and timing data, into dual-channel, image-like representations using Gramian Angular Field (GAF) and reshaping techniques. These transformations allow convolutional neural networks (CNNs), known for their effectiveness in image analysis, to capture subtle and complex anomalous patterns indicative of HTs. We rigorously evaluated our dual-channel methodology using publicly available Advanced Encryption Standard (AES) datasets for HTs provided by TrustHub and IEEE Dataport. Experimental results demonstrate that the proposed approach achieves superior accuracy compared to existing methods, particularly in challenging datasets. Additionally, we assessed the robustness of our model by introducing varying noise levels to simulate real-world operational perturbations such as process variations, aging, and voltage fluctuations. The proposed method maintains high detection accuracy and demonstrates enhanced resilience under noisy conditions, underscoring its practical applicability and effectiveness in detecting sophisticated HT threats.

Abstract The reverse osmosis (RO) process is a well-established desalination technology, wherein energy-efficient techniques and advanced process control methods significantly reduce production costs. This study proposes an optimal real-time management method to minimize the total daily operation cost of an RO desalination plant, integrating a storage tank system to meet varying daily freshwater demand. Utilizing the dynamic model of the RO process, a cascade structure with two reinforcement learning (RL) agents, namely the deep deterministic policy gradient (DDPG) and deep Q-Network (DQN), is developed to optimize the operation of the RO plant. The DDPG agent, manipulating the high-pressure pump, controls the permeate flow rate to track a reference setpoint value. Simultaneously, the DQN agent selects the optimal setpoint value and communicates it to the DDPG controller to minimize the plant’s operation cost. Monitoring storage tanks, permeate flow rates, and water demand enables the DQN agent to determine the required amount of permeate water, optimizing water quality and energy consumption. Additionally, the DQN agent monitors the storage tank’s water level to prevent overflow or underflow of permeate water. Simulation results demonstrate the effectiveness and practicality of the designed RL agents.

With the widespread usage of IoT, processing data streams in real-time have become very important. The traditional data-stream processing systems are inefficient in processing big data for detecting anomalies, classifications, clustering, and prediction in real-time using minimal resources. In this paper, we address this limitation by proposing a scalable pipeline for real-time processing of big data streams. Our proposed solution is capable of dynamically managing resources for different components of the pipeline using automatic scaling. The pipeline is containerized and deployed on a Kubernetes cluster. The proposed scalable pipeline is evaluated using a case study of anomaly detection in IoT data. The proposed solution yields a $\times 1.31$ to $\times 2.4$ increase in throughput, and $\times 32$ to $\times 80$ decreased latency compared to the commonly used static resource allocation strategy for data pipelines.

The performance of the same type of cloud resources, such as virtual machines (VMs), varies over time mainly due to hardware heterogeneity, resource contention among co-located VMs, and virtualization overhead. The performance variation can be significant, introducing challenges to learn workload-specific resource provisioning policies to automatically scale the cloud-hosted applications to maintain the desired response time. Moreover, auto-scaling multi-tier applications using minimal resources is even more challenging because bottlenecks may occur on multiple tiers concurrently. In this paper, we address the problem of using performance varying VMs for gracefully auto-scaling a multi-tier application using minimal resources to handle dynamically increasing workloads and satisfy the response time requirements. The proposed system uses a supervised learning method to identify the appropriate resources provisioning for multi-tier applications based on the prediction of the application response time and the request arrival rate. The supervised learning method learns a state transition configuration map which encodes a resource allocation states invariant to the underlying VMs performance variations. This configuration map helps to use performance varying resources in predictive autoscaling method. Our experimental evaluation using a real-world multi-tier web application hosted on a public cloud shows an improved application performance with minimal resources compared to conventional predictive auto-scaling methods.

Effective management of urban traffic is important for any smart city initiative. Therefore, the quality of the sensory traffic data is of paramount importance. However, like any sensory data, urban traffic data are prone to imperfections leading to missing measurements. In this paper, we focus on inter-region traffic data completion. We model the inter-region traffic as a spatiotemporal tensor that suffers from missing measurements. To recover the missing data, we propose an enhanced CANDECOMP/PARAFAC (CP) completion approach that considers the urban and temporal aspects of the traffic. To derive the urban characteristics, we divide the area of study into regions. Then, for each region, we compute urban feature vectors inspired from biodiversity which are used to compute the urban similarity matrix. To mine the temporal aspect, we first conduct an entropy analysis to determine the most regular time-series. Then, we conduct a joint Fourier and correlation analysis to compute its periodicity and construct the temporal matrix. Both urban and temporal matrices are fed into a modified CP-completion objective function. To solve this objective, we propose an alternating least square approach that operates on the vectorized version of the inputs. We conduct comprehensive comparative study with two evaluation scenarios. In the first one, we simulate random missing values. In the second scenario, we simulate missing values at a given area and time duration. Our results demonstrate that our approach provides effective recovering performance reaching 26% improvement compared to state-of-art CP approaches and 35% compared to state-of-art generative model-based approaches.

Safety risk assessment is an essential process to ensure a dependable cyber-physical system (CPS) design. Traditional risk assessment considers only physical failures. For modern CPSs, failures caused by cyber attacks are on the rise. The focus of latest research effort is on safety–security lifecycle integration and the expansion of modeling formalisms for risk assessment to incorporate security failures. The interaction between safety and security lifecycles and its impact on the overall system design, as well as the reliability loss resulting from ignoring security failures, are some of the overlooked research questions. This article addresses these research questions by presenting a new safety design method named cyber layer of protection analysis (CLOPA) that extends the existing layer of protection analysis (LOPA) framework to include failures caused by cyber attacks. The proposed method provides a rigorous mathematical formulation that expresses quantitatively the tradeoff between designing a highly reliable and a highly secure CPS. We further propose a co-design lifecycle process that integrates the safety and security risk assessment processes. We evaluate the proposed CLOPA approach and the integrated lifecycle on a practical case study of a process reactor controlled by an industrial control testbed and provide a comparison between the proposed CLOPA and current LOPA risk assessment practice.

Most cloud computing platforms offer reactive resource auto-scaling mechanisms for dealing with variable traffic patterns to deliver the desired QoS properties while keeping low provisioning costs. However, a range of scenarios have not been fully addressed by the current auto-scaling solutions, particularly dealing with a rapid increase in workload and the risk of thrashing due to frequent workload variations. A reactive system is vulnerable in such conditions. Realizing the full potential of auto-scaling still remains challenging particularly due to the need of accurately estimating the application resource requirements for time-varying workload patterns. In this work, we propose and evaluate a novel method using only application access logs to estimate more accurately the hardware resource demands and application response time. In particular, we propose novel workload latent features which we compute by applying unsupervised learning on the access logs. We use these latent features to estimate the application hardware resource requirements and response time for various workload patterns. We evaluate the proposed method using multiple benchmark web applications and compare it with current state-of-the-art. Extensive experimental evaluations show an excellent performance of our proposed workload latent features in estimating response time, CPU, memory, and bandwidth utilization.

The widespread of smart devices equipped with high sensing capabilities opened the door to the new paradigm of mobile crowdsourcing. This paradigm relies on the crowd contribution and participation to collect data and relevant information. The abstraction of mobile crowdsourcing as a service has become easier and more seamless thanks to the availability, low-cost and fast access to cloud services. In this context, it is important to satisfy a request for a crowdsourced service, at a given time and place, as soon as possible. Nevertheless, maintaining a balance between the supply and demand of crowdsourced services in a geographic area is challenging given the mobility of both service requesters and providers. Motivated by this requirement, we propose a forecasting approach to infer the supply demand gap of crowdsourced services in a given geographic area. Instead of relying on raw data for prediction, we devise a technique to generate predictors from the raw gap data using topological data analysis to exploit the topological and underlying geometric structures. Our forecasting strategy is conducted in a multiview fashion, that is, we devise the historical time horizon into immediate, near and distant time. Then, using topological analysis, we derive three key features: topological similarity, Betti numbers and the Distance To Measure value (DTM). These features, along with additional context information including weather and temperature, are used to infer the supply-demand gap value using state-of-art prediction approach. Our experiments show that the proposed multiview topological analysis is effective for supply-demand prediction with both clean and noisy data.

Containers provide a lightweight runtime environment for microservices applications while enabling better server utilization. Automatic optimal allocation of CPU pins to the containers serving specific workloads can help to minimize the completion time of jobs. Most of the existing state-of-the-art focused on building new efficient scheduling algorithms for placing the containers on the infrastructure, and the resources to the containers are allocated manually and statically. An automatic method to identify and allocate optimal CPU resources to the containers can help to improve the efficiency of the scheduling algorithms. In this article, we introduce a new deep learning-based approach to allocate optimal CPU resources to the containers automatically. Our approach uses the law of diminishing marginal returns to determine the optimal number of CPU pins for containers to gain maximum performance while maximizing the number of concurrent jobs. The proposed method is evaluated using real workloads on a Docker-based containerized infrastructure. The results demonstrate the effectiveness of the proposed solution in reducing the completion time of the jobs by 23% to 74% compared to commonly used static CPU allocation methods.

The insecurity of the Internet-of-Things (IoT) paradigm continues to wreak havoc in consumer and critical infrastructure realms. Several challenges impede addressing IoT security at large, including, the lack of IoT-centric data that can be collected, analyzed and correlated, due to the highly heterogeneous nature of such devices and their widespread deployments in Internet-wide environments. To this end, this paper explores macroscopic, passive empirical data to shed light on this evolving threat phenomena. This not only aims at classifying and inferring Internet-scale compromised IoT devices by solely observing such one-way network traffic, but also endeavors to uncover, track and report on orchestrated "in the wild" IoT botnets. Initially, to prepare the effective utilization of such data, a novel probabilistic model is designed and developed to cleanse such traffic from noise samples (i.e., misconfiguration traffic). Subsequently, several shallow and deep learning models are evaluated to ultimately design and develop a multi-window convolution neural network trained on active and passive measurements to accurately identify compromised IoT devices. Consequently, to infer orchestrated and unsolicited activities that have been generated by well-coordinated IoT botnets, hierarchical agglomerative clustering is deployed by scrutinizing a set of innovative and efficient network feature sets. By analyzing 3.6 TB of recent darknet traffic, the proposed approach uncovers a momentous 440,000 compromised IoT devices and generates evidence-based artifacts related to 350 IoT botnets. While some of these detected botnets refer to previously documented campaigns such as the Hide and Seek, Hajime and Fbot, other events illustrate evolving threats such as those with cryptojacking capabilities and those that are targeting industrial control system communication and control services.

Autoscaling methods are important to ensure response time guarantees for cloud-hosted microservices. Most of the existing state-of-the-art autoscaling methods use rule-based reactive policies with static thresholds defined either on monitored resource consumption metrics such as CPU and memory utilization or application-level metrics such as the response time. However, it is challenging to determine the most appropriate threshold values to minimize resource consumption and performance violations. Whereas, predictive autoscaling methods can help to address these challenges. These methods require considerable time to collect sufficient performance traces representing different resource provisioning possibilities for a target infrastructure to train a useful predictive autoscaling model. In this paper, we tackle this problem by proposing a system that models the response time of microservices through stress testing and then uses a trace-driven simulation to learn a predictive autoscaling model for satisfying response time requirements automatically. The proposed solution reduces the need for collecting performance traces to learn a predictive autoscaling model. Our experimental evaluation on AWS cloud using a microservice under realistic dynamic workloads validates the proposed solution. The validation results show excellent performance to satisfy the response time requirement with only 4.5% extra cost for using the proposed autoscaling method compared to the reactive autoscaling method.

Mobile crowdsourcing has become easier thanks to the widespread of smartphones capable of seamlessly collecting and pushing the desired data to cloud services. However, the success of mobile crowdsourcing relies on balancing the supply and demand by first accurately forecasting spatially and temporally the supply-demand gap, and then providing efficient incentives to encourage participant movements to maintain the desired balance. In this paper, we propose Deep-Gap, a deep learning approach based on residual learning to predict the gap between mobile crowdsourced service supply and demand at a given time and space. The prediction can drive the incentive model to achieve a geographically balanced service coverage in order to avoid the case where some areas are over-supplied while other areas are under-supplied. This allows anticipating the supply-demand gap and redirecting crowdsourced service providers towards target areas. Deep-Gap relies on historical supply-demand time series data as well as available external data such as weather conditions and day type (e.g., weekday, weekend, holiday). First, we roll and encode the time series of supplydemand as images using the Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF) and the Recurrence Plot (REC). These images are then used to train deep Convolutional Neural Networks (CNN) to extract the low and high-level features and forecast the crowdsourced services gap. We conduct comprehensive comparative study by establishing two supply-demand gap forecasting scenarios: with and without external data. Compared to state-of-art approaches, Deep-Gap achieves the lowest forecasting errors in both scenarios.

The Cypher Physical Power Systems (CPPS) became vital targets for intruders because of the large volume of high speed heterogeneous data provided from the Wide Area Measurement Systems (WAMS). The Nonnested Generalized Exemplars (NNGE) algorithm is one of the most accurate classification techniques that can work with such data of CPPS. However, NNGE algorithm tends to produce rules that test a large number of input features. This poses some problems for the large volume data and hinders the scalability of any detection system. In this paper, we introduce VHDRA, a Vertical and Horizontal Data Reduction Approach, to improve the classification accuracy and speed of the NNGE algorithm and reduce the computational resource consumption. VHDRA provides the following functionalities: (1) it vertically reduces the dataset features by selecting the most significant features and by reducing the NNGE’s hyperrectangles. (2) It horizontally reduces the size of data while preserving original key events and patterns within the datasets using an approach called STEM, State Tracking and Extraction Method. The experiments show that the overall performance of VHDRA using both the vertical and the horizontal reduction reduces the NNGE hyperrectangles by 29.06%, 37.34%, and 26.76% and improves the accuracy of the NNGE by 8.57%, 4.19%, and 3.78% using the Multi-, Binary, and Triple class datasets, respectively.

Accurate estimation of data center resource utilization is a challenging task due to multi-tenant co-hosted applications having dynamic and time-varying workloads. Accurate estimation of future resources utilization helps in better job scheduling, workload placement, capacity planning, proactive auto-scaling, and load balancing. The inaccurate estimation leads to either under or over-provisioning of data center resources. Most existing estimation methods are based on a single model that often does not appropriately estimate different workload scenarios. To address these problems, we propose a novel method to adaptively and automatically identify the most appropriate model to accurately estimate data center resources utilization. The proposed approach trains a classifier based on statistical features of historical resources usage to decide the appropriate prediction model to use for given resource utilization observations collected during a specific time interval. We evaluated our approach on real datasets and compared the results with multiple baseline methods. The experimental evaluation shows that the proposed approach outperforms the state-of-the-art approaches and delivers 6% to 27% improved resource utilization estimation accuracy compared to baseline methods.

In this paper, we consider the data placement problem in the new generation tiered cloud storage services offering hot and cool tiers that are characterized by differentiated Quality of Service (i.e, access latency, availability and throughput) and the corresponding storage and access costs. Given a sequence of read and write requests for an object, we propose an optimal off-line dynamic programming based algorithm to determine the optimal placement of an object in the hot or cool tier and the potential transfer of the object between the tiers in order to minimize the monetary cost comprised of storage and access costs. Additionally, we propose two practical online object placement algorithms that assume no knowledge of future data access. The first online cost optimization algorithm uses no replication (NR) and initially places the object in the hot tier then based on read/write access pattern it may decide to move it to the cool tier to optimize the storage service cost. The second algorithm with replication (WR) initially places the object in the cool tier then it replicates it in the hot tier upon receiving read/write requests. Using a real Twitter workload and a 2-tier storage service pricing, the experimental evaluation shows that the proposed algorithms yield significant cost savings compared to storing data in the hot tier all the time.

Software as a service is a well accepted software deployment and distribution model that has grown exponentially in the last few years. One of the biggest benefits of SaaS is the automated composition of different services in a composite system. It allows users to automatically find and bind these services (to maximize the productivity), meeting both functional and non-functional requirements. In this paper, we present a framework for modeling the dependency relationships among different Quality of Service parameters of the component services. Our proposed approach considers the different invocation patterns of component services, and presents a service composition framework to model the dependencies and uses the global QoS for service selection. We evaluate the efficiency of our proposed technique on the WSDream-QoS Dataset [1] .

IaaS cloud resources are offered by cloud providers using various pricing schemes such as reservation, scheduled, on-demand and spot pricing. Such pricing models suffer from either wasteful payment in case of underutilization of reserved/scheduled instances or volatile prices, particularly for spot instances. This paper explores using financial options for pricing IaaS cloud resources. We advocate using options to complement existing pricing models and to provide a cheaper way to gain access to resources without reserving them in advance while avoiding prices volatility of spot instances. An option provides the cloud consumer the right, but not the obligation, to buy or sell a cloud resource at a fixed price during a predetermined period. That means that the maximum loss associated with an option is limited to the premium paid. Various options pricing methods were studied and compared. Using extensive simulation, we show the practical utility of options particularly for handling the unpredictable workload and their efficacy in terms of cost savings.

We propose a new framework for social-sensor cloud services selection based on spatio-textual correlation between user's query and service. The proposed research defines a formal social-sensor cloud service model that abstracts the functional and non-functional aspects of social-sensor data on the cloud in terms of spatio-temporal, textual and quality of service parameters. Proposed framework is a 4-stage filtering algorithm, to select social-sensor cloud services based on user query and quality of service demands. 4-stage filtering is based on spatial correlation, textual correlation, visual features and quality of service parameters. Analytical results are presented to show the performance of the proposed approach.

We propose a 'Sentiment Analysis as a Service' (SAaaS) framework that abstracts sentiments from social information services, analyses and transforms into useful information. We propose a dynamic service composition mechanism for sentiment analysis based on the social information service classification. We also propose a new quality model to assess the quality of social information services. We use social media based public health surveillance as a motivating scenario. In particular, we focus on the spatio-temporal properties of social media users' sentiments to identify the locations of disease outbreaks. Experiments are conducted on the real-world datasets. Analytical results preliminarily show the performance of our proposed approach.

We investigate the use of subjective metrics in social media to evaluate cloud service performance in the market. We first examine the subjective factors that drive cloud consumers to/from purchasing cloud services. These include the ability to achieve greater scalability, security concerns, etc. according to several industry surveys. We then analyse the correlation between the consumers' perception on those factors and the cloud market revenue growth. This paper identifies the unique subjective metrics that are indicative of cloud service performance from the market perspective. The cloud consumers' perception is sourced from several particular social media using sentiment analysis techniques. We focus on consumers' perception on a leading cloud provider that holds the majority of the cloud market share. We find that subjective metrics are empirically proved to be applicable in evaluating the performance of cloud services in the market.

The prediction of cloud consumer resource needs is a vital step for several cloud deployment applications such as capacity planning, workload management, and dynamic allocation of cloud resources. In this paper, we develop a new prediction model for predicting cloud consumer resource needs. The new model uses a new hybrid prediction approach that combines the Multiple Support Vector Regression (MSVR) model and the Autoregressive Integrated Moving Average (ARIMA) model to predict with higher accuracy the resource needs of a cloud consumer in terms of CPU, memory, and disk storage utilization. The new model is also able to predict the response time and throughput which in turn enable the cloud consumers to make a better scaling decision. The new model elucidated a better prediction accuracy than the current prediction models. In terms of CPU utilization prediction, it outperforms the accuracy of the existing cloud consumer prediction models that uses Linear Regression, Neural Network, and Support Vector Machines approaches by 72.66%, 44.24%, and 56.78% respectively according to MAPE and 56.95%, 80.42%, and 63.86% according to RMSE. The analysis, architecture, and experiment results of the new model are discussed in details in this paper.

In order to enable a secure interaction between dynamically discovered software services and the client’s application in a cooperative information system such as service oriented system, one of the pre-requisites is the reconciliation of service-specific security policies of all stakeholders. Existing service discovery research does not address the issue of enormous search space in finding security-aware services based on preferred security policy alternatives of the client of software services. In this paper, we propose an answer set programming (ASP) approach, drawn from the field of artificial intelligence (AI), to explore a viable solution of finding security-aware services for the client. We argue that the ASP approach can significantly reduce the search space and achieve great performance gains. We use ASP to: (i) specify security policies including expressing service-specific security preference weighting and importance scoring in quantifiable terms; and (ii) reason about the compliance between the security policies of the client and the software service.

Security is an important element in Cloud computing. Intruders may exploit clouds for their advantage. This paper presents a cost-aware model for risk mitigation in cloud computing systems. The proposed model is integrated with our Autonomous Cloud Intrusion detection Framework, ACIDF, which continuously monitors and analyzes system events and computes security and risk parameters to provide risk assessment capabilities with a scalable and elastic architecture. The proposed model helps ACIDF to select the appropriate response to mitigate the detected attacks by considering the costs of deploying a response and the costs of damage caused by a non-responded attack. The proposed model reduces the risk by 18.9%. This paper describes the proposed model functions and advantages.

Secure outsourcing of computation has gained importance with the proliferation of cloud services. However, existing outsourcing protocol specification languages are mainly suitable for secure multi-party computation. They offer limited support for secure outsourcing of computation of large datasets in cloud computing environments. This paper presents a model driven approach to define then coordinate the execution of secure outsourcing protocols. First we present the details of our Outsourcing Protocol Definition Language (OPDL) used to define a machine-process able protocols in an abstract and declarative way while leaving the implementation details to the underlying runtime components. The proposed language aims to simplify the design of these protocols while allowing their verification and the generation of cloud services composition to coordinate the protocol execution. We evaluated the expressiveness of OPDL by using it to define a set of representative secure outsourcing protocols from the literature.

Cloud computing has become a major building block for Web applications where the service providers provide the costumers with Web services performing certain tasks so the system developers can use these services instead of implementing them. Service computing follows the "pay-per-use" as a model for pricing. Due to the variety of service providers, we have many different prices for services and these services have different performance characteristics. The problem here is how to find the best services in terms of cost and performance and is it feasible to switch from one service provider to another. In this paper we propose Wcost, a scheme to resolve this problem based on P-OCEA algorithm which is a combination of P-Optimality and Genetic algorithms which result in more efficient solution than Genetic Algorithm approaches. Our proposed scheme includes a flexible and easily customizable objective function that is suitable for different types of Web services to be composed.

This paper presents a model driven approach to define then coordinate the execution of protocols for secure outsourcing of computation of large datasets in cloud computing environments. First we present our Outsourcing Protocol Definition Language (OPDL) used to define a machine-processable protocols in an abstract and declarative way while leaving the implementation details to the underlying runtime components. The proposed language aims to simplify the design of these protocols while allowing their verification and the generation of cloud services composition to coordinate the protocol execution. We evaluated the expressiveness of OPDL by using it to define a set of representative secure outsourcing protocols from the literature.

The extensive use of virtualization in implementing cloud infrastructure brings unrivaled security concerns for cloud tenants or customers and introduces an additional layer that itself must be completely configured and secured. Intruders can exploit the large amount of cloud resources for their attacks. Most of the current security technologies do not provide the essential security features for cloud systems such as early warnings about future ongoing attacks, autonomic prevention actions, and risk measure. This paper discusses the integration of these three features to our Autonomic Cloud Intrusion Detection Framework (ACIDF). The early warnings are signaled through a new finite State Hidden Markov prediction model that captures the interaction between the attackers and cloud assets. The risk assessment model measures the potential impact of a threat on assets given its occurrence probability. The estimated risk of each security alert is updated dynamically as the alert is correlated to prior ones. This enables the adaptive risk metric to evaluate the cloud's overall security state. The prediction system raises early warnings about potential attacks to the autonomic component, controller. Thus, the controller can take proactive corrective actions before the attacks pose a serious security risk to the system. According to our experiments, both risk metric and prediction model have successfully signaled early warning alerts 39.6 minutes before the launching of the LLDDoS1.0 attack. This gives the system administrator or an autonomic controller ample time to take preventive measures.

Cloud computing significantly increased the security threats because intruders can exploit the large amount of cloud resources for their attacks. However, most of the current security technologies do not provide early warnings about such attacks. This paper presents a Finite State Hidden Markov prediction model that uses an adaptive risk approach to predict multi-staged cloud attacks. The risk model measures the potential impact of a threat on assets given its occurrence probability. The attacks prediction model was integrated with our autonomous cloud intrusion detection framework (ACIDF) to raise early warnings about attacks to the controller so it can take proactive corrective actions before the attacks pose a serious security risk to the system. According to our experiments on DARPA 2000 dataset, the proposed prediction model has successfully fired the early warning alerts 39.6 minutes before the launching of the LLDDoS1.0 attack. This gives the auto response controller ample time to take preventive measures.

This paper introduces an autonomic model-based cyber security management approach for the Internet of Things (IoT) ecosystems. The approach aims at realizing a self-protecting system, which has the ability to autonomously estimate, detect, and react to cyber attacks at an early stage. Our approach integrates various model-based techniques including: 1) real-time estimation and baseline security controls to predict and eliminate potential cyber attacks; 2) data analysis to identify and classify attacks; and 3) a multicriteria optimization method to select the optimal active response for deploying countermeasures while maintaining system functions. The prototype framework has been developed with a master controller virtual machine, which can be configured for various platforms. Experimental results demonstrated the effectiveness of this proposed approach in protecting a Web-based application against known and unknown attacks with little or no human intervention.

This paper reports the design of a cloud-based service for coordinating secure outsourcing of storage and computation of scientific data, particularly matrices. While this service may support different secure outsourcing protocols and mechanisms (e.g. homomorphic encryption, secret sharing and randomization), we hide all the complexity from end-users and move it to a middleware broker. The broker manages the communication between the client and one or more clouds. The requests submitted by users are automatically translated into WS-BPEL workflows and then executed by the broker workflow engine to coordinate the control flow and the data flow for outsourcing of matrix operations. We detail the architecture of our framework and the design of its key components. Our work facilitates real-world and practical deployment of recently proposed secure outsourcing protocols.

Acquisition of new vocabulary is an important element for language learning but it requires repeated and varied exposure to the new words and their usage. This paper reports the experience of designing and developing a game-based micro-learning platform (named LingoSnacks) for interactive learning of Arabic vocabulary. The LingoSnacks learning platform provides an environment of authoring learning content and delivering it to the learner in game-like interactive learning activities. Empirical testing results from students who used LingoSnacks indicate that the participants were able to increase their rate of vocabulary acquisition as the number of new vocabulary that they can recognize, recall and retain was significantly higher that participants who just used conventional lessons in a classroom.

MASC (manageable and adaptive service compositions) is a policy-based middleware for monitoring of Web service compositions and their dynamic adaptation to various runtime changes. MASC policies are described in our new WS-Policy extension called WS-Policy4MASC. Compared with recent related works, MASC has several distinctive characteristics, such as coordination of adaptation on the SOAP messaging layer and the business process orchestration layer, use of both technical and business metrics for adaptation decisions, and extending the power and flexibility of the new Microsoft .NET 3.0 platform. In this paper, we focus on MASC support for adaptation to address business exceptions and manage runtime faults. For example, a sub-process (or an activity) can be added, deleted, replaced, skipped, or retried. We have been implementing a MASC proof-of-concept prototype and evaluating it on adaptation scenarios from a stock trading case study. Our performance studies of the prototype indicate that overheads introduced by MASC are acceptable.

Web services are gaining acceptance as a standards-based approach for integrating loosely coupled services often distributed over a network. Hence, achieving high levels of reliability and availability in spite of service or infrastructure failures poses unique set of challenges. However, Web services middleware provide limited constructs for specifying faults detection and recovery actions. Additionally, faults-handling logic often gets scattered and tangled with the service logic. Consequently, this negatively impacts maintainability and adaptability. To address these requirements for reliable and fault tolerant Web services execution, we propose set extensible recovery policies to declaratively specify how to handle and recover from typical faults in Web services composition. The identified constructs were incorporated into a lightweight service management middleware named MASC (manageable and adaptive service composition) to transparently enact the fault management policies and facilitate the monitoring, configuration and control of managed services. Several experimental results with a service based supply chain management system illustrate the effectiveness of our approach to providing reliable and uninterrupted services

The variation of contexts in which a Web service could be used and the resulting variation in quality of service (QoS) expectations makes a clear case for further research to extend Web services management platforms with more sophisticated control mechanisms to cater for differentiated service offerings. However, most Web services platforms are based on a best-effort model, which treats all requests uniformly, without any type of service differentiation or prioritization. This paper presents WS-DiffServ, a service differentiation middleware based on prioritization which leverages service requestor profile to classify service requests. We first explore the typical generic requirements of a differential QoS support in Web services management. We then present in detail the design of a priority based differentiated responsiveness platform for Web services

As Web services start to be deployed for mission-critical applications and for e-business scenarios, higher quality of service (QoS) and continuous service delivery become a critical issue to ensure high availability and reliability in spite of the failure or unavailability of the participating services or networks. These challenges call for vast improvements in the Web services containers and the mediation infrastructure. To address these requirements, we propose Web services message bus (wsBus), a lightweight service-oriented middleware for reliable and fault tolerant Web services interactions. This paper first discusses wsBus architecture and features and then it reports some experimental results to illustrate the effectiveness of wsBus in adding reliable and uninterrupted services to a supply chain management system.