Abstract: Mobile malware is a malicious code specifically designed to target mobile devices to perform multiple types of fraud. The number of attacks reported each day is increasing constantly and is causing an impact not only at the end-user level but also at the network operator level. Malware like FluBot contributes to identity theft and data loss but also enables remote Command & Control (C2) operations, which can instrument infected devices to conduct Distributed Denial of Service (DDoS) attacks. Current mobile device-installed solutions are not effective, as the end user can ignore security warnings or install malicious software. This article designs and evaluates MONDEO-Tactics5G-a multistage botnet detection mechanism that does not require software installation on end-user devices, together with tactics for 5G network operators to manage infected devices. We conducted an evaluation that demonstrates high accuracy in detecting FluBot malware, and in the different adaptation strategies to reduce the risk of DDoS while minimising the impact on the clients' satisfaction by avoiding disrupting established sessions.

Abstract: The ever-growing complexity of software systems makes it increasingly challenging to foresee at design time all interactions between a system and its environment. Most self-adaptive systems trigger adaptations through operators that are statically confi gured for specifi c environment and system conditions. However, in the occurrence of uncertain conditions, self-adaptive decisions may not be eff ective and might lead to a disruption of the desired non-functional attributes. To address this, we propose an approach that improves the planning stage by predicting the outcome of each strategy. In detail, we automatically derive a stochastic model from a formal architecture description of the managed system with the changes imposed by each strategy. Such information is used to optimize the self-adaptation decisions to fulfill the desired quality goals. To assess the effectiveness of our approach we apply it to a cloud-based news system and predicted the reliability for each possible adaptation strategy. The results obtained from our approach are compared to a representative static planning algorithm as well as to an oracle that always makes the ideal decision. Experiments show that our method improves both availability and cost when compared to the static planning algorithm, while being close to the oracle. Our approach may therefore be used to optimize self-adaptation planning.

Abstract: An important domain for autonomic systems is the area of ubiquitous computing: users are increasingly surrounded by technology that is heterogeneous, pervasive, and variable. In this paper we describe our work in developing self-adapting computing infrastructure that automates the configuration and reconfiguration of such environments. Focusing on the engineering issues of self-adaptation in the presence of heterogeneous platforms, legacy applications, mobile users, and resource variable environments, we describe a new approach based on the following key ideas: (a) Explicit representation of user tasks allows us to determine what service qualities are required of a given configuration; (b) Decoupling task and preference specification from the lower level mechanisms that carry out those preferences provides a clean engineering separation of concerns between what is needed and how it is carried out; and (c) Efficient algorithms allow us to calculate in real time near-optimal resource allocations and reallocations for a given task.

Abstract: An emerging trend in the engineering of complex systems is the use of component integration frameworks. Such a framework prescribes an architectural design that permits flexible composition of third-party components into applications. A good example is Sun Microsystems' Enterprise JavaBeans (EJB) framework, which supports object-oriented, distributed, enterprise-level applications, such as account management systems. One problem with frameworks like EJB is that they are documented informally, making it difficult to understand precisely what is provided by the framework, and what is required to use it. We believe formal specification can help, and in this paper show how a formal architectural description language can be used to describe and provide insight into such frameworks.

Abstract: This chapter introduces a new way of thinking about software systems for supporting the activities of end-users. In this approach, models of user activities are promoted to first class entities, and software systems are assembled and configured dynamically based on activity models. This constitutes a fundamental change of perspective over traditional applications: activities take the main stage and may be long-lived, whereas the agents that carry them out are plentiful and interchangeable. The core of the chapter describes a closed-loop control design that enables activity-oriented systems to become self-aware and self-configurable, and to adapt to dynamic changes both in the requirements of user activities and in the environment resources. The chapter discusses how that design addresses challenges such as user mobility, resolving conflicts in accessing scarce resources, and robustness in the broad sense of responding adequately to user expectations, even in unpredictable situations, such as random failures, erroneous user input, and continuously changing resources. The chapter further summarizes challenges and ongoing work related to managing activities where humans and automated agents collaborate, human-computer interactions for managing activities, and privacy and security aspects.

Abstract: The goal of this roadmap paper is to summarize the state-of-the-art and identify research challenges when developing, deploying and managing self-adaptive software systems. Instead of dealing with a wide range of topics associated with the field, we focus on four essential topics of self-adaptation: design space for self-adaptive solutions, software engineering processes for self-adaptive systems, from centralized to decentralized control, and practical run-time verification & validation for self-adaptive systems. For each topic, we present an overview, suggest future directions, and focus on selected challenges. This paper complements and extends a previous roadmap on software engineering for self-adaptive systems published in 2009 covering a different set of topics, and reflecting in part on the previous paper. This roadmap is one of the many results of the Dagstuhl Seminar 10431 on Software Engineering for Self-Adaptive Systems, which took place in October 2010.

Abstract: This paper presents a framework for engineering resource-adaptive software targeted at small mobile devices. Rather than building a solution from scratch, we extend and integrate existing work on software infrastructures for ubiquitous computing, and on resource-adaptive applications. This paper addresses two research questions: first, is it feasibility to coordinate resource allocation and adaptation policies among several applications in a way that is both effective and efficient. And second, can end-users understand and control such adaptive behaviors dynamically, depending on user-defined goals for each activity. The evaluation covered both the systems and the usability perspectives, the latter by means of a user study. The contributions of this work are: first, a set of design guidelines, including APIs for integrating new applications; second, a concrete infrastructure that implements the guidelines. And third, a way to model quality of service tradeoffs based on utility theory, which our research indicates end-users with diverse backgrounds are able to leverage for guiding the adaptive behaviors towards activity-specific quality goals.

Abstract: This paper presents a framework for engineering resource-adaptive software systems targeted at small mobile devices. The proposed framework empowers users to control tradeoffs among a rich set of servicespecific aspects of quality of service. After motivating the problem, the paper proposes a model for capturing user preferences with respect to quality of service, and illustrates prototype user interfaces to elicit such models. The paper then describes the extensions and integration work made to accommodate the proposed framework on top of an existing software infrastructure for ubiquitous computing. The research question addressed here is the feasibility of coordinating resource allocation and adaptation policies in a way that end-users can understand and control in real time. The evaluation covered both systems and the usability perspectives, the latter by means of a user study. The contributions of this work are: first, a set of design guidelines for resource-adaptive systems, including APIs for integrating new applications; second, a concrete infrastructure that implements the guidelines. And third, a way to model quality of service tradeoffs based on utility theory, which our research indicates end-users with diverse backgrounds are able to leverage for guiding the adaptive behaviors towards activity-specific quality goals.

Abstract: Most pervasive computing technologies focus on helping users with computer-oriented tasks. In this NSF-funded project, we instead focus on using computers to support user-centered “activities” that normally do not involve the use of computers. Examples may include everyday tasks around such as answering the doorbell or doing laundry. A focus on activity-based computing brings to the foreground a number of unique challenges. These include activity definition and representation, system design, interfaces for managing activities, and ensuring robust operation. Our project focuses on the first two challenges.

Abstract: This paper introduces an architectural style for enabling end-users to quickly design and deploy software systems in domains characterized by highly personalized and dynamic requirements.

The style offers an intuitive metaphor based on boxes, pipes, and wires, but retains enough preciseness that systems can be automatically assembled and dynamically reconfigured based on uDesign descriptions. uDesign was primarily motivated and validated within monitoring and control applications for smart spaces, but we envision possible extensions to other domains.

Our contribution differs from early attempts at end-user programming in the level of abstraction, software architecture rather than programming, and in the subject of description: run-time rather than code structures. To validate the approach, the paper presents (a) two case studies, one in health care and one in home security, (b) the formal semantics of uDesign’s primitives, and (c) a mapping of those primitives to an existing software infrastructure: the Aura infrastructure.

Abstract: Self-adapting systems based on multiple concurrent applications must decide how to allocate scarce resources to applications and how to set the quality parameters of each application to best satisfy the user. Past work has made those decisions with analytic models that used current resource availability information: they react to recent changes in resource availability as they occur, rather than anticipating future availability. These reactive techniques may model each local decision optimally, but the accumulation of decisions over time nearly always becomes less than optimal.

 In this paper, we propose an approach to self adaptation, called anticipatory configuration that leverages

predictions of future resource availability to improve utility for the user over the duration of the task. The approach solves the following technical challenges: (1) how to express resource availability prediction, (2) how to combine prediction from multiple sources, and (3) how to leverage predictions continuously while improving utility to the user. Our experiments show that when certain adaptation operations are costly, anticipatory configuration provides better utility to the user than reactive configuration, while being comparable in resource demand.

Abstract: We propose an improved approach to dynamic configuration of resource-aware applications. The new anticipatory model of configuration maximizes utility based on three inputs: user preferences, application capability profiles, and resource availability. In this respect, the proposed model is similar to a model of configuration described in [2]. However, the latter addresses the dynamic nature of the problem by reacting to changes (such as decrease in resource availability), and maximizes the utility in a point-wise manner. The newly proposed anticipatory approach explicitly models the duration of the task and leverages possible information about the future (such as stochastic resource availability over the expected duration of the task). We expect that the anticipatory model will improve user's utility, conserve scarce resources, and reduce the amount of disruption to the user resulting from changes when compared to the reactive model. However, the optimization problem underlying the anticipatory model is computationally more difficult than the problem underlying the reactive model. We would like to investigate if the anticipatory approach is feasible and efficient in practice while delivering the above-mentioned improvements. In this paper, we carefully state the model of anticipatory configuration, highlight the sources of complexity in the problem, propose an algorithm to the anticipatory configuration problem, and provide a roadmap for research.

Abstract: Computers support more and more tasks in the personal and professional activities of users. Such user tasks increasingly span large periods of time and many locations across the enterprise space and beyond. Recently there has been a growing interest in developing applications that can cope with the specific environmental conditions at each location, and adapt to dynamic changes in system resources. However, in a given situation there may be many possible configuration solutions, and an awareness of the user's intent for each task is a critical element in knowing which one to pick. In this paper, we discuss the limitations of building such awareness into applications, and propose to factor the awareness of user tasks into a common software layer. That however, brings up the problem of coordinating the system-wide adaptation performed by such a layer with fine-grain adaptation performed by resource-aware applications. We summarize the main features of an architectural framework that incorporates such a layer, and distill some of the lessons learned in implementing the framework.

Abstract: Recently there has been increasing interest in developing systems that can adapt dynamically to cope with changing environmental conditions and unexpected system errors. Most efforts for achieving self-adaptation have focused on the mechanisms for detecting opportunities for improvement and then taking appropriate action. However, such mechanisms beg the question: what is the system trying to achieve? In a given situation there may be many possible adaptations, and knowing which one to pick is a difficult question. In this paper we advocate the use of explicit representation of user task as a critical element in addressing this missing link.

Abstract: An important emerging requirement for computing systems is the ability to adapt at run time, taking advantage of local computing devices, and coping with dynamically changing resources. Three specific technical challenges in satisfying this requirement are to (1) select an appropriate set of applications or services to carry out a user s task, (2) allocate (possibly scarce) resources among those applications, and (3) reconfigure the applications or resource assignments if the situation changes. In this paper we show how to provide a shared infrastructure that automates configuration decisions given a specification of the user s task. The heart of the approach is an analytical model and an efficient algorithm that can be used at run time to make near-optimal (re)configuration decisions. We validate this approach both analytically and by applying it to a representative scenario.

Abstract: Ubiquitous computing poses a number of challenges for software architecture. One of the most important is the ability to design software systems that accommodate dynamically-changing resources. Resource variability arises naturally in a ubiquitous computing setting through user mobility (a user moves from one computing environment to another), and through the need to exploit time-varying resources in a given environment (such as wireless bandwidth). Traditional approaches to handling resource variability in applications attempt to address the problem by imposing uniformity on the environment. We argue that those approaches are inadequate, and describe an alternative architectural framework that is better matched to the needs of ubiquitous computing. A key feature of the architecture is that user tasks become first class entities. User proxies, or Auras, use models of user tasks to set up, monitor and adapt computing environments proactively. The architectural framework has been implemented and currently being used as a central component of Project Aura, a campus-wide ubiquitous computing effort.

Abstract: An increasingly important requirement for software systems is the capability to adapt at run time in order to accommodate varying resources, system errors, and changing requirements. For such self-repairing systems, one of the hard problems is determining when a change is needed, and knowing what kind of adaptation is required. Recently several researchers have explored the possibility of using architectural models as a basis for run time monitoring, error detection, and repair. Each of these efforts, however, has demonstrated the feasibility of using architectural models in the context of a specific style. In this paper we show how to generalize these solutions by making architectural style a parameter in the monitoring/repair framework and its supporting infrastructure. The value of this generalization is that it allows one to tailor monitoring/ repair mechanisms to match both the properties of interest (such as performance or security), and the available operators for run time adaptation.

Abstract: An important requirement for pervasive computing systems is the ability to adapt at runtime to handle varying resources, user mobility, changing user needs, and system faults. In this paper we describe an approach in which dynamic adaptation is supported by the use of software architectural models to monitor an application and guide dynamic changes to it. The use of externalized models permits one to make reconfiguration decisions based on a global per-spective of the running system, apply analytic models to determine correct re-pair strategies, and gauge the effectiveness of repair through continuous system monitoring. We illustrate the application of this idea to pervasive computing systems, focusing on the need to adapt based on performance-related criteria and models.

Abstract: Advances in computing and networking are prompting users to change their expectations about the availability of computing. Instead of making primary use of a single machine, users may expand their computer-supported tasks across multiple locations, and they may work on some tasks for days or even months. It is well known that such tasks typically involve several applications and information resources, making it a chore to rebuild the state of devices and software for resuming a task interrupted somewhere else or sometime ago. Unfortunately, current systems offer little support for scaling task management in space and in time, and consequently users are torn between taking advantage of increasingly pervasive computing systems, and the price (in attention and skill) that they have to pay for using them. This dissertation describes a new approach to the scalability of task management in space, across heterogeneous environments, and in time, allowing users to recover tasks interrupted long ago. The approach is based on high-level models of what users need from the computing environment for each of their tasks. Such models are exploited at run-time by an infrastructure that automatically configures the computing environment, on demand, on behalf of users. We present an architectural framework that grounds our approach, and that embodies new system design principles that hold independently of the particular infrastructure implementing the framework. As part of the framework, we present a utility-theoretic model that enables finding the best match between user needs and the capabilities and resources in the environment. We evaluate our research from three perspectives. First, from a user’s perspective, we validate that the infrastructure: (a) delivers the capabilities for scaling task management in space and in time; (b) that it reconciles the competing requirements of sparing users from routine configuration chores, while enabling them to take full advantage of the surrounding computing environments; and (c) that it is usable by non-experts. Second, from a software architect’s perspective, we evaluate the benefits and limitations of the architectural framework supporting our approach. And third, from a systems perspective, we validate that the infrastructure exhibits a performance that makes it usable on a daily basis.

Abstract: Addressing resource variation plays an increasingly important role in engineering today’s software sys- tems. Research in resource-adaptive applications takes an important step towards addressing this problem. However, existing solutions stop short of addressing the fact that different user tasks often have specific goals of quality of service, and that such goals often entail multiple aspects of quality of service. This paper presents a framework for engineering software systems capable of adapting to resource variations in ways that are specific to the quality goals of each user task. For that, users are empowered to specify their task-specific preferences with respect to multiple aspects of quality of service. Such prefer- ences are then exploited to both coordinate resource usage across the applications supporting the task, and to dynamically control the resource adaptation polices of those applications. A user study validates that non-expert users can use our framework to successfully control the behavior of such adaptive systems.

Abstract: Computing environments of the future should enable mobile users to take full advantage of the computing capabilities available at each location, while allowing them to focus on their real tasks, rather than being distracted by dealing with the configuration and reconfiguration of computer systems to support those tasks. The Aura infrastructure performs automatic configuration and reconfiguration of Ubicomp environments, according to the user s task and intent. This report describes the software architecture of the Aura infrastructure, and discusses the underlying rational. It describes the architecture from a layered perspective, detailing the partition of responsibility and shared assumptions, as well as from a component-connector perspective, detailing the protocols of interaction between the components (APIs and sequencing). The contents and format of the exchanged messages is extensively discussed, as well as the details pertaining service interconnection and decomposition. This report proposes a utility-based approach for modeling user preferences, and details how such models can be exploited for both coarse-grain automatic (re)configuration, and fine-grain adaptation to resource change.

Abstract: An important issue for software system development is the documentation of architecture designs. In this report, we describe techniques for the architectural documentation of software-based systems in the context of development processes that use UML for software design. The architectural documentation is organized in four kinds of views: problem domain view, code view, run-time view and deployment view. We examine JavaPhoneTM as a case study to illustrate the approach: what kinds of information are provided in each kind of view, what forms of notation should be used, what are their limitations, and what uses can be made of this documentation.