Computer Networks Security Models - A New Approach for Denial-of-Services Attacks Mitigation

Computer networks are a critical factor for the performance of a modern company. Managing networks is as important as managing any other aspect of the company’s performance and security. There are many tools and appliances for monitoring the traffic and analyzing the network flow security. They use different approaches and rely on a variety of characteristics of the network flows. Network researchers are still working on a common approach for security baselining that might enable early watch alerts. This research focuses on the network security models, particularly the Denial-of-Services (DoS) attacks mitigation, based on a network flow analysis using the flows measurements and the theory of Markov models. The content of the paper comprises the essentials of the author’s doctoral thesis.

Extending Object-Oriented Network Protocols via alternative Transportation Bindings (Web Services over XMPP)

Distributed and/or composite web applications are driven by intercommunication via web services, which employ application-level protocols, such as SOAP. However, these protocols usually rely on the classic HTTP for transportation. HTTP is quite efficient for what it does — delivering web page content, but has never been intended to carry complex web service oriented communication. Today there exist modern protocols that are much better fit for the job. Such a candidate is XMPP. It is an XML-based, asynchronous, open protocol that has built-in security and authentication mechanisms and utilizes a network of federated servers. Sophisticated asynchronous multi-party communication patterns can be established, effectively aiding web service developers. This paper’s purpose is to prove by facts, comparisons, and practical examples that XMPP is not only better suited than HTTP to serve as middleware for web service protocols, but can also contribute to the overall development state of web services.

Self-adaptive volunteered services composition through stimulus-and time-awareness

Volunteered Service Composition (VSC) refers to the process of composing volunteered services and resources. These services are typically published to a pool of voluntary resources. Selection and composition decisions tend to encounter numerous uncertainties: service consumers and applications have little control of these services and tend to be uncertain about their level of support for the desired functionalities and non-functionalities. In this paper, we contribute to a self-awareness framework that implements two levels of awareness, Stimulus-awareness and Time-awareness. The former responds to basic changes in the environment while the latter takes into consideration the historical performance of the services. We have used volunteer service computing as an example to demonstrate the benefits that self-awareness can introduce to self-adaptation. We have compared the Stimulus-and Time-awareness approaches with a recent Ranking approach from the literature. The results show that the Time-awareness level has the advantage of satisfying higher number of requests with lower time cost.

Meta level component-based framework for distributed computing systems

Adaptability for distributed object-oriented enterprise frameworks in multimedia technology is a critical mission for system evolution. Today, building adaptive services is a complex task due to lack of adequate framework support in the distributed computing systems. In this paper, we propose a Metalevel Component-Based Framework which uses distributed computing design patterns as components to develop an adaptable pattern-oriented framework for distributed computing applications. We describe our approach of combining a meta-architecture with a pattern-oriented framework, resulting in an adaptable framework which provides a mechanism to facilitate system evolution. This approach resolves the problem of dynamic adaptation in the framework, which is encountered in most distributed multimedia applications. The proposed architecture of the pattern-oriented framework has the abilities to dynamically adapt new design patterns to address issues in the domain of distributed computing and they can be woven together to shape the framework in future. © 2011 Springer Science+Business Media B.V.

Benchmarking of distributed computing engines spark and GraphLab for big data analytics

In this paper we evaluate and compare two representativeand popular distributed processing engines for large scalebig data analytics, Spark and graph based engine GraphLab. Wedesign a benchmark suite including representative algorithmsand datasets to compare the performances of the computingengines, from performance aspects of running time, memory andCPU usage, network and I/O overhead. The benchmark suite istested on both local computer cluster and virtual machines oncloud. By varying the number of computers and memory weexamine the scalability of the computing engines with increasingcomputing resources (such as CPU and memory). We also runcross-evaluation of generic and graph based analytic algorithmsover graph processing and generic platforms to identify thepotential performance degradation if only one processing engineis available. It is observed that both computing engines showgood scalability with increase of computing resources. WhileGraphLab largely outperforms Spark for graph algorithms, ithas close running time performance as Spark for non-graphalgorithms. Additionally the running time with Spark for graphalgorithms over cloud virtual machines is observed to increaseby almost 100% compared to over local computer clusters.

Mobile bluetooth-based multi-player game development in ubiquitous computing

This paper describes the use of Bluetooth and Java-Based technologies in developing a multi-player mobile game in ubiquitous computing, which strongly depends on automatic contextual reconfiguration and context-triggered actions. Our investigation focuses on an extended form of ubiquitous computing which game software developers utilize to develop games for players. We have developed an experimental ubiquitous computing application that provides context-aware services to game server and game players in a mobile distributed computing system. Obviously, contextual services provide useful information in a context-aware system. However, designing a context-aware game is still a daunting task and much theoretical and practical research remains to be done to reach the ubiquitous computing era. In this paper, we present the overall architecture and discuss, in detail, the implementation steps taken to create a Bluetooth and Java based context-aware game. We develop a multi-player game server and prepare the client and server codes in ubiquitous computing, providing adaptive routines to handle connection information requests, logging and context formatting and delivery for automatic contextual reconfiguration and context-triggered actions. © 2010 Binary Information Press.

Development of a new client -server architecture for context aware mobile computing

This dissertation studies the context-aware application with its proposed algorithms at client side. The required context-aware infrastructure is discussed in depth to illustrate that such an infrastructure collects the mobile user’s context information, registers service providers, derives mobile user’s current context, distributes user context among context-aware applications, and provides tailored services. The approach proposed tries to strike a balance between the context server and mobile devices. The context acquisition is centralized at the server to ensure the reusability of context information among mobile devices, while context reasoning remains at the application level. Hence, a centralized context acquisition and distributed context reasoning are viewed as a better solution overall. The context-aware search application is designed and implemented at the server side. A new algorithm is proposed to take into consideration the user context profiles. By promoting feedback on the dynamics of the system, any prior user selection is now saved for further analysis such that it may contribute to help the results of a subsequent search. On the basis of these developments at the server side, various solutions are consequently provided at the client side. A proxy software-based component is set up for the purpose of data collection. This research endorses the belief that the proxy at the client side should contain the context reasoning component. Implementation of such a component provides credence to this belief in that the context applications are able to derive the user context profiles. Furthermore, a context cache scheme is implemented to manage the cache on the client device in order to minimize processing requirements and other resources (bandwidth, CPU cycle, power). Java and MySQL platforms are used to implement the proposed architecture and to test scenarios derived from user’s daily activities. To meet the practical demands required of a testing environment without the impositions of a heavy cost for establishing such a comprehensive infrastructure, a software simulation using a free Yahoo search API is provided as a means to evaluate the effectiveness of the design approach in a most realistic way. The integration of Yahoo search engine into the context-aware architecture design proves how context aware application can meet user demands for tailored services and products in and around the user’s environment. The test results show that the overall design is highly effective, providing new features and enriching the mobile user’s experience through a broad scope of potential applications.

Mixed spatial and nonspatial problems in location based services

With hundreds of millions of users reporting locations and embracing mobile technologies, Location Based Services (LBSs) are raising new challenges. In this dissertation, we address three emerging problems in location services, where geolocation data plays a central role. First, to handle the unprecedented growth of generated geolocation data, existing location services rely on geospatial database systems. However, their inability to leverage combined geographical and textual information in analytical queries (e.g. spatial similarity joins) remains an open problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases. Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe's ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features. Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses. Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe to provide correct safety values. We take steps toward addressing this problem by proposing SpiDeR, an algorithm that takes advantage of the richness of information available in Yelp to detect abnormal review patterns. We propose a fake venue detection solution that applies SpsJoin on Yelp and U.S. housing datasets. We validate the proposed solutions using ground truth data extracted by our experiments and reviews filtered by Yelp.

Architettura cloud per lo sviluppo multi-piattaforma di Sistemi Operativi: Applicazioni specifiche

Il mondo dell’Internet of Things e del single board computing sono settori in forte espansione al giorno d’oggi e le architetture ARM sono, al momento, i dominatori in questo ambito. I sistemi operativi e i software si stanno evolvendo per far fronte a questo cambiamento e ai nuovi casi d’uso che queste tecnologie introducono. In questa tesi ci occuperemo del porting della distribuzione Linux Sabayon per queste architetture, la creazione di un infrastruttura per il rilascio delle immagini e la compilazione dei pacchetti software.

Could the Outsourcing of Incident Response Management provide a Blueprint for Managing Other Cloud Security Requirements?

Postprint

Development of a New Client-Server Architecture for Context Aware Mobile Computing

This dissertation studies the context-aware application with its proposed algorithms at client side. The required context-aware infrastructure is discussed in depth to illustrate that such an infrastructure collects the mobile user’s context information, registers service providers, derives mobile user’s current context, distributes user context among context-aware applications, and provides tailored services. The approach proposed tries to strike a balance between the context server and mobile devices. The context acquisition is centralized at the server to ensure the usability of context information among mobile devices, while context reasoning remains at the application level. Hence, a centralized context acquisition and distributed context reasoning are viewed as a better solution overall. The context-aware search application is designed and implemented at the server side. A new algorithm is proposed to take into consideration the user context profiles. By promoting feedback on the dynamics of the system, any prior user selection is now saved for further analysis such that it may contribute to help the results of a subsequent search. On the basis of these developments at the server side, various solutions are consequently provided at the client side. A proxy software-based component is set up for the purpose of data collection. This research endorses the belief that the proxy at the client side should contain the context reasoning component. Implementation of such a component provides credence to this belief in that the context applications are able to derive the user context profiles. Furthermore, a context cache scheme is implemented to manage the cache on the client device in order to minimize processing requirements and other resources (bandwidth, CPU cycle, power). Java and MySQL platforms are used to implement the proposed architecture and to test scenarios derived from user’s daily activities. To meet the practical demands required of a testing environment without the impositions of a heavy cost for establishing such a comprehensive infrastructure, a software simulation using a free Yahoo search API is provided as a means to evaluate the effectiveness of the design approach in a most realistic way. The integration of Yahoo search engine into the context-aware architecture design proves how context aware application can meet user demands for tailored services and products in and around the user’s environment. The test results show that the overall design is highly effective,providing new features and enriching the mobile user’s experience through a broad scope of potential applications.

Assisting End-users in Filling Out Web Services

With the quick advance of web service technologies, end-users can conduct various on-line tasks, such as shopping on-line. Usually, end-users compose a set of services to accomplish a task, and need to enter values to services to invoke the composite services. Quite often, users re-visit websites and use services to perform re-occurring tasks. The users are required to enter the same information into various web services to accomplish such re-occurring tasks. However, repetitively typing the same information into services is a tedious job for end-users. It can negatively impact user experience when an end-user needs to type the re-occurring information repetitively into web services. Recent studies have proposed several approaches to help users fill in values to services automatically. However, prior studies mainly suffer the following drawbacks: (1) limited support of collecting and analyzing user inputs; (2) poor accuracy of filling values to services; (3) not designed for service composition. To overcome the aforementioned drawbacks, we need maximize the reuse of previous user inputs across services and end-users. In this thesis, we introduce our approaches that prevent end-users from entering the same information into repetitive on-line tasks. More specifically, we improve the process of filling out services in the following 4 aspects: First, we investigate the characteristics of input parameters. We propose an ontology-based approach to automatically categorize parameters and fill values to the categorized input parameters. Second, we propose a comprehensive framework that leverages user contexts and usage patterns into the process of filling values to services. Third, we propose an approach for maximizing the value propagation among services and end-users by linking a set of semantically related parameters together and similar end-users. Last, we propose a ranking-based framework that ranks a list of previous user inputs for an input parameter to save a user from unnecessary data entries. Our framework learns and analyzes interactions of user inputs and input parameters to rank user inputs for input parameters under different contexts.

AGOCS – Accurate Google Cloud Simulator Framework

This paper presents the Accurate Google Cloud Simulator (AGOCS) – a novel high-fidelity Cloud workload simulator based on parsing real workload traces, which can be conveniently used on a desktop machine for day-to-day research. Our simulation is based on real-world workload traces from a Google Cluster with 12.5K nodes, over a period of a calendar month. The framework is able to reveal very precise and detailed parameters of the executed jobs, tasks and nodes as well as to provide actual resource usage statistics. The system has been implemented in Scala language with focus on parallel execution and an easy-to-extend design concept. The paper presents the detailed structural framework for AGOCS and discusses our main design decisions, whilst also suggesting alternative and possibly performance enhancing future approaches. The framework is available via the Open Source GitHub repository.

Acceleration and visualization of Dynamic Network Optimization

With the emerging prevalence of smart phones and 4G LTE networks, the demand for faster-better-cheaper mobile services anytime and anywhere is ever growing. The Dynamic Network Optimization (DNO) concept emerged as a solution that optimally and continuously tunes the network settings, in response to varying network conditions and subscriber needs. Yet, the DNO realization is still at infancy, largely hindered by the bottleneck of the lengthy optimization runtime. This paper presents the design and prototype of a novel cloud based parallel solution that further enhances the scalability of our prior work on various parallel solutions that accelerate network optimization algorithms. The solution aims to satisfy the high performance required by DNO, preliminarily on a sub-hourly basis. The paper subsequently visualizes a design and a full cycle of a DNO system. A set of potential solutions to large network and real-time DNO are also proposed. Overall, this work creates a breakthrough towards the realization of DNO.

New Frontiers in Energy-Efficient Computing

Energy-efficient computing remains a critical challenge across the wide range of future data-processing engines — from ultra-low-power embedded systems to servers, mainframes, and supercomputers. In addition, the advent of cloud and mobile computing as well as the explosion of IoT technologies have created new research challenges in the already complex, multidimensional space of modern and future computer systems. These new research challenges led to the establishment of the IEEE Rebooting Computing Initiative, which specifically addresses novel low-power solutions and technologies as one of the main areas of concern.With this in mind, we thought it timely to survey the state of the art of energy-efficient computing.