Cloud Based IoT Architecture

The Internet of Things (IoT) and cloud computing have grown in popularity over the past decade as the internet becomes faster and more ubiquitous. Cloud platforms are well suited to handle IoT systems as they are accessible and resilient, and they provide a scalable solution to store and analyze large amounts of IoT data. IoT applications are complex software systems and software developers need to have a thorough understanding of the capabilities, limitations, architecture, and design patterns of cloud platforms and cloud-based IoT tools to build an efficient, maintainable, and customizable IoT application. As the IoT landscape is constantly changing, research into cloud-based IoT platforms is either lacking or out of date. The goal of this thesis is to describe the basic components and requirements for a cloud-based IoT platform, to provide useful insights and experiences in implementing a cloud-based IoT solution using Microsoft Azure, and to discuss some of the shortcomings when combining IoT with a cloud platform

BAMBI: BLUETOOTH ACCESS MANAGEMENT & BEACON IDENTIFICATION

Cybersecurity is a constantly developing field. Patches that secured yesterday’s technology do not safeguard against occurring threats, necessitating continuous research in the field. The outbreak of Bluetooth Low Energy (BLE) devices dramatically expands the attack surface. BLE is one of the most widely applicable low-power connectivity standards. The low cost, low power consumption, and ready availability of BLE modules have made them a popular wireless technology for Internet of Things (IoT) devices and power constrained applications. However, the deployment of BLE-enabled devices enlarges the network attack surface. In spite of that, access management is insufficient for Bluetooth Low Energy devices. To elucidate, understanding the difference between known and unknown, malicious and non-malicious devices within a perimeter can be crucial in today’s cyberspace. This research proposes an approach called BAMBI - Beacon Access Management and Beacon Identification, which sought to develop an efficient, accurate, and easy-to implement solution for device/beacon identification and access management. The proposed solution, BAMBI, addresses these areas for the Bluetooth Low Energy Protocol. There are a few components to BAMBI that make up this solution. Device Identification, Device Classification, and Access Management are components that make BAMBI the first of its kind for the BLE protocol. Although this research is limited to the BLE protocol, it does introduce avenues for other connectivity standards such as Zig-bee and Bluetooth to adapt without much overhead

Epäilyttävien pankkitapahtumien tunnistaminen oppivien tilastollisten menetelmien avulla

In this thesis the aim was to find a more efficient way for detecting suspicious transactions from banking data. The chosen approach was to utilize outlier detection methods. The methods were first chosen based on a theoretical review but then narrowed down to those that have stable implementations in Python. The banking transaction data was then preprocessed and fed to the methods. For clustering methods we reviewed the running time and the CH index and for outlier detection the comparison was made from running time and visual exploration of the results. Finally it was found that GMM and iForest were the best performing methods. They were able to perform outlier detection on the large datasets in just minutes and should scale to a dataset of any size. They also have existing implementations in SKlearn and could be implemented as a part of a detection system.Tämän diplomityön tavoitteena oli löytää tehokkaampi tapa tunnistaa poikkeavia pankkitapahtumia. Läthökohdaksi valittiin poikkeamien tunnistusmenetelmät. Alustavat menetelmät valittiin teoreettisen tarkastelun pohjalta, mutta näistä karsittiin pois ne, joilla ei ollut vakaata toteutusta Pythonissa. Pankkitapahtumatiedot esikäsiteltiin ja syötettiin metodeille, jonka jälkeen eri metodien tuloksia tarkasteltiin. Ryhmittely menetelmien osalta tarkasteltiin ajoaikaa ja CH-indeksiä. Poikkeamien tunnistuksessa tarkasteltiin ajoaikaa ja tuloksia käytiin läpi visualisointien avulla. Työssä löydettiin kaksi hyvin toimivaa menetelmää: GMM ja iForest. Ne pystyivät suorittamaan poikkeamien tunnistusta suurille tietomäärille vain minuuteissa ja niiden pitäisi skaalautua minkä kokoiseen tietomäärään vain. Niistä on myöskin olemassa olevat toteutukset SKlearnissa, joten ne voitaisiin toteuttaa osaksi tunnistusjärjestelmää

Distributed Load Testing by Modeling and Simulating User Behavior

Modern human-machine systems such as microservices rely upon agile engineering practices which require changes to be tested and released more frequently than classically engineered systems. A critical step in the testing of such systems is the generation of realistic workloads or load testing. Generated workload emulates the expected behaviors of users and machines within a system under test in order to find potentially unknown failure states. Typical testing tools rely on static testing artifacts to generate realistic workload conditions. Such artifacts can be cumbersome and costly to maintain; however, even model-based alternatives can prevent adaptation to changes in a system or its usage. Lack of adaptation can prevent the integration of load testing into system quality assurance, leading to an incomplete evaluation of system quality. The goal of this research is to improve the state of software engineering by addressing open challenges in load testing of human-machine systems with a novel process that a) models and classifies user behavior from streaming and aggregated log data, b) adapts to changes in system and user behavior, and c) generates distributed workload by realistically simulating user behavior. This research contributes a Learning, Online, Distributed Engine for Simulation and Testing based on the Operational Norms of Entities within a system (LODESTONE): a novel process to distributed load testing by modeling and simulating user behavior. We specify LODESTONE within the context of a human-machine system to illustrate distributed adaptation and execution in load testing processes. LODESTONE uses log data to generate and update user behavior models, cluster them into similar behavior profiles, and instantiate distributed workload on software systems. We analyze user behavioral data having differing characteristics to replicate human-machine interactions in a modern microservice environment. We discuss tools, algorithms, software design, and implementation in two different computational environments: client-server and cloud-based microservices. We illustrate the advantages of LODESTONE through a qualitative comparison of key feature parameters and experimentation based on shared data and models. LODESTONE continuously adapts to changes in the system to be tested which allows for the integration of load testing into the quality assurance process for cloud-based microservices

Using K-means Clustering and Similarity Measure to Deal with Missing Rating in Collaborative Filtering Recommendation Systems

The Collaborative Filtering recommendation systems have been developed to address the information overload problem and personalize the content to the users for business and organizations. However, the Collaborative Filtering approach has its limitation of data sparsity and online scalability problems which result in low recommendation quality. In this thesis, a novel Collaborative Filtering approach is introduced using clustering and similarity technologies. The proposed method using K-means clustering to partition the entire dataset reduces the time complexity and improves the online scalability as well as the data density. Moreover, the similarity comparison method predicts and fills up the missing value in sparsity dataset to enhance the data density which boosts the recommendation quality. This thesis uses MovieLens dataset to investigate the proposed method, which yields amazing experimental outcome on a large sparsity data set that has a higher quality with lower time complexity than the traditional Collaborative Filtering approaches

Real-time big data processing for anomaly detection : a survey

The advent of connected devices and omnipresence of Internet have paved way for intruders to attack networks, which leads to cyber-attack, financial loss, information theft in healthcare, and cyber war. Hence, network security analytics has become an important area of concern and has gained intensive attention among researchers, off late, specifically in the domain of anomaly detection in network, which is considered crucial for network security. However, preliminary investigations have revealed that the existing approaches to detect anomalies in network are not effective enough, particularly to detect them in real time. The reason for the inefficacy of current approaches is mainly due the amassment of massive volumes of data though the connected devices. Therefore, it is crucial to propose a framework that effectively handles real time big data processing and detect anomalies in networks. In this regard, this paper attempts to address the issue of detecting anomalies in real time. Respectively, this paper has surveyed the state-of-the-art real-time big data processing technologies related to anomaly detection and the vital characteristics of associated machine learning algorithms. This paper begins with the explanation of essential contexts and taxonomy of real-time big data processing, anomalous detection, and machine learning algorithms, followed by the review of big data processing technologies. Finally, the identified research challenges of real-time big data processing in anomaly detection are discussed. © 2018 Elsevier Lt