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In cloud computing, users are able to use their own operating system (OS) image to run a virtual machine (VM) on a remote host. The virtual machine OS is started by the user using some interfaces provided by a cloud provider in public or private cloud. In peer to peer cloud, the VM is started by the host admin. After the VM is running, the user could get a remote access to the VM to install, configure, and run services. For the security reasons, the user needs to verify the integrity of the running VM, because a malicious host admin could modify the image or even replace the image with a similar image, to be able to get sensitive data from the VM. We propose an approach to verify the integrity of a running VM on a remote host, without using any specific hardware such as Trusted Platform Module (TPM). Our approach is implemented on a Linux platform where the kernel files (vmlinuz and initrd) could be replaced with new files, while the VM is running. kexec is used to reboot the VM with the new kernel files. The new kernel has secret codes that will be used to verify whether the VM was started using the new kernel files. The new kernel is used to further measuring the integrity of the running VM.
Unified logging system for monitoring multiple cloud storage providers in cloud storage broker
(2018)
With the increasing demand for personal and enterprise data storage service, Cloud Storage Broker (CSB) provides cloud storage service using multiple Cloud Service Providers (CSPs) with guaranteed Quality of Service (QoS), such as data availability and security. However monitoring cloud storage usage in multiple CSPs has become a challenge for CSB due to lack of standardized logging format for cloud services that causes each CSP to implement its own format. In this paper we propose a unified logging system that can be used by CSB to monitor cloud storage usage across multiple CSPs. We gather cloud storage log files from three different CSPs and normalise these into our proposed log format that can be used for further analysis process. We show that our work enables a coherent view suitable for data navigation, monitoring, and analytics.
Cloud Storage Broker (CSB) provides value-added cloud storage service for enterprise usage by leveraging multi-cloud storage architecture. However, it raises several challenges for managing resources and its access control in multiple Cloud Service Providers (CSPs) for authorized CSB stakeholders. In this paper we propose unified cloud access control model that provides the abstraction of CSP's services for centralized and automated cloud resource and access control management in multiple CSPs. Our proposal offers role-based access control for CSB stakeholders to access cloud resources by assigning necessary privileges and access control list for cloud resources and CSB stakeholders, respectively, following privilege separation concept and least privilege principle. We implement our unified model in a CSB system called CloudRAID for Business (CfB) with the evaluation result shows it provides system-and-cloud level security service for cfB and centralized resource and access control management in multiple CSPs.
Massive Open Online Courses (MOOCs) have left their mark on the face of education during the recent years. At the Hasso Plattner Institute (HPI) in Potsdam, Germany, we are actively developing a MOOC platform, which provides our research with a plethora of e-learning topics, such as learning analytics, automated assessment, peer assessment, team-work, online proctoring, and gamification. We run several instances of this platform. On openHPI, we provide our own courses from within the HPI context. Further instances are openSAP, openWHO, and mooc.HOUSE, which is the smallest of these platforms, targeting customers with a less extensive course portfolio. In 2013, we started to work on the gamification of our platform. By now, we have implemented about two thirds of the features that we initially have evaluated as useful for our purposes. About a year ago we activated the implemented gamification features on mooc.HOUSE. Before activating the features on openHPI as well, we examined, and re-evaluated our initial considerations based on the data we collected so far and the changes in other contexts of our platforms.
The "Bachelor Project"
(2019)
One of the challenges of educating the next generation of computer scientists is to teach them to become team players, that are able to communicate and interact not only with different IT systems, but also with coworkers and customers with a non-it background. The “bachelor project” is a project based on team work and a close collaboration with selected industry partners. The authors hosted some of the teams since spring term 2014/15. In the paper at hand we explain and discuss this concept and evaluate its success based on students' evaluation and reports. Furthermore, the technology-stack that has been used by the teams is evaluated to understand how self-organized students in IT-related projects work. We will show that and why the bachelor is the most successful educational format in the perception of the students and how this positive results can be improved by the mentors.
Studies indicate that reliable access to power is an important enabler for economic growth. To this end, modern energy management systems have seen a shift from reliance on time-consuming manual procedures , to highly automated management , with current energy provisioning systems being run as cyber-physical systems . Operating energy grids as a cyber-physical system offers the advantage of increased reliability and dependability , but also raises issues of security and privacy. In this chapter, we provide an overview of the contents of this book showing the interrelation between the topics of the chapters in terms of smart energy provisioning. We begin by discussing the concept of smart-grids in general, proceeding to narrow our focus to smart micro-grids in particular. Lossy networks also provide an interesting framework for enabling the implementation of smart micro-grids in remote/rural areas, where deploying standard smart grids is economically and structurally infeasible. To this end, we consider an architectural design for a smart micro-grid suited to low-processing capable devices. We model malicious behaviour, and propose mitigation measures based properties to distinguish normal from malicious behaviour .
Detecting and recognizing text in natural scene images is a challenging, yet not completely solved task. In recent years several new systems that try to solve at least one of the two sub-tasks (text detection and text recognition) have been proposed. In this paper we present SEE, a step towards semi-supervised neural networks for scene text detection and recognition, that can be optimized end-to-end. Most existing works consist of multiple deep neural networks and several pre-processing steps. In contrast to this, we propose to use a single deep neural network, that learns to detect and recognize text from natural images, in a semi-supervised way. SEE is a network that integrates and jointly learns a spatial transformer network, which can learn to detect text regions in an image, and a text recognition network that takes the identified text regions and recognizes their textual content. We introduce the idea behind our novel approach and show its feasibility, by performing a range of experiments on standard benchmark datasets, where we achieve competitive results.
Cloud storage brokerage is an abstraction aimed at providing value-added services. However, Cloud Service Brokers are challenged by several security issues including enlarged attack surfaces due to integration of disparate components and API interoperability issues. Therefore, appropriate security risk assessment methods are required to identify and evaluate these security issues, and examine the efficiency of countermeasures. A possible approach for satisfying these requirements is employment of threat modeling concepts, which have been successfully applied in traditional paradigms. In this work, we employ threat models including attack trees, attack graphs and Data Flow Diagrams against a Cloud Service Broker (CloudRAID) and analyze these security threats and risks. Furthermore, we propose an innovative technique for combining Common Vulnerability Scoring System (CVSS) and Common Configuration Scoring System (CCSS) base scores in probabilistic attack graphs to cater for configuration-based vulnerabilities which are typically leveraged for attacking cloud storage systems. This approach is necessary since existing schemes do not provide sufficient security metrics, which are imperatives for comprehensive risk assessments. We demonstrate the efficiency of our proposal by devising CCSS base scores for two common attacks against cloud storage: Cloud Storage Enumeration Attack and Cloud Storage Exploitation Attack. These metrics are then used in Attack Graph Metric-based risk assessment. Our experimental evaluation shows that our approach caters for the aforementioned gaps and provides efficient security hardening options. Therefore, our proposals can be employed to improve cloud security.
The identification of vulnerabilities relies on detailed information about the target infrastructure. The gathering of the necessary information is a crucial step that requires an intensive scanning or mature expertise and knowledge about the system even though the information was already available in a different context. In this paper we propose a new method to detect vulnerabilities that reuses the existing information and eliminates the necessity of a comprehensive scan of the target system. Since our approach is able to identify vulnerabilities without the additional effort of a scan, we are able to increase the overall performance of the detection. Because of the reuse and the removal of the active testing procedures, our approach could be classified as a passive vulnerability detection. We will explain the approach and illustrate the additional possibility to increase the security awareness of users. Therefore, we applied the approach on an experimental setup and extracted security relevant information from web logs.
User-generated content on social media platforms is a rich source of latent information about individual variables. Crawling and analyzing this content provides a new approach for enterprises to personalize services and put forward product recommendations. In the past few years, brands made a gradual appearance on social media platforms for advertisement, customers support and public relation purposes and by now it became a necessity throughout all branches. This online identity can be represented as a brand personality that reflects how a brand is perceived by its customers. We exploited recent research in text analysis and personality detection to build an automatic brand personality prediction model on top of the (Five-Factor Model) and (Linguistic Inquiry and Word Count) features extracted from publicly available benchmarks. The proposed model reported significant accuracy in predicting specific personality traits form brands. For evaluating our prediction results on actual brands, we crawled the Facebook API for 100k posts from the most valuable brands' pages in the USA and we visualize exemplars of comparison results and present suggestions for future directions.
Web-based E-Learning uses Internet technologies and digital media to deliver education content to learners. Many universities in recent years apply their capacity in producing Massive Open Online Courses (MOOCs). They have been offering MOOCs with an expectation of rendering a comprehensive online apprenticeship. Typically, an online content delivery process requires an Internet connection. However, access to the broadband has never been a readily available resource in many regions. In Africa, poor and no networks are yet predominantly experienced by Internet users, frequently causing offline each moment a digital device disconnect from a network. As a result, a learning process is always disrupted, delayed and terminated in such regions. This paper raises the concern of E-Learning in poor and low bandwidths, in fact, it highlights the needs for an Offline-Enabled mode. The paper also explores technical approaches beamed to enhance the user experience inWeb-based E-Learning, particular in Africa.
The emergence of cloud computing allows users to easily host their Virtual Machines with no up-front investment and the guarantee of always available anytime anywhere. But with the Virtual Machine (VM) is hosted outside of user's premise, the user loses the physical control of the VM as it could be running on untrusted host machines in the cloud. Malicious host administrator could launch live memory dumping, Spectre, or Meltdown attacks in order to extract sensitive information from the VM's memory, e.g. passwords or cryptographic keys of applications running in the VM. In this paper, inspired by the moving target defense (MTD) scheme, we propose a novel approach to increase the security of application's sensitive data in the VM by continuously moving the sensitive data among several memory allocations (blocks) in Random Access Memory (RAM). A movement function is added into the application source code in order for the function to be running concurrently with the application's main function. Our approach could reduce the possibility of VM's sensitive data in the memory to be leaked into memory dump file by 2 5% and secure the sensitive data from Spectre and Meltdown attacks. Our approach's overhead depends on the number and the size of the sensitive data.
802.15.4 security protects against the replay, injection, and eavesdropping of 802.15.4 frames. A core concept of 802.15.4 security is the use of frame counters for both nonce generation and anti-replay protection. While being functional, frame counters (i) cause an increased energy consumption as they incur a per-frame overhead of 4 bytes and (ii) only provide sequential freshness. The Last Bits (LB) optimization does reduce the per-frame overhead of frame counters, yet at the cost of an increased RAM consumption and occasional energy-and time-consuming resynchronization actions. Alternatively, the timeslotted channel hopping (TSCH) media access control (MAC) protocol of 802.15.4 avoids the drawbacks of frame counters by replacing them with timeslot indices, but findings of Yang et al. question the security of TSCH in general. In this paper, we assume the use of ContikiMAC, which is a popular asynchronous MAC protocol for 802.15.4 networks. Under this assumption, we propose an Intra-Layer Optimization for 802.15.4 Security (ILOS), which intertwines 802.15.4 security and ContikiMAC. In effect, ILOS reduces the security-related per-frame overhead even more than the LB optimization, as well as achieves strong freshness. Furthermore, unlike the LB optimization, ILOS neither incurs an increased RAM consumption nor requires resynchronization actions. Beyond that, ILOS integrates with and advances other security supplements to ContikiMAC. We implemented ILOS using OpenMotes and the Contiki operating system.
MOOCs in Secondary Education
(2019)
Computer science education in German schools is often less than optimal. It is only mandatory in a few of the federal states and there is a lack of qualified teachers. As a MOOC (Massive Open Online Course) provider with a German background, we developed the idea to implement a MOOC addressing pupils in secondary schools to fill this gap. The course targeted high school pupils and enabled them to learn the Python programming language. In 2014, we successfully conducted the first iteration of this MOOC with more than 7000 participants. However, the share of pupils in the course was not quite satisfactory. So we conducted several workshops with teachers to find out why they had not used the course to the extent that we had imagined. The paper at hand explores and discusses the steps we have taken in the following years as a result of these workshops.
LoANs
(2019)
Recently, deep neural networks have achieved remarkable performance on the task of object detection and recognition. The reason for this success is mainly grounded in the availability of large scale, fully annotated datasets, but the creation of such a dataset is a complicated and costly task. In this paper, we propose a novel method for weakly supervised object detection that simplifies the process of gathering data for training an object detector. We train an ensemble of two models that work together in a student-teacher fashion. Our student (localizer) is a model that learns to localize an object, the teacher (assessor) assesses the quality of the localization and provides feedback to the student. The student uses this feedback to learn how to localize objects and is thus entirely supervised by the teacher, as we are using no labels for training the localizer. In our experiments, we show that our model is very robust to noise and reaches competitive performance compared to a state-of-the-art fully supervised approach. We also show the simplicity of creating a new dataset, based on a few videos (e.g. downloaded from YouTube) and artificially generated data.
While the IEEE 802.15.4 radio standard has many features that meet the requirements of Internet of things applications, IEEE 802.15.4 leaves the whole issue of key management unstandardized. To address this gap, Krentz et al. proposed the Adaptive Key Establishment Scheme (AKES), which establishes session keys for use in IEEE 802.15.4 security. Yet, AKES does not cover all aspects of key management. In particular, AKES comprises no means for key revocation and rekeying. Moreover, existing protocols for key revocation and rekeying seem limited in various ways. In this paper, we hence propose a key revocation and rekeying protocol, which is designed to overcome various limitations of current protocols for key revocation and rekeying. For example, our protocol seems unique in that it routes around IEEE 802.15.4 nodes whose keys are being revoked. We successfully implemented and evaluated our protocol using the Contiki-NG operating system and aiocoap.
This paper discusses a new approach for designing and deploying Security-as-a-Service (SecaaS) applications using cloud native design patterns. Current SecaaS approaches do not efficiently handle the increasing threats to computer systems and applications. For example, requests for security assessments drastically increase after a high-risk security vulnerability is disclosed. In such scenarios, SecaaS applications are unable to dynamically scale to serve requests. A root cause of this challenge is employment of architectures not specifically fitted to cloud environments. Cloud native design patterns resolve this challenge by enabling certain properties e.g. massive scalability and resiliency via the combination of microservice patterns and cloud-focused design patterns. However adopting these patterns is a complex process, during which several security issues are introduced. In this work, we investigate these security issues, we redesign and deploy a monolithic SecaaS application using cloud native design patterns while considering appropriate, layered security counter-measures i.e. at the application and cloud networking layer. Our prototype implementation out-performs traditional, monolithic applications with an average Scanner Time of 6 minutes, without compromising security. Our approach can be employed for designing secure, scalable and performant SecaaS applications that effectively handle unexpected increase in security assessment requests.
The detection of all inclusion dependencies (INDs) in an unknown dataset is at the core of any data profiling effort. Apart from the discovery of foreign key relationships, INDs can help perform data integration, integrity checking, schema (re-)design, and query optimization. With the advent of Big Data, the demand increases for efficient INDs discovery algorithms that can scale with the input data size. To this end, we propose S-INDD++ as a scalable system for detecting unary INDs in large datasets. S-INDD++ applies a new stepwise partitioning technique that helps discard a large number of attributes in early phases of the detection by processing the first partitions of smaller sizes. S-INDD++ also extends the concept of the attribute clustering to decide which attributes to be discarded based on the clustering result of each partition. Moreover, in contrast to the state-of-the-art, S-INDD++ does not require the partition to fit into the main memory-which is a highly appreciable property in the face of the ever growing datasets. We conducted an exhaustive evaluation of S-INDD++ by applying it to large datasets with thousands attributes and more than 266 million tuples. The results show the high superiority of S-INDD++ over the state-of-the-art. S-INDD++ reduced up to 50 % of the runtime in comparison with BINDER, and up to 98 % in comparison with S-INDD.
In university teaching today, it is common practice to record regular lectures and special events such as conferences and speeches. With these recordings, a large fundus of video teaching material can be created quickly and easily. Typically, lectures have a length of about one and a half hours and usually take place once or twice a week based on the credit hours. Depending on the number of lectures and other events recorded, the number of recordings available is increasing rapidly, which means that an appropriate form of provisioning is essential for the students. This is usually done in the form of lecture video platforms. In this work, we have investigated how lecture video platforms and the contained knowledge can be improved and accessed more easily by an increasing number of students. We came up with a multistep process we have applied to our own lecture video web portal that can be applied to other solutions as well.