Hendrik Graupner

Gathering and Analyzing Identity Leaks for Security Awareness

The amount of identity data leaks in recent times is drastically increasing. Not only smaller web services, but also established technology companies are affected. However, it is not commonly known, that incidents covered by media are just the tip of the iceberg. Accordingly, more detailed investigation of not just publicly accessible parts of the web but also deep web is imperative to gain greater insight into the large number of data leaks. This paper presents methods and experiences of our deep web analysis. We give insight in commonly used platforms for data exposure, formats of identity related data leaks, and the methods of our analysis. On one hand a lack of security implementations among Internet service providers exists and on the other hand users still tend to generate and reuse weak passwords. By publishing our results we aim to increase awareness on both sides and the establishment of counter measures.

Fast Automated Processing and Evaluation of Identity Leaks

The relevance of identity data leaks on the Internet is more present than ever. Almost every week we read about leakage of databases with more than a million users in the news. Smaller but not less dangerous leaks happen even multiple times a day. The public availability of such leaked data is a major threat to the victims, but also creates the opportunity to learn not only about security of service providers but also the behavior of users when choosing passwords. Our goal is to analyze this data and generate knowledge that can be used to increase security awareness and security, respectively. This paper presents a novel approach to the processing and analysis of a vast majority of bigger and smaller leaks. We evolved from a semi-manual to a fully automated process that requires a minimum of human interaction. Our contribution is the concept and a prototype implementation of a leak processing workflow that includes the extraction of digital identities from structured and unstructured leak-files, the identification of hash routines and a quality control to ensure leak authenticity. By making use of parallel and distributed programming, we are able to make leaks almost immediately available for analysis and notification after they have been published. Based on the data collected, this paper reveals how easy it is for criminals to collect lots of passwords, which are plain text or only weakly hashed. We publish those results and hope to increase not only security awareness of Internet users but also security on a technical level on the service provider side.

Redesign cloudRAID for flexible and secure enterprise file sharing over public cloud storage

CloudRAID is a secure personal cloud storage broker that provides data availability, security, and privacy for private usage. But some of the challenges need to be resolved to use CloudRAID as an enterprise cloud storage broker solution, such as complicated key management, absence of role-based hierarchical access control, and lack of administrative oversight. In this paper we tackle these challenges and propose an enterprise version of CloudRAID called CloudRAID for Business (CfB). We combine CloudRAID with Ciphertext-Policy Attribute-Based Encryption (CP-ABE) and implement administrative oversight for monitoring activities in CfB system and multiple CSPs. Our evaluation of CfB demonstrates that it offers robust security measures through fine-grained role-based access control, scalable key management for multi-user-and-device scenarios, reduces complexity of file sharing revocation, file-level security, and administrative oversight.

Secure self-seeding with power-up SRAM states

Generating seeds on Internet of things (IoT) devices is challenging because these devices typically lack common entropy sources, such as user interaction or hard disks. A promising replacement is to use power-up static random-access memory (SRAM) states, which are partly random due to manufacturing deviations. Thus far, there, however, seems to be no method for extracting close-to-uniformly distributed seeds from power-up SRAM states in an information-theoretically secure and practical manner. Moreover, the min-entropy of power-up SRAM states reduces with temperature, thereby rendering this entropy source vulnerable to so-called freezing attacks. In this paper, we mainly make three contributions. First, we propose a new method for extracting uniformly distributed seeds from power-up SRAM states. Unlike current methods, ours is information-theoretically secure, practical, and freezing attack-resistant rolled into one. Second, we point out a trick that enables using power-up SRAM states not only for self-seeding at boot time, but also for reseeding at runtime. Third, we compare the energy consumption of seeding an IoT device either with radio noise or power-up SRAM states. While seeding with power-up SRAM states turned out to be more energy efficient, we argue for mixing both these entropy sources.

More Lightweight, yet Stronger 802.15.4 Security Through an Intra-layer Optimization

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.