In today’s networks, administrative access to Linux servers is commonly managed by Privileged Access Management (PAM). It is not only important to monitor these privileged accounts, but also to control segregation of duty and detect keys as well as accounts that potentially bypass PAM. Unprohibited access can become a business risk. In order to improve the security in a controlled manner, we establish IdMSecMan, a security management process tailored for identity and access management (IAM). Security management processes typically use the Deming Cycle or an adaption for continuous improvements of products, services, or processes within the network infrastructure. We adjust a security management process with visualization for IAM, which also shifts the focus from typical assets to the attacker. With the controlled cycles, the maturity of IAM is measured and can continually advance. This paper presents and applies the work in progress IdMSecMan to a motivating scenario in the field of Linux server. We evaluate our approach in a controlled test environment with first steps to roll it out in our data center. Last but not least, we discuss challenges and future work.

Many recent publications deal with trend analysis, event detection or opinion mining on social media data. Twitter, as the most important microblogging service, is often in the focus of these works, as it offers free access to big volumes of data. The free access, on that many publications rely, is composed of a random subset of the complete public status stream. Publications rely particularly on the uniform distribution of tweets in this sample stream, and therefore, till today, one has to trust in the statement of Twitter that the sample data is indeed uniformly distributed1. In our research on the technical properties of Twitter's streaming data, we found evidence for discovering the method used by Twitter to decide which tweets will show up in the random sample streams. A deeper insight into this process leads to the possible reasons of why Twitter chose the presented sampling method. For this purpose we provide an overview of how Twitter's unique tweet IDs are generated and explain the regularities of each part of a tweet ID. This results also in some information about the tweet ID generating infrastructure of Twitter and what kind of knowledge can possibly be derived from small features like the tweet ID.

Cyber Physical Systems (CPSs) are widely expected to be formed of networked resource constrained devices. To suit the constraints of such networks, the IETF developed the RPL routing protocol for Low-power and Lossy Networks (LLNs). Security in CPSs is important for maintaining the integrity and privacy of data, while also improving network resiliency to attacks. Even though RPL provides support for integrity and confidentiality of messages, details regarding key management and signatures are not covered. Since complexity and size is a core concern in LLNs, off-loading the security features to a Trusted Platform Module (TPM) can make it possible to include sophisticated security provisions in an RPL implementation. This paper presents how it would be possible to use the security mechanisms of a TPM in order to secure the communication in an RPL network.