Anti-tumor effects of fibroblast growth factor-binding protein (FGF-BP) knockdown in colon carcinoma

Abstract Background Fibroblast growth factors FGF-1 and FGF-2 are often upregulated in tumors, but tightly bound to heparan sulphate proteoglycans of the extracellular matrix (ECM). One mechanism of their bioactivation relies on the FGF-binding protein (FGF-BP) which, upon reversible binding to FGF-1 or -2, leads to their release from the ECM. FGF-BP increases tumorigenicity and is highly expressed in tumors like colon carcinoma. In this paper, we analyse cellular and molecular consequences of RNAi-mediated FGF-BP knockdown in colon carcinoma, and explore the therapeutic effects of the nanoparticle-mediated delivery of small interfering RNAs (siRNAs) for FGF-BP targeting. Results Employing stable RNAi cells, we establish a dose-dependence of cell proliferation on FGF-BP expression levels. Decreased proliferation is mirrored by alterations in cell cycle distribution and upregulation of p21, which is relevant for mediating FGF-BP effects. While inhibition of proliferation is mainly associated with reduced Akt and increased GSK3β activation, antibody array-based analyses also reveal other alterations in MAPK signalling. Additionally, we demonstrate induction of apoptosis, mediated through caspase-3/7 activation, and alterations in redox status upon FGF-BP knockdown. These effects are based on the upregulation of Bad, Bax and HIF-1α, and the downregulation of catalase. In a therapeutic FGF-BP knockdown approach based on RNAi, we employ polymer-based nanoparticles for the in vivo delivery of siRNAs into established wildtype colon carcinoma xenografts. We show that the systemic treatment of mice leads to the inhibition of tumor growth based on FGF-BP knockdown. Conclusions FGF-BP is integrated in a complex network of cytoprotective effects, and represents a promising therapeutic target for RNAi-based knockdown approaches.</p

Analyzing the Real-World Applicability of DGA Classifiers

Separating benign domains from domains generated by DGAs with the help of a binary classifier is a well-studied problem for which promising performance results have been published. The corresponding multiclass task of determining the exact DGA that generated a domain enabling targeted remediation measures is less well studied. Selecting the most promising classifier for these tasks in practice raises a number of questions that have not been addressed in prior work so far. These include the questions on which traffic to train in which network and when, just as well as how to assess robustness against adversarial attacks. Moreover, it is unclear which features lead a classifier to a decision and whether the classifiers are real-time capable. In this paper, we address these issues and thus contribute to bringing DGA detection classifiers closer to practical use. In this context, we propose one novel classifier based on residual neural networks for each of the two tasks and extensively evaluate them as well as previously proposed classifiers in a unified setting. We not only evaluate their classification performance but also compare them with respect to explainability, robustness, and training and classification speed. Finally, we show that our newly proposed binary classifier generalizes well to other networks, is time-robust, and able to identify previously unknown DGAs.Comment: Accepted at The 15th International Conference on Availability, Reliability and Security (ARES 2020

Malware and Botnet Analysis Methodology

Malware is responsible for massive economic damage. Being the preferred tool for digital crime, botnets are becoming increasingly sophisticated, using more and more resilient, distributed infrastructures based on peer-to-peer (P2P) protocols. On the other side, current investigation techniques for malware and botnets on a technical level are time-consuming and highly complex. Fraunhofer FKIE is addressing this problem, researching new ways of intelligent process automation and information management for malware analysis in order to minimize the time needed to investigate these threats

Case study of the Miner Botnet

Malware and botnets are one of the most serious threats to today's Internet security. In this paper, we characterise the so-called "Miner Botnet". It received major media attention after massive distributed denial of service attacks against a wide range of German and Russian websites, mainly during August and September 2011. We use our insights on this botnet to outline current botnet-related money-making concepts and to show that multiple activities of this botnet are actually centred on the virtual anonymised currency Bitcoin, thus justifying the name. Furthermore, we provide a binary-level analysis of the malware's design and components to illustrate the modularity of the previously mentioned concepts. We give an overview of the structure of the command-and-control protocol as well as of the botnet's architecture. Both centralised as well as distributed infrastructure aspects realised through peer-to-peer are present to run the botnet, the latter for increasing its resiliency. Finally, we provide the results of our ongoing tracking efforts that started in September 2011, focusing on the development of the botnet's size and geographic distribution. In addition we point out the challenge that is generally connected with size measurements of botnets due to the reachability of individual nodes and the persistence of IP addresses over time

Highly resilient peer-to-peer botnets are here: An analysis of Gameover Zeus

Zeus is a family of credential-stealing trojans which originally appeared in 2007. The first two variants of Zeus are based on centralized command servers. These command servers are now routinely tracked and blocked by the security community. In an apparent effort to withstand these routine countermeasures, the second version of Zeus was forked into a peer-to-peer variant in September 2011. Compared to earlier versions of Zeus, this peer-to-peer variant is fundamentally more difficult to disable. Through a detailed analysis of this new Zeus variant, we demonstrate the high resilience of state of the art peer-to-peer botnets in general, and of peer-to-peer Zeus in particular