Abstract. In this work we present a NIDS cluster as a scalable solution for realizing high-performance, stateful network intrusion detection on commodity hardware. The design addresses three challenges: (i) distributing traffic evenly across an extensible set of analysis nodes in a fashion that minimizes the communication required for coordination, (ii) adapting the NIDS's operation to support coordinating its low-level analysis rather than just aggregating alerts; and (iii) validating that the cluster produces sound results. Prototypes of our NIDS cluster now operate at the Lawrence Berkeley National Laboratory and the University of California at Berkeley. In both environments the clusters greatly enhance the power of the network security monitoring.

Abstract. While conventional wisdom holds that residential users experience a high degree of compromise and infection, this presumption has seen little validation in the way of an in-depth study. In this paper we present a first step towards an assessment based on monitoring network activity (anonymized for user privacy) of 20,000 residential DSL customers in a European urban area, roughly 1,000 users of a community network in rural India, and several thousand dormitory users at a large US university. Our study focuses on security issues that overtly manifest in such data sets, such as scanning, spamming, payload signatures, and contact to botnet rendezvous points. We analyze the relationship between overt manifestations of such activity versus the "security hygiene" of the user populations (anti-virus and OS software updates) and potential risky behavior (accessing blacklisted URLs). We find that hygiene has little correlation with observed behavior, but risky behavior-which is quite prevalent-more than doubles the likelihood that a system will manifest security issues.

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Today, human security analysts collapse under the sheer volume of alerts they have to triage during investigations. The inability to cope with this load, coupled with a high false positive rate of alerts, creates alert fatigue. This results in failure to detect complex attacks, such as advanced persistent threats (APTs), because they manifest over long time frames and attackers tread carefully to evade detection mechanisms. In this paper, we contribute a new method to synthesize attack graphs from state machines. We use the network direction to derive potential attack stages from single and meta-alerts and model resulting attack scenarios in a kill chain state machine (KCSM). Our algorithm yields a graphical summary of the attack, APT scenario graphs, where nodes represent involved hosts and edges infection activity. We evaluate the feasibility of our approach in multiple experiments based on the CSE-CIC-IDS2018 data set [21]. We obtain up to 446 458 singleton alerts that our algorithm condenses into 700 APT scenario graphs resulting in a reduction of up to three orders of magnitude. This reduction makes it feasible for human analysts to effectively triage potential incidents. An evaluation on the same data set, in which we embedded a synthetic yet realistic APT campaign, supports the applicability of our approach of detecting and contextualizing complex attacks. The APT scenario graphs constructed by our algorithm correctly link large parts of the APT campaign and present a coherent view to support the human analyst in further analyses. CCS CONCEPTS• Security and privacy → Intrusion detection systems; Network security.

When an organization detects a security breach, it undertakes a forensic analysis to figure out what happened. This investigation involves inspecting a wide range of heterogeneous data sources spanning over a long period of time. The iterative nature of the analysis procedure requires an interactive experience with the data. However, the distributed processing paradigms we find in practice today fail to provide this requirement: the batch-oriented nature of MapReduce cannot deliver sub-second round-trip times, and distributed in-memory processing cannot store the terabytes of activity logs needed to inspect during an incident.We present the design and implementation of Visibility Across Space and Time (VAST), a distributed database to support interactive network forensics, and libcppa, its exceptionally scalable messaging core. The extended actor framework libcppa enables VAST to distribute lightweight tasks at negligible overhead. In our live demo, we showcase how VAST enables security analysts to grapple with the huge amounts of data often associated with incident investigations.