In this paper, we study two pieces of malware that attempted to create blackouts in Ukraine. Our findings include new malware behavior not previously documented (such as the detailed algorithm for the MMS protocol payload) and an illustration of how attacking different targets will produce different effects.

In this work we collaborate with a commercial SOC to devise a 4-stage (network) process to support the collection and analysis of relevant information for threat analysis. We conduct an experiment with ten T1 analysts employed in the SOC and show that analysts following the proposed process are 2.5 times more likely to produce an accurate assessment than analysts who do not.

In this paper, we introduce a novel, PLC-compatible control-flow integrity (CFI) mechanism named ECFI to protect such devices from control-flow hijacking attacks. Our CFI approach is the first system for real-time PLCs and considers the runtime operation of the PLC as the highest priority.

This paper discusses a novel approach to specification-based intrusion detection in the field of networked control systems. Our approach reduces the substantial human effort required to deploy a specification-based intrusion detection system by automating the development of its specification rules.

Off-the-shelf intrusion detection systems prove an ill fit for protecting industrial control systems. Specifically, current systems fail to detect recent process control attacks. In this work we present a detector that continuously tracks updates to corresponding process variables to then derive variable-specific prediction models as the basis for assessing future activity.

In this paper we investigate and test the actual effectiveness of emulation-based detection and show that the detection can be circumvented by employing a wide range of evasion techniques, exploiting weakness that are present at all three levels in the detection process. We draw the conclusion that current emulation-based systems have limitations that allow attackers to craft generic shellcode encoders able to circumvent their detection mechanisms.

In this paper we investigate the effectiveness of a widely studied category of network intrusion detection systems: anomaly-based algorithms using n-gram analysis for payload inspection. Our tests show that the analyzed systems, in presence of data with high variability, cannot deliver high detection and low false positive rates at the same time.