Biblio

In January 2017 encrypted Internet traffic surpassed non-encrypted traffic. Although encryption increases security, it also masks intrusions and attacks by blocking the access to packet contents and traffic features, therefore making data analysis unfeasible. In spite of the strong effect of encryption, its impact has been scarcely investigated in the field. In this paper we study how encryption affects flow feature spaces and machine learning-based attack detection. We propose a new cross-layer feature vector that simultaneously represents traffic at three different levels: application, conversation, and endpoint behavior. We analyze its behavior under TLS and IPSec encryption and evaluate the efficacy with recent network traffic datasets and by using Random Forests classifiers. The cross-layer multi-key approach shows excellent attack detection in spite of TLS encryption. When IPsec is applied, the reduced variant obtains satisfactory detection for botnets, yet considerable performance drops for other types of attacks. The high complexity of network traffic is unfeasible for monolithic data analysis solutions, therefore requiring cross-layer analysis for which the multi-key vector becomes a powerful profiling core.

Smart grids require communication networks for supervision functions and control operations. With this they become attractive targets for attackers. In newer power grids, State Estimation (SE) is often performed based on Kalman Filters (KFs) to deal with noisy measurement data and detect Bad Data (BD) due to failures in the measurement system. Nevertheless, in a setting where attackers can gain access to modify sensor data, they can exploit the fact that SE is used to process the data. In this paper, we show how an attacker can modify Phasor Measurement Unit (PMU) sensor data in a way that it remains undetected in the state estimation process. We show how anomaly detection methods based on innovation gain fail if an attacker is aware of the state estimation and uses the right strategy to circumvent detection.

Subliminal channels in digital signatures provide a very effective method to clandestinely leak information from inside a system to a third party outside. Information can be hidden in signature parameters in a way that both network operators and legitimate receivers would not notice any suspicious traces. Subliminal channels have previously been discovered in other signatures, such as ElGamal and ECDSA. Those signatures are usually just sparsely exchanged in network protocols, e.g. during authentication, and their usability for leaking information is therefore limited. With the advent of high-speed signatures such as EdDSA, however, scenarios become feasible where numerous packets with individual signatures are transferred between communicating parties. This significantly increases the bandwidth for transmitting subliminal information. Examples are broadcast clock synchronization or signed sensor data export. A subliminal channel in signatures appended to numerous packets allows the transmission of a high amount of hidden information, suitable for large scale data exfiltration or even the operation of command and control structures. In this paper, we show the existence of a broadband subliminal channel in the EdDSA signature scheme. We then discuss the implications of the subliminal channel in practice using thee different scenarios: broadcast clock synchronization, signed sensor data export, and classic TLS. We perform several experiments to show the use of the subliminal channel and measure the actual bandwidth of the subliminal information that can be leaked. We then discuss the applicability of different countermeasures against subliminal channels from other signature schemes to EdDSA but conclude that none of the existing solutions can sufficiently protect against data exfiltration in network protocols secured by EdDSA.

Wide Area Monitoring Systems (WAMSs) provide an essential building block for Smart Grid supervision and control. Distributed Phasor Measurement Units (PMUs) allow accurate clock-synchronized measurements of voltage and current phasors (amplitudes, phase angles) and frequencies. The sensor data from PMUs provide situational awareness in the grid, and are used as input for control decisions. A modification of sensor data can severely impact grid stability, overall power supply, and physical devices. Since power grids are critical infrastructures, WAMSs are tempting targets for all kinds of attackers, including well-organized and motivated adversaries such as terrorist groups or adversarial nation states. Such groups possess sufficient resources to launch sophisticated attacks. In this paper, we provide an in-depth analysis of attack possibilities on WAMSs. We model the dependencies and building blocks of Advanced Persistent Threats (APTs) on WAMSs using attack trees. We consider the whole WAMS infrastructure, including aggregation and data collection points, such as Phasor Data Concentrators (PDCs), classical IT components, and clock synchronization. Since Smart Grids are cyber-physical systems, we consider physical perturbations, in addition to cyber attacks in our models. The models provide valuable information about the chain of cyber or physical attack steps that can be combined to build a sophisticated attack for reaching a higher goal. They assist in the assessment of physical and cyber vulnerabilities, and provide strategic guidance for the deployment of suitable countermeasures.