Avishai Wool

Aviv Engelberg, Avishai Wool

It is well known that when IoT traffic is unencrypted it is possible to identify the active devices based on their TCP/IP headers. And when traffic is encrypted, packet-sizes and timings can still be used to do so. To defend against such fingerprinting, traffic padding and shaping were introduced. In this paper we demonstrate that the packet-sizes distribution can still be used to successfully fingerprint the active IoT devices when shaping and padding are used, as long as the adversary is aware that these mitigations are deployed, and even if the values of the padding and shaping parameters are unknown. The main tool we use in our analysis is the full distribution of packet-sizes, as opposed to commonly used statistics such as mean and variance. We further show how an external adversary who only sees the padded and shaped traffic as aggregated and hidden behind a NAT middlebox can accurately identify the subset of active devices with Recall and Precision of at least 96%. We also show that the adversary can distinguish time windows containing only bogus cover packets from windows with real device activity, at a granularity of $1sec$ time windows, with 81% accuracy. Using similar methodology, but now on the defender's side, we are also able to detect anomalous activities in IoT traffic due to the Mirai worm.

Rony Komissarov, Avishai Wool

The safety and security of the passengers in vehicles in the face of cyber attacks is a key element in the automotive industry, especially with the emergence of the Advanced Driver Assistance Systems (ADAS) and the vast improvement in Autonomous Vehicles (AVs). Such platforms use various sensors, including cameras, LiDAR and mmWave radar. These sensors themselves may present a potential security hazard if exploited by an attacker. In this paper we propose a system to attack an automotive FMCW mmWave radar, that uses fast chirp modulation. Using a single rogue radar, our attack system is capable of spoofing the distance and velocity measured by the victim vehicle simultaneously, presenting phantom measurements coherent with the laws of physics governing vehicle motion. The attacking radar controls the delay in order to spoof its distance, and uses phase compensation and control in order to spoof its velocity. After developing the attack theory, we demonstrate the spoofing attack by building a proof-of-concept hardware-based system, using a Software Defined Radio. We successfully demonstrate two real world scenarios in which the victim radar is spoofed to detect either a phantom emergency stop or a phantom acceleration, while measuring coherent range and velocity. We also discuss several countermeasures to the attack, in order to propose ways to mitigate the described attack.

Nimrod Gilboa Markevich, Avishai Wool

ARINC 429 is the most common data bus in use today in civil avionics. However, the protocol lacks any form of source authentication. A technician with physical access to the bus is able to replace a transmitter by a rogue device, and the receivers will accept its malicious data as they have no method of verifying the authenticity of messages. Updating the protocol would close off security loopholes in new aircraft but would require thousands of airplanes to be modified. For the interim, until the protocol is replaced, we propose the first intrusion detection system that utilizes a hardware fingerprinting approach for sender identification for the ARINC 429 data bus. Our approach relies on the observation that changes in hardware, such as replacing a transmitter or a receiver with a rogue one, modify the electric signal of the transmission. Because we rely on the analog properties, and not on the digital content of the transmissions, we are able to detect a hardware switch as soon as it occurs, even if the data that is being transmitted is completely normal. Thus, we are able to preempt the attack before any damage is caused. In this paper we describe the design of our intrusion detection system and evaluate its performance against different adversary models. Our analysis includes both a theoretical Markov-chain model and an extensive empirical evaluation. For this purpose, we collected a data corpus of ARINC 429 data traces, which may be of independent interest since, to the best of our knowledge, no public corpus is available. We find that our intrusion detection system is quite realistic: e.g., it achieves near-zero false alarms per second, while detecting a rogue transmitter in under 50ms, and detecting a rogue receiver in under 3 seconds. In other words, technician attacks can be reliably detected during the pre-flight checks, well before the aircraft takes off.

Liron David, Avishai Wool

Human-chosen passwords are the a dominant form of authentication systems. Passwords strength estimators are used to help users avoid picking weak passwords by predicting how many attempts a password cracker would need until it finds a given password. In this paper we propose a novel password strength estimator, called PESrank, which accurately models the behavior of a powerful password cracker. PESrank calculates the rank of a given password in an optimal descending order of likelihood. PESrank estimates a given password's rank in fractions of a second---without actually enumerating the passwords---so it is practical for online use. It also has a training time that is drastically shorter than previous methods. Moreover, PESrank is efficiently tweakable to allow model personalization in fractions of a second, without the need to retrain the model; and it is explainable: it is able to provide information on why the password has its calculated rank, and gives the user insight on how to pick a better password. Our idea is to cast the question of password rank estimation in a probabilistic framework used in side-channel cryptanalysis. We view each password as a point in a $d$-dimensional search space, and learn the probability distribution of each dimension separately. The dimensions represent the base word, plus a dimension for each possible transformation such as adding a suffix or using a capitalization pattern. Using this model, password strength estimation is analogous to side-channel rank estimation. We implemented PERrank in Python and conducted an extensive evaluation study of it. We also integrated it into the registration page of a course at our university. Even with a model based on 905 million passwords, the response time was well under 1 second, with up to a 1-bit accuracy margin between the upper bound and the lower bound on the rank.

Chen Markman, Avishai Wool, Alvaro A. Cardenas

In Industrial Control Systems (ICS/SCADA), machine to machine data traffic is highly periodic. Previous work showed that in many cases, it is possible to create an automata-based model of the traffic between each individual Programmable Logic Controller (PLC) and the SCADA server, and to use the model to detect anomalies in the traffic. When testing the validity of previous models, we noticed that overall, the models have difficulty in dealing with communication patterns that change over time. In this paper we show that in many cases the traffic exhibits phases in time, where each phase has a unique pattern, and the transition between the different phases is rather sharp. We suggest a method to automatically detect traffic phase shifts, and a new anomaly detection model that incorporates multiple phases of the traffic. Furthermore we present a new sampling mechanism for training set assembly, which enables the model to learn all phases during the training stage with lower complexity. The model presented has similar accuracy and much less permissiveness compared to the previous general DFA model. Moreover, the model can provide the operator with information about the state of the controlled process at any given time, as seen in the traffic phases.

Amit Kleinmann, Ori Amichay, Avishai Wool et al.

SCADA protocols for Industrial Control Systems (ICS) are vulnerable to network attacks such as session hijacking. Hence, research focuses on network anomaly detection based on meta--data (message sizes, timing, command sequence), or on the state values of the physical process. In this work we present a class of semantic network-based attacks against SCADA systems that are undetectable by the above mentioned anomaly detection. After hijacking the communication channels between the Human Machine Interface (HMI) and Programmable Logic Controllers (PLCs), our attacks cause the HMI to present a fake view of the industrial process, deceiving the human operator into taking manual actions. Our most advanced attack also manipulates the messages generated by the operator's actions, reversing their semantic meaning while causing the HMI to present a view that is consistent with the attempted human actions. The attacks are totaly stealthy because the message sizes and timing, the command sequences, and the data values of the ICS's state all remain legitimate. We implemented and tested several attack scenarios in the test lab of our local electric company, against a real HMI and real PLCs, separated by a commercial-grade firewall. We developed a real-time security assessment tool, that can simultaneously manipulate the communication to multiple PLCs and cause the HMI to display a coherent system--wide fake view. Our tool is configured with message-manipulating rules written in an ICS Attack Markup Language (IAML) we designed, which may be of independent interest. Our semantic attacks all successfully fooled the operator and brought the system to states of blackout and possible equipment damage.

Amit Kleinmann, Avishai Wool

Traffic of Industrial Control System (ICS) between the Human Machine Interface (HMI) and the Programmable Logic Controller (PLC) is known to be highly periodic. However, it is sometimes multiplexed, due to asynchronous scheduling. Modeling the network traffic patterns of multiplexed ICS streams using Deterministic Finite Automata (DFA) for anomaly detection typically produces a very large DFA, and a high false-alarm rate. We introduce a new modeling approach that addresses this gap. Our Statechart DFA modeling includes multiple DFAs, one per cyclic pattern, together with a DFA-selector that de-multiplexes the incoming traffic into sub-channels and sends them to their respective DFAs. We demonstrate how to automatically construct the Statechart from a captured traffic stream. Our unsupervised learning algorithm builds a Discrete-Time Markov Chain (DTMC) from the stream. Next it splits the symbols into sets, one per multiplexed cycle, based on symbol frequencies and node degrees in the DTMC graph. Then it creates a sub-graph for each cycle, and extracts Euler cycles for each sub-graph. The final Statechart is comprised of one DFA per Euler cycle. The algorithms allow for non-unique symbols, that appear in more than one cycle, and also for symbols that appear more than once in a cycle. We evaluated our solution on traces from a production ICS using the Siemens S7-0x72 protocol. We also stress-tested our algorithms on a collection of synthetically-generated traces that simulated multiplexed ICS traces with varying levels of symbol uniqueness and time overlap. The algorithms were able to split the symbols into sets with 99.6% accuracy. The resulting Statechart modeled the traces with a low median false-alarm rate of 0.483%. In all but the most extreme scenarios the Statechart model drastically reduced both the false-alarm rate and the learned model size in compare to a naive single-DFA model

Uri Kanonov, Avishai Wool

Bring Your Own Device (BYOD) is a growing trend among enterprises, aiming to improve workers' mobility and productivity via their smartphones. The threats and dangers posed by the smartphones to the enterprise are also ever-growing. Such dangers can be mitigated by running the enterprise software inside a "secure container" on the smartphone. In our work we present a systematic assessment of security critical areas in design and implementation of a secure container for Android using reverse engineering and attacker-inspired methods. We do this through a case-study of Samsung KNOX, a real-world product deployed on millions of devices. Our research shows how KNOX security features work behind the scenes and lets us compare the vendor's public security claims against reality. Along the way we identified several design weaknesses and a few vulnerabilities that were disclosed to Samsung.

Eyal Itkin, Avishai Wool

The Precision Time Protocol (PTP) aims to provide highly accurate and synchronised clocks. Its defining standard, IEEE 1588, has a security section ("Annex K") which relies on symmetric-key secrecy. In this paper we present a detailed threat analysis of the PTP standard, in which we highlight the security properties that should be addressed by any security extension. During this analysis we identify a sequence of new attacks and non-cryptographic network-based defenses that mitigate them. We then suggest to replace Annex K's symmetric cryptography by an efficient elliptic-curve Public-Key signatures. We implemented all our attacks to demonstrate their effectiveness, and also implemented and evaluated both the network and cryptographic defenses. Our results show that the proposed schemes are extremely practical, and much more secure than previous suggestions.