Securing Real-Time Internet-of-Things

Modern embedded and cyber-physical systems are ubiquitous. A large number of critical cyber-physical systems have real-time requirements (e.g., avionics, automobiles, power grids, manufacturing systems, industrial control systems, etc.). Recent developments and new functionality requires real-time embedded devices to be connected to the Internet. This gives rise to the real-time Internet-of-things (RT-IoT) that promises a better user experience through stronger connectivity and efficient use of next-generation embedded devices. However RT- IoT are also increasingly becoming targets for cyber-attacks which is exacerbated by this increased connectivity. This paper gives an introduction to RT-IoT systems, an outlook of current approaches and possible research challenges towards secure RT- IoT frameworks

A Novel Side-Channel in Real-Time Schedulers

We demonstrate the presence of a novel scheduler side-channel in preemptive, fixed-priority real-time systems (RTS); examples of such systems can be found in automotive systems, avionic systems, power plants and industrial control systems among others. This side-channel can leak important timing information such as the future arrival times of real-time tasks.This information can then be used to launch devastating attacks, two of which are demonstrated here (on real hardware platforms). Note that it is not easy to capture this timing information due to runtime variations in the schedules, the presence of multiple other tasks in the system and the typical constraints (e.g., deadlines) in the design of RTS. Our ScheduLeak algorithms demonstrate how to effectively exploit this side-channel. A complete implementation is presented on real operating systems (in Real-time Linux and FreeRTOS). Timing information leaked by ScheduLeak can significantly aid other, more advanced, attacks in better accomplishing their goals

Moving target defense for securing smart grid communications: Architectural design, implementation and evaluation

Supervisory Control And Data Acquisition (SCADA) communications are often subjected to various kinds of sophisticated cyber-attacks which can have a serious impact on the Critical Infrastructure such as the power grid. Most of the time, the success of the attack is based on the static characteristics of the system, thereby enabling an easier profiling of the target system(s) by the adversary and consequently exploiting their limited resources. In this thesis, a novel approach to mitigate such static vulnerabilities is proposed by implementing a Moving Target Defense (MTD) strategy in a power grid SCADA environment, which leverages the existing communication network with an end-to-end IP Hopping technique among the trusted peer devices. This offers a proactive L3 layer network defense, minimizing IP-specific threats and thwarting worm propagation, APTs, etc., which utilize the cyber kill chain for attacking the system through the SCADA network. The main contribution of this thesis is to show how MTD concepts provide proactive defense against targeted cyber-attacks, and a dynamic attack surface to adversaries without compromising the availability of a SCADA system. Specifically, the thesis presents a brief overview of the different type of MTD designs, the proposed MTD architecture and its implementation with IP hopping technique over a Control Center–Substation network link along with a 3-way handshake protocol for synchronization on the Iowa State’s Power Cyber testbed. The thesis further investigates the delay and throughput characteristics of the entire system with and without the MTD to choose the best hopping rate for the given link. It also includes additional contributions for making the testbed scenarios more realistic to real world scenarios with multi-hop, multi-path WAN. Using that and studying a specific attack model, the thesis analyses the best ranges of IP address for different hopping rate and different number of interfaces. Finally, the thesis describes two case studies to explore and identify potential weaknesses of the proposed mechanism, and also experimentally validate the proposed mitigation alterations to resolve the discovered vulnerabilities. As part of future work, we plan to extend this work by optimizing the MTD algorithm to be more resilient by incorporating other techniques like network port mutation to further increase the attack complexity and cost

Markov modeling of moving target defense games

We introduce a Markov-model-based framework for Moving Target Defense (MTD) analysis. The framework allows modeling of broad range of MTD strategies, provides general theorems about how the probability of a successful adversary defeating an MTD strategy is related to the amount of time/cost spent by the adversary, and shows how a multi-level composition of MTD strategies can be analyzed by a straightforward combination of the analysis for each one of these strategies. Within the proposed framework we define the concept of security capacity which measures the strength or effectiveness of an MTD strategy: the security capacity depends on MTD specific parameters and more general system parameters. We apply our framework to two concrete MTD strategies

Secured information dissemination and misbehavior detection in VANETs

In a connected vehicle environment, the vehicles in a region can form a distributed network (Vehicular Ad-hoc Network or VANETs) where they can share traffic-related information such as congestion or no-congestion with other vehicles within its proximity, or with a centralized entity via. the roadside units (RSUs). However, false or fabricated information injected by an attacker (or a malicious vehicle) within the network can disrupt the decision-making process of surrounding vehicles or any traffic-monitoring system. Since in VANETs the size of the distributed network constituting the vehicles can be small, it is not difficult for an attacker to propagate an attack across multiple vehicles within the network. Under such circumstances, it is difficult for any traffic monitoring organization to recognize the traffic scenario of the region of interest (ROI). Furthermore, even if we are able to establish a secured connected vehicle environment, an attacker can leverage the connectivity of individual vehicles to the outside world to detect vulnerabilities, and disrupt the normal functioning of the in-vehicle networks of individual vehicles formed by the different sensors and actuators through remote injection attacks (such as Denial of Service (DoS)). Along this direction, the core contribution of our research is directed towards secured data dissemination, detection of malicious vehicles as well as false and fabricated information within the network. as well as securing the in-vehicle networks through improvisation of the existing arbitration mechanism which otherwise leads to Denial of Service (DoS) attacks (preventing legitimate components from exchanging messages in a timely manner). --Abstract, page iv

Tiresias: Predicting Security Events Through Deep Learning

With the increased complexity of modern computer attacks, there is a need for defenders not only to detect malicious activity as it happens, but also to predict the specific steps that will be taken by an adversary when performing an attack. However this is still an open research problem, and previous research in predicting malicious events only looked at binary outcomes (e.g., whether an attack would happen or not), but not at the specific steps that an attacker would undertake. To fill this gap we present Tiresias, a system that leverages Recurrent Neural Networks (RNNs) to predict future events on a machine, based on previous observations. We test Tiresias on a dataset of 3.4 billion security events collected from a commercial intrusion prevention system, and show that our approach is effective in predicting the next event that will occur on a machine with a precision of up to 0.93. We also show that the models learned by Tiresias are reasonably stable over time, and provide a mechanism that can identify sudden drops in precision and trigger a retraining of the system. Finally, we show that the long-term memory typical of RNNs is key in performing event prediction, rendering simpler methods not up to the task