More Like this

1. MaDIoT 2.0: Modern High-Wattage IoT Botnet Attacks and Defenses

   Shekari, Tohid; Cardenas, Alvaro A; Beyah Raheem (July 2022, USENIX Security Symposium)

   The widespread availability of vulnerable IoT devices has resulted in IoT botnets. A particularly concerning IoT botnet can be built around high-wattage IoT devices such as EV chargers because, in large numbers, they can abruptly change the electricity consumption in the power grid. These attacks are called Manipulation of Demand via IoT (MaDIoT) attacks. Previous research has shown that the existing power grid protection mechanisms prevent any large-scale negative consequences to the grid from MaDIoT attacks. In this paper, we analyze this assumption and show that an intelligent attacker with extra knowledge about the power grid and its state, can launch more sophisticated attacks. Rather than attacking all locations at random times, our adversary uses an instability metric that lets the attacker know the specific time and geographical location to activate the high-wattage bots. We call these new attacks MaDIoT 2.0. 

   more » « less
2. BlackIoT: IoT Botnet of High Wattage Devices Can Disrupt the Power Grid

   Saleh Soltan, Prateek Mittal (August 2018, Proceedings of the 27th USENIX Security Symposium)

   We demonstrate that an Internet of Things (IoT) botnet of high wattage devices–such as air conditioners and heaters–gives a unique ability to adversaries to launch large-scale coordinated attacks on the power grid. In particular, we reveal a new class of potential attacks on power grids called the Manipulation of demand via IoT (MadIoT) attacks that can leverage such a botnet in order to manipulate the power demand in the grid. We study five variations of the MadIoT attacks and evaluate their effectiveness via state-of-the-art simulators on real-world power grid models. These simulation results demonstrate that the MadIoT attacks can result in local power outages and in the worst cases, large-scale blackouts. Moreover, we show that these attacks can rather be used to increase the operating cost of the grid to benefit a few utilities in the electricity market. This work sheds light upon the interdependency between the vulnerability of the IoT and that of the other networks such as the power grid whose security requires attention from both the systems security and power engineering communities. 

   more » « less
3. MaMIoT: Manipulation of Energy Market Leveraging High Wattage IoT Botnets

   https://doi.org/10.1145/3460120.3484581  

   Shekari, Tohid; Irvene, Celine; Cardenas, Alvaro A.; Beyah, Raheem (November 2021, Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security)

   If a trader could predict price changes in the stock market better than other traders, she would make a fortune. Similarly in the electricity market, a trader that could predict changes in the electricity load, and thus electricity prices, would be able to make large profits. Predicting price changes in the electricity market better than other market participants is hard, but in this paper, we show that attackers can manipulate the electricity prices in small but predictable ways, giving them a competitive advantage in the market. Our attack is possible when the adversary controls a botnet of high wattage devices such as air conditioning units, which are able to abruptly change the total demand of the power grid. Such attacks are called Manipulation of Demand via IoT (MaDIoT) attacks. In this paper, we present a new variant of MaDIoT and name it Manipulation of Market via IoT (MaMIoT). MaMIoT is the first energy market manipulation cyberattack that leverages high wattage IoT botnets to slightly change the total demand of the power grid with the aim of affecting the electricity prices in the favor of specific market players. Using real-world data obtained from two major energy markets, we show that MaMIoT can significantly increase the profit of particular market players or financially damage a group of players depending on the motivation of the attacker. 

   more » « less
4. Leveraging Data-Centric Edge Computing to Defend IoT-based Attacks in Power Grids

   Shrestha, Bibek; Lin, Hui (May 2020, Computer)

   Internet-of-things (IoT) introduce new attack surfaces for power grids with the usage of Wi-Fi enabled high wattage appliances. Adversaries can use IoT networks as a foothold to significantly change load demands and cause physical disruptions in power systems. This new IoT-based attack makes current security mechanisms, focusing on either power systems or IoT clouds, ineffective. To defend the attack, we propose to use a data-centric edge computing infrastructure to host defense mechanisms in IoT clouds by integrating physical states in decentralized regions of a power grid. By enforcing security policies on IoT devices, we can significantly limit the range of malicious activities, reducing the impact of IoT-based attacks. To fully understand the impact of data-centric edge computing on IoT clouds and power systems, we developed a cyber-physical testbed simulating six different power grids. Our preliminary results show that performance overhead is negligible, with less than 5% on average. 

   more » « less
5. EV Charging Infrastructure Discovery to Contextualize Its Deployment Security

   https://doi.org/10.1109/TNSM.2023.3318406  

   Sarieddine, Khaled; Sayed, Mohammad Ali; Assi, Chadi; Atallah, Ribal; Torabi, Sadegh; Khoury, Joseph; Pour, Morteza Safaei; Bou-Harb, Elias (February 2024, IEEE Transactions on Network and Service Management)

   Electric Vehicle Charging Stations (EVCSs) have been shown to be susceptible to remote exploitation due to manufacturer-induced vulnerabilities, demonstrated by recent attacks on this ecosystem. What is more alarming is that compromising these high-wattage IoT systems can be leveraged to perform coordinated oscillatory load attacks against the power grid which could lead to the instability of this critical infrastructure. In this paper, we investigate a previously sidelined aspect of EVCS security. We analyze the deployment security of EVCSs and highlight operator-induced vulnerabilities rendering the ecosystem exposed to remote intrusions. We create an advanced discovery technique that leverages Web interface artifacts to dynamically discover new charging station vendors. As a result, we uncover 33,320 charging station management systems in the wild. Consequently, we study the deployment security of the charging stations and identify that 28,046 EVCSs were found to be vulnerable to eavesdropping, and around 24% of the studied EVCSs are deployed with default configurations exposing the ecosystem to a Mirai-like attack vector. Aligned with this finding, we discover that the EVCS ecosystem has been targeted by nefarious IoT malware such as Mirai and its variants. This demonstrates that further security measures should be implemented by vendors and operators to ensure the security of this vital ecosystem. Consequently, we provide a comprehensive recommendation for securing the deployment of EVCSs. 

   more » « less