More Like this

1. Cyber-physical simulation platform for security assessment of transactive energy systems

   https://doi.org/10.1109/MSCPES.2019.8738802  

   Zhang, Yue ; Eisele, Scott ; Dubey, Abhishek ; Laszka, Aron ; Srivastava, Anurag K. ( April 2019 , 2019 7th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES))

   Transactive energy systems (TES) are emerging as a transformative solution for the problems that distribution system operators face due to an increase in the use of distributed energy resources and rapid growth in scalability of managing active distribution system (ADS). On the one hand, these changes pose a decentralized power system control problem, requiring strategic control to maintain reliability and resiliency for the community and for the utility. On the other hand, they require robust financial markets while allowing participation from diverse prosumers. To support the computing and flexibility requirements of TES while preserving privacy and security, distributed software platforms are required. In this paper, we enable the study and analysis of security concerns by developing Transactive Energy Security Simulation Testbed (TESST), a TES testbed for simulating various cyber attacks. In this work, the testbed is used for TES simulation with centralized clearing market, highlighting weaknesses in a centralized system. Additionally, we present a blockchain enabled decentralized market solution supported by distributed computing for TES, which on one hand can alleviate some of the problems that we identify, but on the other hand, may introduce newer issues. Future study of these differing paradigms is necessary and will continue as we develop our security simulation testbed. 

   more » « less
2. A Survey of DDOS Attack Detection Techniques for IoT Systems Using BlockChain Technology

   https://doi.org/10.3390/electronics11233892  

   Khan, Zulfiqar Ali ; Namin, Akbar Siami ( December 2022 , Electronics)

   The Internet of Things (IoT) is a network of sensors that helps collect data 24/7 without human intervention. However, the network may suffer from problems such as the low battery, heterogeneity, and connectivity issues due to the lack of standards. Even though these problems can cause several performance hiccups, security issues need immediate attention because hackers access vital personal and financial information and then misuse it. These security issues can allow hackers to hijack IoT devices and then use them to establish a Botnet to launch a Distributed Denial of Service (DDoS) attack. Blockchain technology can provide security to IoT devices by providing secure authentication using public keys. Similarly, Smart Contracts (SCs) can improve the performance of the IoT–blockchain network through automation. However, surveyed work shows that the blockchain and SCs do not provide foolproof security; sometimes, attackers defeat these security mechanisms and initiate DDoS attacks. Thus, developers and security software engineers must be aware of different techniques to detect DDoS attacks. In this survey paper, we highlight different techniques to detect DDoS attacks. The novelty of our work is to classify the DDoS detection techniques according to blockchain technology. As a result, researchers can enhance their systems by using blockchain-based support for detecting threats. In addition, we provide general information about the studied systems and their workings. However, we cannot neglect the recent surveys. To that end, we compare the state-of-the-art DDoS surveys based on their data collection techniques and the discussed DDoS attacks on the IoT subsystems. The study of different IoT subsystems tells us that DDoS attacks also impact other computing systems, such as SCs, networking devices, and power grids. Hence, our work briefly describes DDoS attacks and their impacts on the above subsystems and IoT. For instance, due to DDoS attacks, the targeted computing systems suffer delays which cause tremendous financial and utility losses to the subscribers. Hence, we discuss the impacts of DDoS attacks in the context of associated systems. Finally, we discuss Machine-Learning algorithms, performance metrics, and the underlying technology of IoT systems so that the readers can grasp the detection techniques and the attack vectors. Moreover, associated systems such as Software-Defined Networking (SDN) and Field-Programmable Gate Arrays (FPGA) are a source of good security enhancement for IoT Networks. Thus, we include a detailed discussion of future development encompassing all major IoT subsystems. 

   more » « less
3. Anomaly Detection based on Traffic Monitoring for Secure Blockchain Networking

   https://doi.org/10.1109/ICBC51069.2021.9461119  

   Kim, Jinoh ; Nakashima, Makiya ; Fan, Wenjun ; Wuthier, Simeon ; Zhou, Xiaobo ; Kim, Ikkyun ; Chang, Sang-Yoon ( May 2021 , IEEE International Conference on Blockchain and Cryptocurrency (ICBC))

   While the blockchain technology provides strong cryptographic protection on the ledger and the system operations, the underlying blockchain networking remains vulnerable due to potential threats such as denial of service (DoS), Eclipse, spoofing, and Sybil attacks. Effectively detecting such malicious events should thus be an essential task for securing blockchain networks and services. Due to its importance, several studies investigated anomaly detection in Bitcoin and blockchain networks, but their analyses mainly focused on the blockchain ledger in the application context (e.g., transactions) and targets specific types of attacks (e.g., double-spending, deanonymization, etc). In this study, we present a security mechanism based on the analysis of blockchain network traffic statistics (rather than ledger data) to detect malicious events, through the functions of data collection and anomaly detection. The data collection engine senses the underlying blockchain traffic and generates multi-dimensional data streams in a periodic manner. The anomaly detection engine then detects anomalies from the created data instances based on semi-supervised learning, which is capable of detecting previously unseen patterns, and we introduce our profiling-based detection engine implemented on top of AutoEncoder (AE). Our experimental results support the effectiveness of the presented security mechanism for accurate, online detection of malicious events from blockchain networking traffic data. We also show further reduction in time complexity (up to 66.8% for training and 85.7% for testing), without any performance degradation using feature prioritization compared to the utilization of the entire features. 

   more » « less
4. Exploiting Security Vulnerabilities in Intermittent Computing

   https://doi.org/10.1007/978-3-030-05072-6_7  

   Krishnan, Archanaa ; Schaumont, Patrick ( December 2018 , Security, Privacy, and Applied Cryptography Engineering. SPACE 2018.)

   Energy harvesters have enabled widespread utilization of ultra-low-power devices that operate solely based on the energy harvested from the environment. Due to the unpredictable nature of harvested energy, these devices experience frequent power outages. They resume execution after a power loss by utilizing intermittent computing techniques and non-volatile memory. In embedded devices, intermittent computing refers to a class of computing that stores a snapshot of the system and application state, as a checkpoint, in non-volatile memory, which is used to restore the system and application state in case of power loss. Although non-volatile memory provides tolerance against power failures, they introduce new vulnerabilities to the data stored in them. Sensitive data, stored in a checkpoint, is available to an attacker after a power loss, and the state-of-the-art intermittent computing techniques fail to consider the security of checkpoints. In this paper, we utilize the vulnerabilities introduced by the intermittent computing techniques to enable various implementation attacks. For this study, we focus on TI’s Compute Through Power Loss utility as an example of the state-of-the-art intermittent computing solution. First, we analyze the security, or lack thereof, of checkpoints in the latest intermittent computing techniques. Then, we attack the checkpoints and locate sensitive data in non-volatile memory. Finally, we attack AES using this information to extract the secret key. To the best of our knowledge, this work presents the first systematic analysis of the seriousness of security threats present in the field of intermittent computing. 

   more » « less
5. Detecting Insider attacks in Blockchain Networks

   Ajayi, O. ; Saadawi, T. ( November 2021 , IEEE 2021 International Symposium on Networks, Computers and Communications: Trust, Security, and Privacy (ISNCC- 2021 TSP)

   null (Ed.)

   Blockchain technology has recently gained high popularity in data security, primarily to mitigate against data breach and manipulation. Since its inception in 2008, it has been applied in different areas mainly to maintain data integrity and consistency. Blockchain has been tailored to secure data due to its data immutability and distributive technology. Despite the high success rate in data security, the inability to identify compromised insider nodes is one of the significant problems encountered in blockchain architectures. A Blockchain network is made up of nodes that initiate, verify and validate transactions. If compromised, these nodes can manipulate submitted transactions, inject fake transactions, or retrieve unauthorized information that might eventually compromise the stored data's integrity and consistency. This paper proposes a novel method of detecting these compromised blockchain nodes using a server-side authentication process and thwart their activities before getting updated in the blockchain ledger. In evaluating the proposed system, we perform four common insider attacks, which fall under the following three categories: (1)Those attacks targeting the Blockchain to bring it down. (2) the attacks that attempt to inject fake data into the database. (3) The attacks that attempt to hijack or retrieve unauthorized data. We described how we implement the attacks and how our architecture detects them before they impact the network. Finally, we displayed the attack detection time for each attack and compared our approach with other existing methods. 

   more » « less