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1. Machine Learning-based Vulnerability Study of Interpose PUFs as Security Primitives for IoT Networks

   https://doi.org/10.1109/NAS51552.2021.9605405  

   Thapaliya, Bipana; Mursi, Khalid T.; Zhuang, Yu (October 2021, 2021 IEEE International Conference on Networking, Architecture and Storage (NAS))

   Security is of importance for communication networks, and many network nodes, like sensors and IoT devices, are resource-constrained. Physical Unclonable Functions (PUFs) leverage physical variations of the integrated circuits to produce responses unique to individual circuits and have the potential for delivering security for low-cost networks. But before a PUF can be adopted for security applications, all security vulnerabilities must be discovered. Recently, a new PUF known as Interpose PUF (IPUF) was proposed, which was tested to be secure against reliability-based modeling attacks and machine learning attacks when the attacked IPUF is of small size. A recent study showed IPUFs succumbed to a divide-and-conquer attack, and the attack method requires the position of the interpose bit known to the attacker, a condition that can be easily obfuscated by using a random interpose position. Thus, large IPUFs may still remain secure against all known modeling attacks if the interpose position is unknown to attackers. In this paper, we present a new modeling attack method of IPUFs using multilayer neural networks, and the attack method requires no knowledge of the interpose position. Our attack was tested on simulated IPUFs and silicon IPUFs implemented on FPGAs, and the results showed that many IPUFs which were resilient against existing attacks cannot withstand our new attack method, revealing a new vulnerability of IPUFs by re-defining the boundary between secure and insecure regions in the IPUF parameter space. 

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2. Hardware Security in Sensor and its Networks

   https://doi.org/10.3389/fsens.2022.850056  

   Monjur, Mohammad Mezanur; Heacock, Joseph; Calzadillas, Joshua; Mahmud, MD Shaad; Roth, John; Mankodiya, Kunal; Sazonov, Edward; Yu, Qiaoyan (May 2022, Frontiers in Sensors)

   Sensor networks and IoT systems have been widely deployed in monitoring and controlling system. With its increasing utilization, the functionality and performance of sensor networks and their applications are not the only design aims; security issues in sensor networks attract more and more attentions. Security threats in sensor and its networks could be originated from various sectors: users in cyber space, security-weak protocols, obsolete network infrastructure, low-end physical devices, and global supply chain. In this work, we take one of the emerging applications, advanced manufacturing, as an example to analyze the security challenges in the sensor network. Presentable attacks—hardware Trojan attack, man-in-the-middle attack, jamming attack and replay attack—are examined in the context of sensing nodes deployed in a long-range wide-area network (LoRaWAN) for advanced manufacturing. Moreover, we analyze the challenges of detecting those attacks. 

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3. Targeted Privacy Attacks by Fingerprinting Mobile Apps in LTE Radio Layer

   https://doi.org/10.1109/DSN58367.2023.00035  

   Baek, Jaejong; Soundrapandian, Pradeep Kumar; Kyung, Sukwha; Wang, Ruoyu; Shoshitaishvili, Yan; Doupé, Adam; Ahn, Gail-Joon (June 2023, 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN))

   IEEE/IFIP (Ed.)

   We investigate the feasibility of targeted privacy attacks using only information available in physical channels of LTE mobile networks and propose three privacy attacks to demonstrate this feasibility: mobile-app fingerprinting attack, history attack, and correlation attack. These attacks can reveal the geolocation of targeted mobile devices, the victim's app usage patterns, and even the relationship between two users within the same LTE network cell. An attacker also may launch these attacks stealthily by capturing radio signals transmitted over the air, using only a passive sniffer as equipment. To ensure the impact of these attacks on mobile users' privacy, we perform evaluations in both laboratory and real-world settings, demonstrating their practicality and dependability. Furthermore, we argue that these attacks can target not only 4G/LTE but also the evolving 5G standards. 

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4. Detecting and Measuring In-The-Wild DRDoS Attacks at IXPs

   https://doi.org/10.1007/978-3-030-80825-9_3  

   Subramani, Karthika; Perdisci, Roberto; Konte, Maria (July 2021, International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment)

   null; null; null; null (Ed.)

   Distributed reflective denial of service (DRDoS) attacks are a popular choice among adversaries. In fact, one of the largest DDoS attacks ever recorded, reaching a peak of 1.3 Tbps against GitHub, was a memcached-based DRDoS attack. More recently, a record-breaking 2.3 Tbps attack against Amazon AWS was due to a CLDAP-based DRDoS attack. Although reflective attacks have been known for years, DRDoS attacks are unfortunately still popular and largely unmitigated. In this paper, we measure in-the-wild DRDoS attacks as observed from a large Internet exchange point (IXP) and provide a number of security-relevant insights. To enable our measurements, we first developed IXmon, an open-source DRDoS detection system specifically designed for deployment at large IXP-like network connectivity providers and peering hubs. We deployed IXmon at Southern Crossroads (SoX), an IXP-like hub that provides both peering and upstream Internet connectivity services to more than 20 research and education (R&E) networks in the South-East United States. In a period of about 21 months, IXmon detected more than 900 DRDoS attacks towards 31 different victim ASes. An analysis of the real-world DRDoS attacks detected by our system shows that most DRDoS attacks are short lived, lasting only a few minutes, but that large-volume, long-lasting, and highly-distributed attacks against R&E networks are not uncommon. We then use the results of our analysis to discuss possible attack mitigation approaches that can be deployed at the IXP level, before the attack traffic overwhelms the victim’s network bandwidth. 

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5. A Remediation Framework Against False Data Injection Cyberattacks Targeting ULTC Transformers to Avoid Voltage Collapse in Smart Distribution Networks

   https://doi.org/10.1109/TIA.2025.3542717  

   Naderi, Ehsan; Asrari, Arash (February 2025, IEEE Transactions on Industry Applications)

   False data injection (FDI) attacks targeting under-load tap changing (ULTC) transformers pose a significant threat to smart distribution networks by exploiting vulnerabilities in the volt-var optimization (VVO) process, leading to potential undervoltage and voltage collapse. The increased integration of renewable energy and cyber-physical systems has expanded the attack surface, making traditional detection methods inadequate. For example, in 2023, attacks on utilities and decentralized components in the United States rose by 200%, with overall cyber threats increasing by 104%, highlighting growing vulnerabilities in distribution systems. To this end, this article proposes a two-stage remediation framework for decentralized FDI (DFDI) attacks targeting ULTC transformers. In the attack stage, vulnerabilities in ULTCs and voltage regulators are scrutinized, risking voltage collapse or blackouts in the distribution system. In the remediation stage, the distribution system operator focuses on non-attacked ULTCs, voltage regulators, distributed generation (DG) units, and smart homes to minimize reliance on compromised components. In this regard, a distinctive formulation of distribution network resilience and load management (DNRLM) problem is introduced to identify a resilient network topology and determine a situational power balance strategy. The proposed framework focuses on minimizing the system's reliance on the attacked ULTCs and voltage regulator components, thereby avoiding the intended voltage collapse caused by such DFDIs. The simulation results verify that the proposed method reduces the voltage collapse proximity index by over 60%, enhancing system resilience under DFDI attacks. 

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