Varun Kohli

Varun Kohli, Arijit Bhattacharjee, Samar Shailendra et al.

Low-Earth Orbit (LEO) mega-constellations such as Starlink by SpaceX and Kuiper by Amazon rely on optical Inter-Satellite Links (ISLs) for autonomous mesh routing to provide low-latency telecommunication, Internet of Things (IoT), and security services globally. As commercial operators and governments deploy increasingly dense constellations and form multi-operator peering coalitions, ISL integrity becomes critical to both commercial availability and national security. However, there is a lack of real-world data for LEO constellations and existing real-time security approaches focus strictly on physical layer security, leaving blind spots in the coverage of network-layer and composite attacks. In this paper, we present a cross-layer, lightweight behavioral fingerprinting framework that fuses onboard physical-layer measurements with network-layer data to detect anomalies at low computational overhead. We construct an orbital simulation covering the first shells of Starlink (1,584 satellites), Kuiper (1,156 satellites), and a joint multi-operator peering scenario (2,740 satellites), injecting ten attack types that span spoofing, traffic manipulation, and routing subversion at varying severity. We evaluate three unsupervised, per-satellite detectors among which our Mahalanobis-distance-based detector achieves 99.5% recall on Starlink, 99.4% on Kuiper, and 94.8\% on the multi-operator constellation, while maintaining False Positive Rates (FPR) below 0.7%. Our results demonstrate that cross-layer feature fusion is not only necessary for comprehensive security of LEO constellations but highly cost-effective for large-scale networks while fitting into the strict onboard energy budgets of resource-constrained satellites.

Varun Kohli, Biplab Sikdar

As the Internet of Things (IoT) becomes an integral part of critical infrastructure, smart cities, and consumer networks, there has been an increase in the number of software attacks on the microcontrollers (MCUs) that constitute such networks. Runtime firmware attestation, i.e., the verification of a firmware's integrity, has become instrumental, and prior work focuses on lightweight IoT MCUs, offloading the verification task to capable remote verifiers. However, modern IoT devices feature large flash and volatile memory, on-device TinyML inference, and Trusted Execution Environments (TEE). Leveraging these capabilities, this paper presents a verifier-less, hybrid Self-Attestation (SA) framework called LiteAtt, which is based on TinyML execution in the Arm TrustZone of an IoT MCU for quick, on-device evaluation of the IoT firmware's SRAM footprint. LiteAtt takes a step towards ubiquitous intelligence and decentralized trust in IoT networks. It eliminates the need for firmware copies for attestation, and protects the privacy of user SRAM data by leveraging twin devices to train the TinyML models. The proposed framework achieves an average accuracy of 98.7%, F1 score of 99.33%, TPR of 98.72%, and TNR of 97.45% on SRAM attestation datasets collected from real devices. LiteAtt operates with a latency of 1.29ms, an energy consumption of 42.79uJ, and a runtime memory overhead of up to 32KB, which is suitable for battery-operated Arm Cortex-M devices. A security analysis is provided for the protocol regarding mutual authentication, confidentiality, integrity, SRAM privacy, and defense against replay and impersonation attacks. Practical deployment scenarios and future works are also discussed.

Aayush Jain, Varun Kohli, Girish Mishra

Recent years have seen an increasing involvement of Deep Learning in the cryptanalysis of various ciphers. The present study is inspired by past works on differential distinguishers, to develop a Deep Neural Network-based differential distinguisher for round reduced lightweight block ciphers PRESENT and Simeck. We make improvements in the state-of-the-art approach and extend its use to the two structurally different block ciphers, PRESENT-80 and Simeck64/128. The obtained results suggest the universality of our cryptanalysis method. The proposed method can distinguish random data from the cipher data obtained until 6 rounds of PRESENT and 7 rounds of Simeck encryption with high accuracy. In addition to this, we explore a new approach to select good input differentials, which to the best of our knowledge has not been explored in the past. We also provide a minimum-security requirement for the discussed ciphers against our differential attack.