Publications

In this paper, we present a very first study to investigate trust and ethical implications of on-device artificial intelligence (AI), focusing on ''small'' language models (SLMs) amenable for personal devices like smartphones. While on-device SLMs promise enhanced privacy, reduced latency, and improved user experience compared to cloud-based services, we posit that they might also introduce significant challenges and vulnerabilities compared to on-server counterparts. As part of our trust assessment study, we conduct a systematic evaluation of the state-of-the-art on-devices SLMs, contrasted to their on-server counterparts, based on a well-established trustworthiness measurement framework. Our results show on-device SLMs to be (statistically) significantly less trustworthy, specifically demonstrating more stereotypical, unfair and privacy-breaching behavior. Informed by these findings, we then perform our ethics assessment study by inferring whether SLMs would provide responses to potentially unethical vanilla prompts, collated from prior jailbreaking and prompt engineering studies and other sources. Strikingly, the on-device SLMs did answer valid responses to these prompts, which ideally should be rejected. Even more seriously, the on-device SLMs responded with valid answers without any filters and without the need for any jailbreaking or prompt engineering. These responses can be abused for various harmful and unethical scenarios including: societal harm, illegal activities, hate, self-harm, exploitable phishing content and exploitable code, all of which indicates the high vulnerability and exploitability of these on-device SLMs. Overall, our findings highlight gaping vulnerabilities in state-of-the-art on-device AI which seem to stem from resource constraints faced by these models and which may make typical defenses fundamentally challenging to be deployed in these environments.

In this research, we introduce MIND-Crypt, a novel attack framework that uses deep learning (DL) and transfer learning (TL) to challenge the indistinguishability of block ciphers, specifically SPECK32/64 encryption algorithm in CBC mode (Cipher Block Chaining) against Known Plaintext Attacks (KPA). Our methodology includes training a DL model with ciphertexts of two messages encrypted using the same key. The selected messages have the same byte-length and differ by only one bit at the binary level. This DL model employs a residual network architecture. For the TL, we use the trained DL model as a feature extractor, and these features are then used to train a shallow machine learning, such as XGBoost. This dual strategy aims to distinguish ciphertexts of two encrypted messages, addressing traditional cryptanalysis challenges. Our findings demonstrate that the DL model achieves an accuracy of approximately 99% under consistent cryptographic conditions (Same Key or Rounds) with the SPECK32/64 cipher. However, performance degrades to random guessing levels (50%) when tested with ciphertext generated from different keys or different encryption rounds of SPECK32/64. To enhance the results, the DL model requires retraining with different keys or encryption rounds using larger datasets (10^7 samples). To overcome this limitation, we implement TL, achieving an accuracy of about 53% with just 10,000 samples, which is better than random guessing. Further training with 580,000 samples increases accuracy to nearly 99%, showing a substantial reduction in data requirements by over 94%. This shows that an attacker can utilize machine learning models to break indistinguishability by accessing pairs of plaintexts and their corresponding ciphertexts encrypted with the same key, without directly interacting with the communicating parties.

In the design of planar wireless sensor networks (PWSNs), the preliminary tasks are to achieve both coverage and connectivity, which are essential for the correct operation of this type of network. A PWSN is said to be in perfect operational condition only when it is capable of guaranteeing both coverage of the field and connectivity among all the active sensor nodes. In order to achieve both coverage and connectivity in PWSNs, we intend to solve the problem of connected k-coverage in PWSNs, where each point in a field of interest is covered (or sensed) by at least k sensor nodes (k > 1) simultaneously and all the participating sensor nodes in the k-coverage process are connected to each other. In our study, we found an irregular hexagon, denoted by IrHx(rs/n), as the best polygon for tessellating a field of interest and allowing to deploy sensor nodes, where n > 1 is a natural number and rs is the radius of the sensing range of the sensor nodes. First, we construct our proposed irregular hexagon, IrHx(rs/n), which forms a tile, using the regular hexagon. Second, we compute the minimum sensor density required to k-cover a planar field of interest using our proposed IrHx(rs/n) tile. Third, we establish a relationship between the sensing and communication radii of the sensor nodes to ensure network connectivity in k-covered PWSNs. Finally, we substantiate our theory with various simulation results.

Quantum era is near and advanced quantum computing will pose a huge threat to the security of the current cryptographic systems in distributed energy resources (DER)rich power grids. It is necessary to prepare now to combat quantum computing attacks with serious real-world consequences in the years ahead. Recently, post-quantum cryptography (PQC) is considered as one of the major candidates of quantum attack defense strategies, and the adoption of PQC for DER systems has not been fully studied yet. This paper discusses the adoption of PQC in a standard DER network protocol (i.e., IEEE 2030.5-PQC), and proposes a real-time hardware-in-the-loop co-simulation PQC testbed consisting of a DER physical system simulation and a cyber system simulation such as DER gateways and DER management system (DERMS) server. Universal custom-made PQC client and server software are developed to meet the compliant with IEEE 2030.5-2018 standard and implemented in the DER gateways and the DERMS server, respectively. Finally, the feasibility of the proposed PQC-grade DER network is validated by using the real-time co-simulation testbed.