pAElla: Edge-AI based Real-Time Malware Detection in Data Centers

The increasing use of Internet-of-Things (IoT) devices for monitoring a wide spectrum of applications, along with the challenges of "big data" streaming support they often require for data analysis, is nowadays pushing for an increased attention to the emerging edge computing paradigm. In particular, smart approaches to manage and analyze data directly on the network edge, are more and more investigated, and Artificial Intelligence (AI) powered edge computing is envisaged to be a promising direction. In this paper, we focus on Data Centers (DCs) and Supercomputers (SCs), where a new generation of high-resolution monitoring systems is being deployed, opening new opportunities for analysis like anomaly detection and security, but introducing new challenges for handling the vast amount of data it produces. In detail, we report on a novel lightweight and scalable approach to increase the security of DCs/SCs, that involves AI-powered edge computing on high-resolution power consumption. The method -- called pAElla -- targets real-time Malware Detection (MD), it runs on an out-of-band IoT-based monitoring system for DCs/SCs, and involves Power Spectral Density of power measurements, along with AutoEncoders. Results are promising, with an F1-score close to 1, and a False Alarm and Malware Miss rate close to 0%. We compare our method with State-of-the-Art MD techniques and show that, in the context of DCs/SCs, pAElla can cover a wider range of malware, significantly outperforming SoA approaches in terms of accuracy. Moreover, we propose a methodology for online training suitable for DCs/SCs in production, and release open dataset and code

On the cost of computing isogenies between supersingular elliptic curves

The security of the Jao-De Feo Supersingular Isogeny Diffie-Hellman (SIDH) key agreement scheme is based on the intractability of the Computational Supersingular Isogeny (CSSI) problem --- computing ${\mathbb F}_{p^2}$-rational isogenies of degrees $2^e$ and $3^e$ between certain supersingular elliptic curves defined over ${\mathbb F}_{p^2}$. The classical meet-in-the-middle attack on CSSI has an expected running time of $O(p^{1/4})$, but also has $O(p^{1/4})$ storage requirements. In this paper, we demonstrate that the van Oorschot-Wiener collision finding algorithm has a lower cost (but higher running time) for solving CSSI, and thus should be used instead of the meet-in-the-middle attack to assess the security of SIDH against classical attacks. The smaller parameter $p$ brings significantly improved performance for SIDH

Investigation of Parallel Data Processing Using Hybrid High Performance CPU + GPU Systems and CUDA Streams

The paper investigates parallel data processing in a hybrid CPU+GPU(s) system using multiple CUDA streams for overlapping communication and computations. This is crucial for efficient processing of data, in particular incoming data stream processing that would naturally be forwarded using multiple CUDA streams to GPUs. Performance is evaluated for various compute time to host-device communication time ratios, numbers of CUDA streams, for various numbers of threads managing computations on GPUs. Tests also reveal benefits of using CUDA MPS for overlapping communication and computations when using multiple processes. Furthermore, using standard memory allocation on a GPU and Unified Memory versions are compared, the latter including programmer added prefetching. Performance of a hybrid CPU+GPU version as well as scaling across multiple GPUs are demonstrated showing good speed-ups of the approach. Finally, the performance per power consumption of selected configurations are presented for various numbers of streams and various relative performances of GPUs and CPUs

Cryptography: Against AI and QAI Odds

Artificial Intelligence (AI) presents prodigious technological prospects for development, however, all that glitters is not gold! The cyber-world faces the worst nightmare with the advent of AI and quantum computers. Together with Quantum Artificial Intelligence (QAI), they pose a catastrophic threat to modern cryptography. It would also increase the capability of cryptanalysts manifold, with its built-in persistent and extensive predictive intelligence. This prediction ability incapacitates the constrained message space in device cryptography. With the comparison of these assumptions and the intercepted ciphertext, the code-cracking process will considerably accelerate. Before the vigorous and robust developments in AI, we have never faced and never had to prepare for such a plaintext-originating attack. The supremacy of AI can be challenged by creating ciphertexts that would give the AI attacker erroneous responses stymied by randomness and misdirect them. AI threat is deterred by deviating from the conventional use of small, known-size keys and pattern-loaded ciphers. The strategy is vested in implementing larger secret size keys, supplemented by ad-hoc unilateral randomness of unbound limitations and a pattern-devoid technique. The very large key size can be handled with low processing and computational burden to achieve desired unicity distances. The strategy against AI odds is feasible by implementing non-algorithmic randomness, large and inexpensive memory chips, and wide-area communication networks. The strength of AI, i.e., randomness and pattern detection can be used to generate highly optimized ciphers and algorithms. These pattern-devoid, randomness-rich ciphers also provide a timely and plausible solution for NIST's proactive approach toward the quantum challenge

Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation

Quantum computing is a game-changing technology for global academia, research centers and industries including computational science, mathematics, finance, pharmaceutical, materials science, chemistry and cryptography. Although it has seen a major boost in the last decade, we are still a long way from reaching the maturity of a full-fledged quantum computer. That said, we will be in the Noisy-Intermediate Scale Quantum (NISQ) era for a long time, working on dozens or even thousands of qubits quantum computing systems. An outstanding challenge, then, is to come up with an application that can reliably carry out a nontrivial task of interest on the near-term quantum devices with non-negligible quantum noise. To address this challenge, several near-term quantum computing techniques, including variational quantum algorithms, error mitigation, quantum circuit compilation and benchmarking protocols, have been proposed to characterize and mitigate errors, and to implement algorithms with a certain resistance to noise, so as to enhance the capabilities of near-term quantum devices and explore the boundaries of their ability to realize useful applications. Besides, the development of near-term quantum devices is inseparable from the efficient classical simulation, which plays a vital role in quantum algorithm design and verification, error-tolerant verification and other applications. This review will provide a thorough introduction of these near-term quantum computing techniques, report on their progress, and finally discuss the future prospect of these techniques, which we hope will motivate researchers to undertake additional studies in this field.Comment: Please feel free to email He-Liang Huang with any comments, questions, suggestions or concern