Streaming (machine) learning (SML) can capture dynamic changes in real-time data and perform continuous updates. It has been widely applied in real-world scenarios such as network security, financial regulation, and energy supply. How-ever, due to the sensitivity and lightweight nature of SML models, existing work suffers from low robustness, sudden decline, and catastrophic forgetting when facing unexpected data distribution drifts. Previous studies have attempted to enhance the stability of SML through methods such as data selection, replay, and constraints. However, these methods are typically designed for specific feature spaces and specific ML algorithms. In this paper, we introduce a shift graph based on the distances between data distributions and define three distinct data shift patterns. For these three patterns, we design three adaptive mechanisms, (a) multi-time granularity models, (b) coherent experience clustering, and (c) historical knowledge reuse, that are triggered by a strategy selector, with the goal of enhancing the accuracy and stability of SML. We implement an adaptive and stable SML framework, FreewayML, on top of PyTorch, which is suitable for most SML models. Experimental results show that FreewayML significantly outperforms existing SML systems in both stability and accuracy, with a comparable throughput and latency.

Developing correct multi-threaded programs is challenging and concurrency bugs can be easily introduced. Many of them, known as concurrency vulnerabilities, can be exploited to launch attacks. Fuzzing is shown to be a practical and effective technique to expose vulnerabilities. However, existing works on fuzzing concurrency vulnerabilities almost all follow the framework (like AFL++) designed for fuzzing sequential vulnerabilities. Unlike sequential vulnerabilities, concurrency ones cannot be easily triggered. Concurrency vulnerabilities rely on both inputs and thread interleaving to be exposed while existing fuzzing techniques mainly focus on how to generate effective inputs. We present a new framework based on an existing fuzzing technique, AFL++, to integrate the predictive techniques for effective concurrency vulnerability detection. For every input (the original and the mutated ones), we call a predictive tool such that, even if a concurrency vulnerability is not really triggered, it can be predicted. To overcome heavy efficiency challenges existing in predictive tools, we propose to selectively call a predictive tool based on concurrency coverage criteria. We have selected a sound predictive tool SeqCheck and adapted it to propose our fuzzing framework PredFuzz. We compared our tool with two tools, AFL++ integrated with Google ThreadSanitizer and AFL++ directly integrated with SeqCheck, on six previously studied multi-threaded programs. The experimental results showed that PredFuzz detected significantly more vulnerabilities than AFL++ integrated with ThreadSanitizer and about 70% vulnerabilities detected by AFL++ directly integrated with SeqCheck. Besides, it is extremely efficient without compromising the fuzzing speed of AFL++: it added a smaller slowdown to AFL++ than ThreadSanitizer did and achieved a speedup of more than 1,000x when compared to AFL++ directly integrated with SeqCheck.

At present, ‘mixed-type’ multivariate schemes are relatively rare except the Dragon scheme and its variants (Little Dragon Two scheme and Poly-Dragon scheme). However, they are insecure. In this paper, we first define a particular polynomial called Three-color Polynomial (this polynomial has three-class variables, and the form of the associated symmetric matrix of its quadratic part is similar to the ‘three-color model’ in colorimetry. So we call it three-color polynomial), and its corresponding Three-color Map. Based on the three-color map, we then present a mixed multivariate signature scheme named RGB (it means Red–Green–Blue, because the central map of this scheme is a three-color map, and the ‘three-color’ stands for RGB in colorimetry), which is a variant of the Unbalanced Oil–Vinegar (UOV) signature scheme. Compared with UOV, each polynomial of the central map of RGB has more cross-terms among all the variables |$\{Y,Z,T\}$|⁠. The variable |$Y$| has much to do with message values. To a certain degree, the variable |$Y$| stands for the message values. This means that the message values can be more fully mixed with other variable values in the central map, and the adversary is very difficult to forge the signature. Thus, in theory RGB is more secure than UOV. Through detailed analysis, we find that RGB can resist current known algebraic attacks under proper parameters, such as exhaustive search attack, separation attack, MinRank attack and direct attack (other algebraic attacks are inapplicable for RGB). Besides, our experiments show that under choosing the security level of |$2^{80}$|⁠, the signing time of Magma implementation of RGB is 0.046 s on an ordinary Linux-PC with 2.50 GHz, and the signing time of its C implementation is |$\boldsymbol {\sim }$|0.003 s on an 800 MHz machine. The comparisons show that the signing speed of RGB is faster than that of |${\rm Sflash}^{v2}$|⁠, Quartz, UOV, Rainbow and RSA-1024, and is slightly slower than that of ECDSA-163 and NTRUSign-251. Overall, this new scheme can attain very good performance in terms of security and efficiency.