Abstract Ransomware has become one of the most destructive cyberattacks worldwide in recent years and has caused billions of dollars. Numerous defense mechanisms have been proposed to mitigate the ransomware threat. However, as attack technologies advance, ransomware has rapidly evolved. It employs sophisticated techniques to evade traditional defense mechanisms and has lead to devastating impacts on organizations globally. While many studies have focused on ransomware and its defense, none have provided a comprehensive overview of the ongoing battle between defense mechanisms and the evolving ransomware. They also do not explore the techniques employed by ransomware authors to evade detection. To fill this gap and motivate further research, we conduct an extensive investigation into evasive ransomware, including the common techniques they employ and the efforts researchers have made to counter them. Based on this, we offer a preliminary exploration of potential defense concepts that track multi-level events across different attack stages and leverage the correlations between them to construct the attack flow. This paper helps researchers in the ransomware field gain a comprehensive understanding of evasive ransomware. It also offers insights for future researchers to enhance defense mechanisms to cover the potential threats identified in this study, minimizing the losses caused by evasive ransomware.

This article presents an adaptive integral backstepping sliding mode controller (AIBSC) for a class of microgravity electromagnetic vibration isolation systems (MEVIS) that exhibit nonlinearity and uncertainty. To address the errors caused by the backstepping strategy, a finite-time command filter is applied. Additionally, to overcome acceleration disturbances, a fixed-time observer is used for estimation. Lyapunov theory is employed to prove the global asymptotic stability of the proposed controllers. Finally, simulations demonstrate that this controller ensures system stability while improving the isolation performance.

Although Deep Neural Network (DNN) architectures have made some breakthroughs in computer vision tasks, they are not close to biological brain neurons. Spiking Neural Network (SNN) is highly expected to bridge the gap between artificial computing systems and bio-systems. And it also shows great potential in low power computing. This paper presents a low power hardware accelerator for SNN inference using systolic array, and a corresponding software framework for optimization. First, we give the hardware design which adopts systolic array inspired by explorations of SNN. Then we ensure correct data mapping for systolic array for the sake of computational correctness. Next, we use compression methods for decreasing both the runtime and memory footprint. Finally, we make the systolic array size-configurable to adapt to different input, so as to reduce computational overhead. We implement the accelerator on Xilinx FPGA V7 690T. The experimental results show that SNN inference on our scheme suffers little loss on accuracy (less than 0.1%) on MNIST and Fashion-MNIST, and the runtime of the time-consuming layers decreases. The total power of our scheme is 0.745 W at 100 MHz.