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Abstract Ransomware attacks are increasingly prevalent in recent years. Crypto-ransomware corrupts files on an infected device and demands a ransom to recover them. In computing devices using flash memory storage (e.g., SSD, MicroSD, etc.), existing designs recover the compromised data by extracting the entire raw flash memory image, restoring the entire external storage to a good prior state. This is feasible through taking advantage of the out-of-place updates feature implemented in the flash translation layer (FTL). However, due to the lack of “file” semantics in the FTL, such a solution does not allow a fine-grained data recovery in terms of files. Considering the file-centric nature of ransomware attacks, recovering the entire disk is mostly unnecessary. In particular, the user may just wish a speedy recovery of certain critical files after a ransomware attack. In this work, we have designed$$\textsf{FFRecovery}$$ $\mathrm{FFRecovery}$, a new ransomware defense strategy that can support fine-grained per file data recovery after the ransomware attack. Our key idea is that, to restore a file corrupted by the ransomware, we (1) restore its file system metadata via file system forensics, and (2) extract its file data via raw data extraction from the FTL, and (3) assemble the corresponding file system metadata and the file data. Another essential aspect of$$\textsf{FFRecovery}$$ $\mathrm{FFRecovery}$is that we add a garbage collection delay and freeze mechanism into the FTL so that no raw data will be lost prior to the recovery and, additionally, the raw data needed for the recovery can be always located. A prototype of$$\textsf{FFRecovery}$$ $\mathrm{FFRecovery}$has been developed and our experiments using real-world ransomware samples demonstrate the effectiveness of$$\textsf{FFRecovery}$$ $\mathrm{FFRecovery}$. We also demonstrate that$$\textsf{FFRecovery}$$ $\mathrm{FFRecovery}$has negligible storage cost and performance impact. 

In-memory key-value caches are widely used as a performance-critical layer in web applications, disk-based storage, and distributed systems. The Least Recently Used (LRU) replacement policy has become the de facto standard in those systems since it exploits workload locality well. However, the LRU implementation can be costly due to the rigid data structure in maintaining object priority, as well as the locks for object order updating. Redis as one of the most effective and prevalent deployed commercial systems adopts an approximated LRU policy, where the least recently used item from a small, randomly sampled set of items is chosen to evict. This random sampling-based policy is lightweight and shows its flexibility. We observe that there can exist a significant miss ratio gap between exact LRU and random sampling-based LRU under different sampling size $$K$$ s. Therefore existing LRU miss ratio curve (MRC) construction techniques cannot be directly applied without loss of accuracy. In this paper, we introduce a new probabilistic stack algorithm named KRR to accurately model random sampling based-LRU, and extend it to handle both fixed and variable objects in key-value caches. We present an efficient stack update algorithm that reduces the expected running time of KRR significantly. To improve the performance of the in-memory multi-tenant key-value cache that utilizes random sampling-based replacement, we propose kRedis, a reference locality- and latency-aware memory partitioning scheme. kRedis guides the memory allocation among the tenants and dynamically customizes $$K$$ to better exploit the locality of each individual tenant. Evaluation results over diverse workloads show that our model generates accurate miss ratio curves for both fixed and variable object size workloads, and enables practical, low-overhead online MRC prediction. Equipped with KRR, kRedis delivers up to a 50.2% average access latency reduction, and up to a 262.8% throughput improvement compared to Redis. Furthermore, by comparing with pRedis, a state-of-the-art design of memory allocation in Redis, kRedis shows up to 24.8% and 61.8% improvements in average access latency and throughput, respectively. 

Abstract Plasmonic nanostructures exhibit intriguing optical properties due to spectrally selective plasmon resonance and thus have broad applications, including biochemical sensing and photoelectric detections. However, excited plasmons are often strongly influenced by the substrates supporting the metallic nanostructures, which not only weakens the intrinsic plasmon coupling effect, but also results in a great reduction of optical near‐field enhancement. Here, a plasmonic nanostructure combining collapsible Au‐nanofingers with selective‐etching that enables Au to be suspended is demonstrated, thus avoiding the undesirable influence of the substrates on the local near‐field distribution and forming symmetric electromagnetic‐field enhancements at both the top and bottom surfaces. The polymer support of the Au‐nanofingers is selectively etched by oxygen plasma, while the Au‐cap retains its original size. After an ultrathin dielectric coating is applied on the Au‐nanofingers, suspended Au‐caps with extremely small dielectric gaps are formed via the collapse of neighboring Au‐nanofingers by exposing them to ethanol. These nanostructures can provide a surface‐enhanced Raman scattering (SERS) enhancement of up to ≈10⁹, which is nearly twice that in the nonsuspended system. As a highly active SERS substrate, the label‐free detection of low‐concentration harmful plastic phthalates in a child's urine without any pretreatment is successfully demonstrated, which suggests that this method is suitable for medical prediagnosis. 

Abstract Light beams carrying orbital angular momentum (OAM) in the form of optical vortices have attracted great interest due to their capability for providing a new dimension and approach to manipulate light–matter interactions. Recently, plasmonics has offered efficient ways to focus vortex beams beyond the diffraction limit. However, unlike in the visible and near‐infrared regime, it is still a big challenge to realize plasmonic vortices at far‐infrared and even longer wavelengths. An effective strategy to create deep‐subwavelength near‐field electromagnetic (EM) vortices operating in the low frequency region is proposed. Taking advantage of the asymmetric spatial distribution of EM field supported by a metallic comb‐shaped waveguide, plasmonic vortex modes that are strongly confined in a well‐designed deep‐subwavelength meta‐particle with desired topological charges can be excited. Such unique phenomena are confirmed by the microwave experiments. An equivalent physical model backed up by the numerical simulations is performed to reveal the underlying mechanism of the plasmonic vortex generation. This spoof‐plasmon assisted focusing of EM waves with OAM may find potentials for functional integrated elements and devices operating in the microwave, terahertz, and even far‐infrared regions.