We propose a novel whitelist-based anti-ransomware solution called alohomora. Alohomora is based on our observation that an I/O activity of an application can be an effective abstraction level for managing I/O whitelisting. In alohomora, when a write request is sent to an SSD, its program context value (which is supported by a host CPU register) is passed to the SSD. The SSD checks if the request was pre-approved using the program context value, thus preventing ransomware from modifying files in the SSD. Our experimental results using a prototype alohomora system show that alohomora can achieve a strong security level against sophisticated ransomware attacks without degrading I/O performance.

A recent ultra-large SSD (e.g., a 32-TB SSD) provides many benefits in building cost-efficient enterprise storage systems. Owing to its large capacity, however, when such SSDs fail in a RAID storage system, a long rebuild overhead is inevitable for RAID reconstruction that requires a huge amount of data copies among SSDs. Motivated by modern SSD failure characteristics, we propose a new recovery scheme, called reparo , for a RAID storage system with ultra-large SSDs. Unlike existing RAID recovery schemes, reparo repairs a failed SSD at the NAND die granularity without replacing it with a new SSD, thus avoiding most of the inter-SSD data copies during a RAID recovery step. When a NAND die of an SSD fails, reparo exploits a multi-core processor of the SSD controller in identifying failed LBAs from the failed NAND die and recovering data from the failed LBAs. Furthermore, reparo ensures no negative post-recovery impact on the performance and lifetime of the repaired SSD. Experimental results using 32-TB enterprise SSDs show that reparo can recover from a NAND die failure about 57 times faster than the existing rebuild method while little degradation on the SSD performance and lifetime is observed after recovery.

As data privacy and security rapidly become key requirements, securely erasing data from a storage system becomes as important as reliably storing data in the system. Unfortunately, in modern flash-based storage systems, it is challenging to irrecoverably erase (i.e., sanitize) a file without large performance or reliability penalties. In this paper, we propose Evanesco, a new data sanitization technique specifically designed for high-density 3D NAND flash memory. Unlike existing techniques that physically destroy stored data, Evanesco provides data sanitization by blocking access to stored data. By exploiting existing spare flash cells in the flash memory chip, Evanesco efficiently supports two new flash lock commands (pLock and bLock) that disable access to deleted data at both page and block granularities. Since the locked page (or block) can be unlocked only after its data is erased, Evanesco provides a strong security guarantee even against an advanced threat model. To evaluate our technique, we build SecureSSD, an Evanesco-enabled emulated flash storage system. Our experimental results show that SecureSSD can effectively support data sanitization with a small performance overhead and no reliability degradation.

3D NAND flash memory exhibits two contrasting process characteristics from its manufacturing process. While process variability between different horizontal layers are well known, little has been systematically investigated about strong process similarity (PS) within the horizontal layer. In this paper, based on an extensive characterization study using real 3D flash chips, we show that 3D NAND flash memory possesses very strong process similarity within a 3D flash block: the word lines (WLs) on the same horizontal layer of the 3D flash block exhibit virtually equivalent reliability characteristics. This strong process similarity, which was not previously utilized, opens simple but effective new optimization opportunities for 3D flash memory. In this paper, we focus on exploiting the process similarity for improving the I/O latency. By carefully reusing various flash operating parameters monitored from accessing the leading WL, the remaining WLs on the same horizontal layer can be quickly accessed, avoiding unnecessary redundant steps for subsequent program and read operations. We also propose a new program sequence, called mixed order scheme (MOS), for 3D NAND flash memory which can further reduce the program latency. We have implemented a PS-aware FTL, called cubeFTL, which takes advantage of the proposed techniques. Our evaluation results show that cubeFTL can improve the IOPS by up to 48% over an existing PS-unaware FTL.

SUMMARY: Neuropeptides are essential for cell-cell communication in neurological and endocrine physiological processes in health and disease. While many neuropeptides have been identified in previous studies, the resulting data has not been structured to facilitate further analysis by tandem mass spectrometry (MS/MS), the main technology for high-throughput neuropeptide identification. Many neuropeptides are difficult to identify when searching MS/MS spectra against large protein databases because of their atypical lengths (e.g. shorter/longer than common tryptic peptides) and lack of tryptic residues to facilitate peptide ionization/fragmentation. NeuroPedia is a neuropeptide encyclopedia of peptide sequences (including genomic and taxonomic information) and spectral libraries of identified MS/MS spectra of homolog neuropeptides from multiple species. Searching neuropeptide MS/MS data against known NeuroPedia sequences will improve the sensitivity of database search tools. Moreover, the availability of neuropeptide spectral libraries will also enable the utilization of spectral library search tools, which are known to further improve the sensitivity of peptide identification. These will also reinforce the confidence in peptide identifications by enabling visual comparisons between new and previously identified neuropeptide MS/MS spectra. AVAILABILITY: http://proteomics.ucsd.edu/Software/NeuroPedia.html CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.