Active Sample Selection Based Incremental Algorithm for Attribute Reduction with Rough Sets

Attribute reduction with rough sets is an effective technique for obtaining a compact and informative attribute set from a given dataset. However, traditional algorithms have no explicit provision for handling dynamic datasets where data present themselves in successive samples. Incremental algorithms for attribute reduction with rough sets have been recently introduced to handle dynamic datasets with large samples, though they have high complexity in time and space. To address the time/space complexity issue of the algorithms, this paper presents a novel incremental algorithm for attribute reduction with rough sets based on the adoption of an active sample selection process and an insight into the attribute reduction process. This algorithm first decides whether each incoming sample is useful with respect to the current dataset by the active sample selection process. A useless sample is discarded while a useful sample is selected to update a reduct. At the arrival of a useful sample, the attribute reduction process is then employed to guide how to add and/or delete attributes in the current reduct. The two processes thus constitute the theoretical framework of our algorithm. The proposed algorithm is finally experimentally shown to be efficient in time and space.This is a manuscript of the publication Yang, Yanyan, Degang Chen, and Hui Wang. "Active Sample Selection Based Incremental Algorithm for Attribute Reduction With Rough Sets." IEEE Transactions on Fuzzy Systems 25, no. 4 (2017): 825-838. DOI: 10.1109/TFUZZ.2016.2581186. Posted with permission.</p

The Energy-dependent Checkerboard Patterns in Cuprate Superconductors

Motivated by the recent scanning tunneling microscopy (STM) experiments [J. E. Hoffman {\it et al.}, Science {\bf 297}, 1148 (2002); K. McElroy {\it et al.}, Nature (to be published)], we investigate the real space local density of states (LDOS) induced by weak disorder in a d-wave superconductor. We first present the energy dependent LDOS images around a single weak defect at several energies, and then point out that the experimentally observed checkerboard pattern in the LDOS could be understood as a result of quasiparticle interferences by randomly distributed defects. It is also shown that the checkerboard pattern oriented along $45^0$ to the Cu-O bonds at low energies would transform to that oriented parallel to the Cu-O bonds at higher energies. This result is consistent with the experiments.Comment: 3 pages, 3 figure

A new class of orthosteric uPAR·uPA small-molecule antagonists are allosteric inhibitors of the uPAR·vitronectin interaction

The urokinase receptor (uPAR) is a GPI-anchored cell surface receptor that is at the center of an intricate network of protein-protein interactions. Its immediate binding partners are the serine proteinase urokinase (uPA), and vitronectin (VTN), a component of the extracellular matrix. uPA and VTN bind at distinct sites on uPAR to promote extracellular matrix degradation and integrin signaling, respectively. Here, we report the discovery of a new class of pyrrolone small-molecule inhibitors of the tight ∼1 nM uPAR·uPA protein-protein interaction. These compounds were designed to bind to the uPA pocket on uPAR. The highest affinity compound, namely 7, displaced a fluorescently labeled α-helical peptide (AE147-FAM) with an inhibition constant Ki of 0.7 μM and inhibited the tight uPAR·uPAATF interaction with an IC50 of 18 μM. Biophysical studies with surface plasmon resonance showed that VTN binding is highly dependent on uPA. This cooperative binding was confirmed as 7, which binds at the uPAR·uPA interface, also inhibited the distal VTN·uPAR interaction. In cell culture, 7 blocked the uPAR·uPA interaction in uPAR-expressing human embryonic kidney (HEK-293) cells and impaired cell adhesion to VTN, a process that is mediated by integrins. As a result, 7 inhibited integrin signaling in MDA-MB-231 cancer cells as evidenced by a decrease in focal adhesion kinase (FAK) phosphorylation and Rac1 GTPase activation. Consistent with these results, 7 blocked breast MDA-MB-231 cancer cell invasion with IC50 values similar to those observed in ELISA and surface plasmon resonance competition studies. Explicit-solvent molecular dynamics simulations show that the cooperativity between uPA and VTN is attributed to stabilization of uPAR motion by uPA. In addition, free energy calculations revealed that uPA stabilizes the VTNSMB·uPAR interaction through more favorable electrostatics and entropy. Disruption of the uPAR·VTNSMB interaction by 7 is consistent with the cooperative binding to uPAR by uPA and VTN. Interestingly, the VTNSMB·uPAR interaction was less favorable in the VTNSMB·uPAR·7 complex suggesting potential cooperativity between 7 and VTN. Compound 7 provides an excellent starting point for the development of more potent derivatives to explore uPAR biology

Kairos: Practical Intrusion Detection and Investigation using Whole-system Provenance

Provenance graphs are structured audit logs that describe the history of a system's execution. Recent studies have explored a variety of techniques to analyze provenance graphs for automated host intrusion detection, focusing particularly on advanced persistent threats. Sifting through their design documents, we identify four common dimensions that drive the development of provenance-based intrusion detection systems (PIDSes): scope (can PIDSes detect modern attacks that infiltrate across application boundaries?), attack agnosticity (can PIDSes detect novel attacks without a priori knowledge of attack characteristics?), timeliness (can PIDSes efficiently monitor host systems as they run?), and attack reconstruction (can PIDSes distill attack activity from large provenance graphs so that sysadmins can easily understand and quickly respond to system intrusion?). We present KAIROS, the first PIDS that simultaneously satisfies the desiderata in all four dimensions, whereas existing approaches sacrifice at least one and struggle to achieve comparable detection performance. Kairos leverages a novel graph neural network-based encoder-decoder architecture that learns the temporal evolution of a provenance graph's structural changes to quantify the degree of anomalousness for each system event. Then, based on this fine-grained information, Kairos reconstructs attack footprints, generating compact summary graphs that accurately describe malicious activity over a stream of system audit logs. Using state-of-the-art benchmark datasets, we demonstrate that Kairos outperforms previous approaches.Comment: 23 pages, 16 figures, to appear in the 45th IEEE Symposium on Security and Privacy (S&P'24

Profiling Good Leakage Models For Masked Implementations

Leakage model plays a very important role in side channel attacks. An accurate leakage model greatly improves the efficiency of attacks. However, how to profile a good enough leakage model, or how to measure the accuracy of a leakage model, is seldom studied. Durvaux et al. proposed leakage certification tests to profile good enough leakage model for unmasked implementations. However, they left the leakage model profiling for protected implementations as an open problem. To solve this problem, we propose the first practical higher-order leakage model certification tests for masked implementations. First and second order attacks are performed on the simulations of serial and parallel implementations of a first-order fixed masking. A third-order attack is performed on another simulation of a second-order random masked implementation. The experimental results show that our new tests can profile the leakage models accurately

Towards Optimal Pre-processing in Leakage Detection

An attacker or evaluator can detect more information leakages if he improves the Signal-to-Noise Ratio (SNR) of power traces in his tests. For this purpose, pre-processings such as de-noise, distribution-based traces biasing are used. However, the existing traces biasing schemes can\u27t accurately express the characteristics of power traces with high SNR, making them not ideal for leakage detections. Moreover, if the SNR of power traces is very low, it is very difficult to use the existing de-noise schemes and traces biasing schemes to enhance leakage detection. In this paper, a known key based pre-processing tool named Traces Linear Optimal Biasing (TLOB) is proposed, which performs very well even on power traces with very low SNR. It can accurately evaluate the noise of time samples and give reliable traces optimal biasing. Experimental results show that TLOB significantly reduces number of traces used for detection; correlation coefficients in $\rho$-tests using TLOB approach 1.00, thus the confidence of tests is significantly improved. As far as we know, there is no pre-processing tool more efficient than TLOB. TLOB is very simple, and only brings very limited time and memory consumption. We strongly recommend to use it to pre-process traces in side channel evaluations