A Hierarchical Provable Massive Data Migration Method under Multicloud Storage

Now, many users have stored files on multiple clouds, and sometime, a large number of files are migrated between clouds. Because cloud providers and cloud servers are not entirely trusted, the corruption of user’s files event occur from time to time during the processes of storage and migration. Therefore, integrity verification must be performed, and the time verification overhead should be as low as possible. The existing provable data migrate methods still have the issue of high time overhead when a large number of files are migrated. Aiming at this problem, this paper proposed a hierarchical provable data migration method, which can provide the efficiency of data transfer integrity verification when moving large number of continuous files between clouds. In this paper, the proposed method is described in detail as well as the security analysis performance evaluation. The results proved that the proposed method can significantly decrease the detection latency of files transfer between clouds.

ROBIN: A Robust Optical Binary Neural Network Accelerator

Domain specific neural network accelerators have garnered attention because of their improved energy efficiency and inference performance compared to CPUs and GPUs. Such accelerators are thus well suited for resource-constrained embedded systems. However, mapping sophisticated neural network models on these accelerators still entails significant energy and memory consumption, along with high inference time overhead. Binarized neural networks (BNNs), which utilize single-bit weights, represent an efficient way to implement and deploy neural network models on accelerators. In this paper, we present a novel optical-domain BNN accelerator, named ROBIN , which intelligently integrates heterogeneous microring resonator optical devices with complementary capabilities to efficiently implement the key functionalities in BNNs. We perform detailed fabrication-process variation analyses at the optical device level, explore efficient corrective tuning for these devices, and integrate circuit-level optimization to counter thermal variations. As a result, our proposed ROBIN architecture possesses the desirable traits of being robust, energy-efficient, low latency, and high throughput, when executing BNN models. Our analysis shows that ROBIN can outperform the best-known optical BNN accelerators and many electronic accelerators. Specifically, our energy-efficient ROBIN design exhibits energy-per-bit values that are ∼4 × lower than electronic BNN accelerators and ∼933 × lower than a recently proposed photonic BNN accelerator, while a performance-efficient ROBIN design shows ∼3 × and ∼25 × better performance than electronic and photonic BNN accelerators, respectively.

Tolerating Defects in Low-Power Neural Network Accelerators Via Retraining-Free Weight Approximation

Hardware accelerators are essential to the accommodation of ever-increasing Deep Neural Network (DNN) workloads on the resource-constrained embedded devices. While accelerators facilitate fast and energy-efficient DNN operations, their accuracy is threatened by faults in their on-chip and off-chip memories, where millions of DNN weights are held. The use of emerging Non-Volatile Memories (NVM) further exposes DNN accelerators to a non-negligible rate of permanent defects due to immature fabrication, limited endurance, and aging. To tolerate defects in NVM-based DNN accelerators, previous work either requires extra redundancy in hardware or performs defect-aware retraining, imposing significant overhead. In comparison, this paper proposes a set of algorithms that exploit the flexibility in setting the fault-free bits in weight memory to effectively approximate weight values, so as to mitigate defect-induced accuracy drop. These algorithms can be applied as a one-step solution when loading the weights to embedded devices. They only require trivial hardware support and impose negligible run-time overhead. Experiments on popular DNN models show that the proposed techniques successfully boost inference accuracy even in the face of elevated defect rates in the weight memory.

Compression with unified and accessible byte blocks to enhance management and analyses of UKBB-scale genotypes

Whole-genome sequencing projects of millions of persons contain enormous genotypes, entailing a huge memory burden and time overhead during computation. Here, we introduce Genotype Blocking Compressor (GBC), a method for rapidly compressing large-scale genotypes into a fast-accessible and highly parallelizable format. We demonstrate that GBC has a competitive compression ratio to help save storage space. Furthermore, GBC is the fastest method to access and manage compressed large-scale genotype files (sorting, merging, splitting, etc.). Our results indicate that GBC can help resolve the fundamental problem of time- and space-consuming computation with large-scale genotypes, and conventional analysis would be substantially enhanced if integrated with GBC to access genotypes. Therefore, GBC's advanced data structure and algorithms will accelerate future population-based biomedical research involving big genomics data.

PICO

Memory safety violations such as buffer overflows are a threat to security to this day. A common solution to ensure memory safety for C is code instrumentation. However, this often causes high execution-time overhead and is therefore rarely used in production. Static analyses can reduce this overhead by proving some memory accesses in bounds at compile time. In practice, however, static analyses may fail to verify in-bounds accesses due to over-approximation. Therefore, it is important to additionally optimize the checks that reside in the program. In this article, we present PICO, an approach to eliminate and replace in-bounds checks. PICO exactly captures the spatial memory safety of accesses using Presburger formulas to either verify them statically or substitute existing checks with more efficient ones. Thereby, PICO can generate checks of which each covers multiple accesses and place them at infrequently executed locations. We evaluate our LLVM-based PICO prototype with the well-known SoftBound instrumentation on SPEC benchmarks commonly used in related work. PICO reduces the execution-time overhead introduced by SoftBound by 36% on average (and the code-size overhead by 24%). Our evaluation shows that the impact of substituting checks dominates that of removing provably redundant checks.

An Assessment of Intrusion Detection using Machine Learning on Traffic Statistical Data

<div> <div> <div> <p>Detecting Zero-Day intrusions has been the goal of Cybersecurity, especially intrusion detection for a long time. Machine learning is believed to be the promising methodology to solve that problem, numerous models have been proposed but a practical solution is still yet to come, mainly due to the limitation caused by the out-of-date open datasets available. In this paper, we propose an approach for Zero-Day intrusion detection based on machine learning, using flow-based statistical data generated by CICFlowMeter as training dataset. The machine learning classification model used is selected from eight most popular classification models, based on their cross validation results, in terms of precision, recall, F1 value, area under curve (AUC) and time overhead. Finally, the proposed system is tested on the testing dataset. To evaluate the feasibility and efficiency of tested models, the testing datasets are designed to contain novel types of intrusions (intrusions have not been trained during the training process). The normal data in the datasets are generated from real life traffic flows generated from daily use. Promising results have been received with the accuracy as high as almost 100%, false positive rate as low as nearly 0%, and with a reasonable time overhead. We argue that with the proper selected flow based statistical data, certain machine learning models such as MLP classifier, Quadratic discriminant analysis, K-Neighbor classifier have satisfying performance in detecting Zero-Day attacks. </p> </div> </div> </div>

An Assessment of Intrusion Detection using Machine Learning on Traffic Statistical Data

<div> <div> <div> <p>Detecting Zero-Day intrusions has been the goal of Cybersecurity, especially intrusion detection for a long time. Machine learning is believed to be the promising methodology to solve that problem, numerous models have been proposed but a practical solution is still yet to come, mainly due to the limitation caused by the out-of-date open datasets available. In this paper, we propose an approach for Zero-Day intrusion detection based on machine learning, using flow-based statistical data generated by CICFlowMeter as training dataset. The machine learning classification model used is selected from eight most popular classification models, based on their cross validation results, in terms of precision, recall, F1 value, area under curve (AUC) and time overhead. Finally, the proposed system is tested on the testing dataset. To evaluate the feasibility and efficiency of tested models, the testing datasets are designed to contain novel types of intrusions (intrusions have not been trained during the training process). The normal data in the datasets are generated from real life traffic flows generated from daily use. Promising results have been received with the accuracy as high as almost 100%, false positive rate as low as nearly 0%, and with a reasonable time overhead. We argue that with the proper selected flow based statistical data, certain machine learning models such as MLP classifier, Quadratic discriminant analysis, K-Neighbor classifier have satisfying performance in detecting Zero-Day attacks. </p> </div> </div> </div>

Path Planning Method for UAVs Based on Constrained Polygonal Space and an Extremely Sparse Waypoint Graph

Finding an optimal/quasi-optimal path for Unmanned Aerial Vehicles (UAVs) utilizing full map information yields time performance degradation in large and complex three-dimensional (3D) urban environments populated by various obstacles. A major portion of the computing time is usually wasted on modeling and exploration of spaces that have a very low possibility of providing optimal/sub-optimal paths. However, computing time can be significantly reduced by searching for paths solely in the spaces that have the highest priority of providing an optimal/sub-optimal path. Many Path Planning (PP) techniques have been proposed, but a majority of the existing techniques equally evaluate many spaces of the maps, including unlikely ones, thereby creating time performance issues. Ignoring high-probability spaces and instead exploring too many spaces on maps while searching for a path yields extensive computing-time overhead. This paper presents a new PP method that finds optimal/quasi-optimal and safe (e.g., collision-free) working paths for UAVs in a 3D urban environment encompassing substantial obstacles. By using Constrained Polygonal Space (CPS) and an Extremely Sparse Waypoint Graph (ESWG) while searching for a path, the proposed PP method significantly lowers pathfinding time complexity without degrading the length of the path by much. We suggest an intelligent method exploiting obstacle geometry information to constrain the search space in a 3D polygon form from which a quasi-optimal flyable path can be found quickly. Furthermore, we perform task modeling with an ESWG using as few nodes and edges from the CPS as possible, and we find an abstract path that is subsequently improved. The results achieved from extensive experiments, and comparison with prior methods certify the efficacy of the proposed method and verify the above assertions.