scholarly journals Bloom Filter-Based Secure Data Forwarding in Large-Scale Cyber-Physical Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-12
Author(s):  
Siyu Lin ◽  
Hao Wu
Keyword(s):  
False Positive ◽  
Large Scale ◽  
False Positive Rate ◽  
Bloom Filter ◽  
Cyber Physical Systems ◽  
Security Requirements ◽  
Data Forwarding ◽  
Physical Systems ◽  
Secure Data ◽  
Positive Rate

Cyber-physical systems (CPSs) connect with the physical world via communication networks, which significantly increases security risks of CPSs. To secure the sensitive data, secure forwarding is an essential component of CPSs. However, CPSs require high dimensional multiattribute and multilevel security requirements due to the significantly increased system scale and diversity, and hence impose high demand on the secure forwarding information query and storage. To tackle these challenges, we propose a practical secure data forwarding scheme for CPSs. Considering the limited storage capability and computational power of entities, we adopt bloom filter to store the secure forwarding information for each entity, which can achieve well balance between the storage consumption and query delay. Furthermore, a novel link-based bloom filter construction method is designed to reduce false positive rate during bloom filter construction. Finally, the effects of false positive rate on the performance of bloom filter-based secure forwarding with different routing policies are discussed.

Identification of and Correction for Publication Bias: Comment

2019 ◽  
Author(s):  
Amanda Kvarven ◽  
Eirik Strømland ◽  
Magnus Johannesson
Keyword(s):  
Publication Bias ◽  
False Positive ◽  
Large Scale ◽  
Meta Analysis ◽  
False Positive Rate ◽  
Effect Sizes ◽  
Replication Studies ◽  
Moderate Reduction ◽  
Positive Rate ◽  
Meta Analyses

Andrews & Kasy (2019) propose an approach for adjusting effect sizes in meta-analysis for publication bias. We use the Andrews-Kasy estimator to adjust the result of 15 meta-analyses and compare the adjusted results to 15 large-scale multiple labs replication studies estimating the same effects. The pre-registered replications provide precisely estimated effect sizes, which do not suffer from publication bias. The Andrews-Kasy approach leads to a moderate reduction of the inflated effect sizes in the meta-analyses. However, the approach still overestimates effect sizes by a factor of about two or more and has an estimated false positive rate of between 57% and 100%.

A new analysis of the false positive rate of a Bloom filter

2010 ◽  
Vol 110 (21) ◽  
pp. 944-949 ◽  
Author(s):  
Ken Christensen ◽  
Allen Roginsky ◽  
Miguel Jimeno
Keyword(s):  
False Positive ◽  
False Positive Rate ◽  
Bloom Filter ◽  
Positive Rate

scholarly journals Deep Learning Model Improves Radiologists’ Performance in Detection and Classification of Breast Lesions

2021 ◽  
Author(s):  
Ying-Shi Sun ◽  
Yu-Hong Qu ◽  
Dong Wang ◽  
Yi Li ◽  
Lin Ye ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Roc Curve ◽  
False Positive ◽  
Large Scale ◽  
False Positive Rate ◽  
Training Dataset ◽  
Validation Dataset ◽  
Breast Lesions ◽  
Positive Rate

Abstract Background: Computer-aided diagnosis using deep learning algorithms has been initially applied in the field of mammography, but there is no large-scale clinical application.Methods: This study proposed to develop and verify an artificial intelligence model based on mammography. Firstly, retrospectively collected mammograms from six centers were randomized to a training dataset and a validation dataset for establishing the model. Secondly, the model was tested by comparing 12 radiologists’ performance with and without it. Finally, prospectively multicenter mammograms were diagnosed by radiologists with the model. The detection and diagnostic capabilities were evaluated using the free-response receiver operating characteristic (FROC) curve and ROC curve.Results: The sensitivity of model for detecting lesion after matching was 0.908 for false positive rate of 0.25 in unilateral images. The area under ROC curve (AUC) to distinguish the benign from malignant lesions was 0.855 (95% CI: 0.830, 0.880). The performance of 12 radiologists with the model was higher than that of radiologists alone (AUC: 0.852 vs. 0.808, P = 0.005). The mean reading time of with the model was shorter than that of reading alone (80.18 s vs. 62.28 s, P = 0.03). In prospective application, the sensitivity of detection reached 0.887 at false positive rate of 0.25; the AUC of radiologists with the model was 0.983 (95% CI: 0.978, 0.988), with sensitivity, specificity, PPV, and NPV of 94.36%, 98.07%, 87.76%, and 99.09%, respectively.Conclusions: The artificial intelligence model exhibits high accuracy for detecting and diagnosing breast lesions, improves diagnostic accuracy and saves time.Trial registration: NCT, NCT03708978. Registered 17 April 2018, https://register.clinicaltrials.gov/prs/app/ NCT03708978

scholarly journals Study of challenges faced by Enterprises using Security Information and Event Management (SIEM)

2021 ◽  
Vol 23 (08) ◽  
pp. 511-522
Author(s):  
Mukesh Yadav ◽  
◽  
Dhirendra S Mishra ◽  
Keyword(s):  
False Positive ◽  
User Behavior ◽  
False Positive Rate ◽  
Sensitive Information ◽  
Event Management ◽  
Secure Data ◽  
Secure Data Transmission ◽  
Positive Rate ◽  
Security Information ◽  
Mean Time

The field of information security plays an important role in education, IT, health domain, etc. Much research has been carried out in order to secure data in hardware, on the cloud, and during transmission over the network. A secure data transmission and securing the stored data is still taken as one of the concerned areas. Cloud-based SIEM is used nowadays, which is the art and science to secure the information of the organization. SIEM is Security Information and Event Management, which means securing the organization containing network devices and devices holding critical and sensitive information. In this paper, a survey is carried out to determine the gap in current security providers and areas that need attention. We take logs as input and send them to SIEM for analysis. Whether a SIEM is capable enough to determine the unknown threats and user behavior to identify insider threats. Also, terms such as EPS, False positive Rate, Mean Time to Resolution are used as compassion and aim to keep False positive rate and mean time resolution value low and EPS no restriction.

scholarly journals An Improved Chinese String Comparator for Bloom Filter Based Privacy-Preserving Record Linkage

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1091
Author(s):  
Siqi Sun ◽  
Yining Qian ◽  
Ruoshi Zhang ◽  
Yanqi Wang ◽  
Xinran Li
Keyword(s):  
Record Linkage ◽  
False Positive ◽  
Personal Information ◽  
Chinese Language ◽  
False Positive Rate ◽  
Bloom Filter ◽  
Privacy Preserving ◽  
Computational Time ◽  
Positive Rate ◽  
Language Environment

With the development of information technology, it has become a popular topic to share data from multiple sources without privacy disclosure problems. Privacy-preserving record linkage (PPRL) can link the data that truly matches and does not disclose personal information. In the existing studies, the techniques of PPRL have mostly been studied based on the alphabetic language, which is much different from the Chinese language environment. In this paper, Chinese characters (identification fields in record pairs) are encoded into strings composed of letters and numbers by using the SoundShape code according to their shapes and pronunciations. Then, the SoundShape codes are encrypted by Bloom filter, and the similarity of encrypted fields is calculated by Dice similarity. In this method, the false positive rate of Bloom filter and different proportions of sound code and shape code are considered. Finally, we performed the above methods on the syntheticdatasets, and compared the precision, recall, F1-score and computational time with different values of false positive rate and proportion. The results showed that our method for PPRL in Chinese language environment improved the quality of the classification results and outperformed others with a relatively low additional cost of computation.

scholarly journals A collaborative approach for national cybersecurity incident management

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Oluwafemi Oriola ◽  
Adesesan Barnabas Adeyemo ◽  
Maria Papadaki ◽  
Eduan Kotzé
Keyword(s):  
False Positive ◽  
Large Scale ◽  
False Positive Rate ◽  
Incident Management ◽  
Collaborative Approach ◽  
Total Response ◽  
Content Type ◽  
Attack Scenario ◽  
Positive Rate ◽  
Incident Handling

Purpose Collaborative-based national cybersecurity incident management benefits from the huge size of incident information, large-scale information security devices and aggregation of security skills. However, no existing collaborative approach has been able to cater for multiple regulators, divergent incident views and incident reputation trust issues that national cybersecurity incident management presents. This paper aims to propose a collaborative approach to handle these issues cost-effectively. Design/methodology/approach A collaborative-based national cybersecurity incident management architecture based on ITU-T X.1056 security incident management framework is proposed. It is composed of the cooperative regulatory unit with cooperative and third-party management strategies and an execution unit, with incident handling and response strategies. Novel collaborative incident prioritization and mitigation planning models that are fit for incident handling in national cybersecurity incident management are proposed. Findings Use case depicting how the collaborative-based national cybersecurity incident management would function within a typical information and communication technology ecosystem is illustrated. The proposed collaborative approach is evaluated based on the performances of an experimental cyber-incident management system against two multistage attack scenarios. The results show that the proposed approach is more reliable compared to the existing ones based on descriptive statistics. Originality/value The approach produces better incident impact scores and rankings than standard tools. The approach reduces the total response costs by 8.33% and false positive rate by 97.20% for the first attack scenario, while it reduces the total response costs by 26.67% and false positive rate by 78.83% for the second attack scenario.

scholarly journals The Research of Technology of Bloom Filter Realization in Hardware

2012 ◽  
Vol 6-7 ◽  
pp. 790-795
Author(s):  
Teng Fei Guo ◽  
Jian Biao Mao ◽  
Zhi Gang Sun
Keyword(s):  
False Positive ◽  
Hardware Implementation ◽  
False Positive Rate ◽  
Hardware Design ◽  
Bloom Filter ◽  
False Positives ◽  
Implementation Framework ◽  
Positive Rate ◽  
Efficient Data

Bloom filter is a space-efficient data with a certain probability of false positive . We present a reusable hardware implementation framework, define a module interface to provide users with a customize module, and introduce the constraints of hardware resources in the analysis of false positive rate against the traditional Bloom filter hardware design and analysis of the Bloom filter false positives. Finally, we make verification and analysis of our design combined with the the NetMagic platform.

scholarly journals Locality Sensitive Hardware Signature Variants for Hardware Transactional Memory

2021 ◽  
Author(s):  
sangeetha r ◽  
Satyanarayana Vollala ◽  
Ramasubramanian N
Keyword(s):  
False Positive ◽  
Transactional Memory ◽  
False Positive Rate ◽  
False Negative ◽  
Bloom Filter ◽  
Conflict Detection ◽  
Bloom Filters ◽  
Positive Rate ◽  
Memory Conflicts ◽  
Probabilistic Data Structure

Abstract Lock based techniques have its own limitations like priority inversion, convoying, and deadlock. Lock free techniques overcome those mentioned limitations. Transactional memory (TM) is one leading lock free technique used in recent multi core processors like Intel Haswell and IBM BlueGene/Q. TM has to do data versioning and conflict detection. For conflict detection probabilistic data structure called Bloom Filters are used. Bloom filter based hardware signatures are used in TM. In TM shared memory conflicts like RAW, WAR, and WAW hazards are handled by Bloom Filter (BF). Hardware signatures store memory addresses in hashed form on Bloom filters. Bloom filters are easy to use, performance efficient data structures lead to false positive but never support false negative. Locality sensitive hardware signatures reduce filter occupancy by sharing bits for the contiguous memory addresses, in turn reduces the false positive rate. This paper implements existing H3 – HS and LS – HS proposed by Ricardo Quislant et al. [13]. Also this paper proposes RS – HS, CS – HS, and RO – HS. RO – HS equally spreads addresses among bloom filters thereby reduces filter occupancy. In turn reduced filter occupancy leads to better False Positive Rate.

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