The Effect of Weighted Moving Windows on Security Vulnerability Prediction

With virtual assistants, both changes and serious conveniences are provided in human life. For this reason, the use of virtual assistants is increasing. The virtual assistant software has started to be produced as separate devices as well as working on phones, tablets, and computer systems. Google Home is one of these devices. Google Home can work integrated with smart home systems and various Internet of Things devices. The security of these systems is an important issue. As a result of attackers taking over these systems, very serious problems may occur. It is very important to take the necessary actions to detect these problems and to take the necessary measures to prevent possible attacks. The purpose of this study is to test whether an attack that attackers can make to these systems via network time protocol will be successful or not. Accordingly, it has been tried to attack the wireless connection established between Google Home and an Internet of Things device over the network time protocol. Attack results have been shared.

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Change Point Problem in Security Vulnerability Discovery Model

2019 International Conference on Software Security and Assurance (ICSSA) ◽

10.1109/icssa48308.2019.00010 ◽

2019 ◽

Author(s):

Ankur Shukla ◽

Basel Katt

Keyword(s):

Change Point ◽

Point Problem ◽

Security Vulnerability ◽

Change Point Problem ◽

Vulnerability Discovery

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Lags in the release, adoption, and propagation of npm vulnerability fixes

Empirical Software Engineering ◽

10.1007/s10664-021-09951-x ◽

2021 ◽

Vol 26 (3) ◽

Author(s):

Bodin Chinthanet ◽

Raula Gaikovina Kula ◽

Shane McIntosh ◽

Takashi Ishio ◽

Akinori Ihara ◽

...

Keyword(s):

Empirical Study ◽

Empirical Investigation ◽

Third Party ◽

Future Research ◽

Software Ecosystem ◽

Security Vulnerability ◽

Preliminary Study ◽

Client Side

AbstractSecurity vulnerability in third-party dependencies is a growing concern not only for developers of the affected software, but for the risks it poses to an entire software ecosystem, e.g., Heartbleed vulnerability. Recent studies show that developers are slow to respond to the threat of vulnerability, sometimes taking four to eleven months to act. To ensure quick adoption and propagation of a release that contains the fix (fixing release), we conduct an empirical investigation to identify lags that may occur between the vulnerable release and its fixing release (package-side fixing release). Through a preliminary study of 231 package-side fixing release of npm projects on GitHub, we observe that a fixing release is rarely released on its own, with up to 85.72% of the bundled commits being unrelated to a fix. We then compare the package-side fixing release with changes on a client-side (client-side fixing release). Through an empirical study of the adoption and propagation tendencies of 1,290 package-side fixing releases that impact throughout a network of 1,553,325 releases of npm packages, we find that stale clients require additional migration effort, even if the package-side fixing release was quick (i.e., package-side fixing releasetypeSpatch). Furthermore, we show the influence of factors such as the branch that the package-side fixing release lands on and the severity of vulnerability on its propagation. In addition to these lags we identify and characterize, this paper lays the groundwork for future research on how to mitigate propagation lags in an ecosystem.

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Predicting plaque vulnerability change using intravascular ultrasound + optical coherence tomography image-based fluid–structure interaction models and machine learning methods with patient follow-up data: a feasibility study

BioMedical Engineering OnLine ◽

10.1186/s12938-021-00868-6 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Xiaoya Guo ◽

Akiko Maehara ◽

Mitsuaki Matsumura ◽

Liang Wang ◽

Jie Zheng ◽

...

Keyword(s):

Machine Learning ◽

Coronary Plaque ◽

Support Vector ◽

Single Factor ◽

Plaque Vulnerability ◽

Learning Methods ◽

Machine Learning Methods ◽

Vulnerability Prediction ◽

Biomechanical Factors

Abstract Background Coronary plaque vulnerability prediction is difficult because plaque vulnerability is non-trivial to quantify, clinically available medical image modality is not enough to quantify thin cap thickness, prediction methods with high accuracies still need to be developed, and gold-standard data to validate vulnerability prediction are often not available. Patient follow-up intravascular ultrasound (IVUS), optical coherence tomography (OCT) and angiography data were acquired to construct 3D fluid–structure interaction (FSI) coronary models and four machine-learning methods were compared to identify optimal method to predict future plaque vulnerability. Methods Baseline and 10-month follow-up in vivo IVUS and OCT coronary plaque data were acquired from two arteries of one patient using IRB approved protocols with informed consent obtained. IVUS and OCT-based FSI models were constructed to obtain plaque wall stress/strain and wall shear stress. Forty-five slices were selected as machine learning sample database for vulnerability prediction study. Thirteen key morphological factors from IVUS and OCT images and biomechanical factors from FSI model were extracted from 45 slices at baseline for analysis. Lipid percentage index (LPI), cap thickness index (CTI) and morphological plaque vulnerability index (MPVI) were quantified to measure plaque vulnerability. Four machine learning methods (least square support vector machine, discriminant analysis, random forest and ensemble learning) were employed to predict the changes of three indices using all combinations of 13 factors. A standard fivefold cross-validation procedure was used to evaluate prediction results. Results For LPI change prediction using support vector machine, wall thickness was the optimal single-factor predictor with area under curve (AUC) 0.883 and the AUC of optimal combinational-factor predictor achieved 0.963. For CTI change prediction using discriminant analysis, minimum cap thickness was the optimal single-factor predictor with AUC 0.818 while optimal combinational-factor predictor achieved an AUC 0.836. Using random forest for predicting MPVI change, minimum cap thickness was the optimal single-factor predictor with AUC 0.785 and the AUC of optimal combinational-factor predictor achieved 0.847. Conclusion This feasibility study demonstrated that machine learning methods could be used to accurately predict plaque vulnerability change based on morphological and biomechanical factors from multi-modality image-based FSI models. Large-scale studies are needed to verify our findings.

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