Top Tweets for #VulnerabilityPrediction
Prof. @mikepapadakis has just concluded his keynote "A Tale of #VulnerabilityPrediction"! ππ€
We are extremely grateful to him for taking part in the #SECUTE workshop and holding such an interesting and insightful talk! ππ»π₯³
Thrilled to meet Pamela Phan, Deputy Assistant Secretary for Asia at U.S. Department of Commerce. Had fruitful conversation about Securin's products and #vulnerabilityresearch and US's mission to secure the cyber landscape.
#cybersecurity #vulnerabilityprediction #ransomware
Excited to have shared Securin's research on #VulnerabilityPrediction and #Ransomware with US Ambassador to Singapore @JonathanKaplan along with our partner @GoIvanti . Committed to defending the homeland and contributing to global security.
#cybersecurity #USmission
Excited to have shared Securin's research on #VulnerabilityPrediction and #Ransomware with US Ambassador to Singapore @JonathanKaplan with @GoIvanti . Committed to defending the homeland and contributing to global security.
#cybersecurity #CyberDefense #StopRansomware
π§΅(5/5) 5β£ Finally, there is a need for better #DataSharing, #Governance & #QualityControl of #vulnerability #datasets, to promote future work, increase the reliability of produced outcomes, & advance this field of research.
#SLR
#VulnerabilityPrediction
#DataPreparation
π§΅(4/5) 4β£ Current #data #solutions aim to address #challenges in data #accessability, #effort, & #noise, but they are imperfect solutions.
Many potentially important #data #quality attributes are yet unconsidered by researchers, such as data #understandability & #timeliness.
π§΅(3/5) 3β£ #Label #correctness is a significant issue for many #vulnerability #datasets due to incomplete reporting & #localization issues stemming from the original data sources.
It is also highly error-prone.
#SLR
#VulnerabilityPrediction
#DataQuality
#DataPreparation
π§΅(2/5) 2β£ Existing #vulnerability #datasets are assumed to lack #generalizability. Synthetic datasets are not considered to be representative of real-world code, & most #datasets are limited to a small set of application contexts.
#SLR
#VulnerabilityPrediction
#DataQuality
π§΅(2/5) 2β£ Existing #vulnerability #datasets are assumed to lack #generalizability. Synthetic datasets are not considered to be representative of real-world code, & most #datasets are limited to a small set of application contexts.
#SLR
#VulnerabilityPrediction
#DataQuality
π§΅of key contributions of our recently accepted #TSE paperπ
(1/5) 1β£ Presents a comprehensive source of information regarding SVP data preparation #Practices #Challenges & #Solutions, including a #Taxonomy of SV data challenges -> useful to guide future SVP #Research & Practice
Check out our recently accepted paper at #MSR2022 that investigates #NoisyLabel learning methods applied to #Software #VulnerabilityPrediction π #CREST_Research @CSCRCoz
Using #MachineLearning 4 #software #security & troubled by noisy #data? @crest team provides an #innovative solution to #SVP using #Noisy Label Learning; accepted in @msrconf, https://t.co/d6zdIRelLA, funded by @CSCRCoz & led by @CroftRoland involving @Huaming_Chen & me; #GameOn
Are you interested in learning how #DeepLearning could potentially improve the accuracy of #VulnerabilityPrediction models in cross-project vulnerability prediction? Have a look at our latest paper!
Pre-print: π https://t.co/a6lk9X77cI
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