title: AI-enabled automated common vulnerability scoring from common vulnerabilities and exposures descriptions

authors: Zhang, Zijing

subjects: computer science

abstract: With the sheer amount of vulnerabilities, manually evaluating the impact of them is challenging. This paper proposes employing artificial intelligence models as substitutes for humans or as aides to human experts in estimating vulnerabilities. We compare the precision, recall, and F1 score amongst the Universal Sentence Encoder, Generative Pre-trained Transformer, and Support Vector Machine, trained on 118,000 vulnerabilities and tested on 51,000 vulnerabilities, with human experts on mean estimation error and variance for each type of vulnerability from the state of the art work in estimating vulnerability severity scores. The Universal Sentence Encoder demonstrates superior performance with results (72/77 % accuracy on severity level prediction) that significantly outperform human experts in assessment tasks for various types of vulnerabilities with high efficiency for memory consumption and low running time. Additionally, we examine the efficacy of our models in predicting the components of vulnerability evaluation metrics and the severity level of vulnerabilities. The findings highlight the potential of artificial intelligence agents to assist cybersecurity experts in this task which in the current state of the art is entirely manual.

text: AI-enabled automated common vulnerability scoring from common vulnerabilities and exposures descriptions With the sheer amount of vulnerabilities, manually evaluating the impact of them is challenging. This paper proposes employing artificial intelligence models as substitutes for humans or as aides to human experts in estimating vulnerabilities. We compare the precision, recall, and F1 score amongst the Universal Sentence Encoder, Generative Pre-trained Transformer, and Support Vector Machine, trained on 118,000 vulnerabilities and tested on 51,000 vulnerabilities, with human experts on mean estimation error and variance for each type of vulnerability from the state of the art work in estimating vulnerability severity scores. The Universal Sentence Encoder demonstrates superior performance with results (72/77 % accuracy on severity level prediction) that significantly outperform human experts in assessment tasks for various types of vulnerabilities with high efficiency for memory consumption and low running time. Additionally, we examine the efficacy of our models in predicting the components of vulnerability evaluation metrics and the severity level of vulnerabilities. The findings highlight the potential of artificial intelligence agents to assist cybersecurity experts in this task which in the current state of the art is entirely manual.

year: 2025

title: Dimension-adaptive bounds on compressive FLD Classification

authors: Kabán, Ata

subjects: compressed learning

abstract: Efficient dimensionality reduction by random projections (RP) gains popularity, hence the learning guarantees achievable in RP spaces are of great interest. In finite dimensional setting, it has been shown for the compressive Fisher Linear Discriminant (FLD) classifier that forgood generalisation the required target dimension grows only as the log of the number of classes and is not adversely affected by the number of projected data points. However these bounds depend on the dimensionality d of the original data space. In this paper we give further guarantees that remove d from the bounds under certain conditions of regularity on the data density structure. In particular, if the data density does not fill the ambient space then the error of compressive FLD is independent of the ambient dimension and depends only on a notion of ‘intrinsic dimension'.

text: Dimension-adaptive bounds on compressive FLD Classification Efficient dimensionality reduction by random projections (RP) gains popularity, hence the learning guarantees achievable in RP spaces are of great interest. In finite dimensional setting, it has been shown for the compressive Fisher Linear Discriminant (FLD) classifier that forgood generalisation the required target dimension grows only as the log of the number of classes and is not adversely affected by the number of projected data points. However these bounds depend on the dimensionality d of the original data space. In this paper we give further guarantees that remove d from the bounds under certain conditions of regularity on the data density structure. In particular, if the data density does not fill the ambient space then the error of compressive FLD is independent of the ambient dimension and depends only on a notion of ‘intrinsic dimension'.

year: 2013

title: Te rima tekau ngahurutanga o Te Whare Wānanga o Waikato

authors: Dixon, Ngahuia

subjects: Tohu mātauranga

abstract: E te Whare Wānanga o Waikato – te tohu o te mātauranga, tēnā koe.  Tēnā koe i roto i tō rima tekau tau ngahurutanga.  Kua pakeke haere koe.  Kua tau atu koe ki tērā kaumātuatanga nō reira me mihi ka tika.  He hōnore nui tēnei mōu ake i te mea nāu te moana o mātauranga i roto o Waikato i tuku kia rere, kia pari, ka rere anō.  He wai marino ēnei i ētehi wā, he rehutai i ētehi atu,  he wā anō ka karekare, ka haruru rānei i te pai o te rere o āu mahi.  Ahakoa ēnei huringa o ngā awa o te wā, kei te rere tonu te moana nei, me ōna pikonga ki uru, ki rāwhiti, ki tonga, ki raki rānei, arā ki ngā pito katoa o te ao.  He mātauranga ēnei i tukuna e koe kia pīoioi i te ao.  Hei aha - hei whakawhānui ki te mātatoru o te tangata, he aha kei ō whare kōrero, hei whakakīkī haere i ā rātou kete mātauranga, ahakoa Māori, tauiwi huri noa.  Koinei ngā korowai mō ngā manu i kai i te mātauranga nāu i rumaki.   Ināianei arā te kōrero:

Nō rātou, nō mātou, nō tātou te ao 

E kore e mimiti ēnei huarahi tautoko āu, - nā mēnā ka tūwhera tonu tō ngākau ki ngā kete kōrero, mātauranga āku ake a ngāi-Māori, kei hea mai tō mana.

text: Te rima tekau ngahurutanga o Te Whare Wānanga o Waikato E te Whare Wānanga o Waikato – te tohu o te mātauranga, tēnā koe.  Tēnā koe i roto i tō rima tekau tau ngahurutanga.  Kua pakeke haere koe.  Kua tau atu koe ki tērā kaumātuatanga nō reira me mihi ka tika.  He hōnore nui tēnei mōu ake i te mea nāu te moana o mātauranga i roto o Waikato i tuku kia rere, kia pari, ka rere anō.  He wai marino ēnei i ētehi wā, he rehutai i ētehi atu,  he wā anō ka karekare, ka haruru rānei i te pai o te rere o āu mahi.  Ahakoa ēnei huringa o ngā awa o te wā, kei te rere tonu te moana nei, me ōna pikonga ki uru, ki rāwhiti, ki tonga, ki raki rānei, arā ki ngā pito katoa o te ao.  He mātauranga ēnei i tukuna e koe kia pīoioi i te ao.  Hei aha - hei whakawhānui ki te mātatoru o te tangata, he aha kei ō whare kōrero, hei whakakīkī haere i ā rātou kete mātauranga, ahakoa Māori, tauiwi huri noa.  Koinei ngā korowai mō ngā manu i kai i te mātauranga nāu i rumaki.   Ināianei arā te kōrero:

Nō rātou, nō mātou, nō tātou te ao 

E kore e mimiti ēnei huarahi tautoko āu, - nā mēnā ka tūwhera tonu tō ngākau ki ngā kete kōrero, mātauranga āku ake a ngāi-Māori, kei hea mai tō mana.

year: 2016