在万物互联的云时代,云应用程序编程接口(API)是数字经济建设和服务化软件开发的关键数字基础设施。然而,云API数量的持续增长给用户决策和推广带来挑战,设计有效的推荐方法成为亟待解决的重要问题。现有研究多利用调用偏好、搜索关键...在万物互联的云时代,云应用程序编程接口(API)是数字经济建设和服务化软件开发的关键数字基础设施。然而,云API数量的持续增长给用户决策和推广带来挑战,设计有效的推荐方法成为亟待解决的重要问题。现有研究多利用调用偏好、搜索关键词或二者结合进行建模,主要解决为给定Mashup推荐合适云API的问题,未考虑开发者对个性化高阶互补云API的实际需求。该文提出一种基于个性化张量分解的高阶互补云API推荐方法(Personalized Tensor Decomposition based High-order Complementary cloud API Recommendation,PTDHCR)。首先,将Mashup与云API之间的调用关系,以及云API与云API之间的互补关系建模为三维张量,并利用RECAL张量分解技术对这两种关系进行共同学习,以挖掘云API之间的个性化非对称互补关系。然后,考虑到不同互补关系对推荐结果的影响程度不同,构建个性化高阶互补感知网络,充分利用Mashup、查询云API以及候选云API的多模态特征,动态计算Mashup对不同查询和候选云API之间互补关系的关注程度。在此基础上,将个性化互补关系拓展到高阶,得到候选云API与查询云API集合的整体个性化互补性。最后,利用两个真实云API数据集进行实验,结果表明,相较于传统方法,PTDHCR在挖掘个性化互补关系和推荐方面具有较大的优势。展开更多
Ransomware,particularly crypto-ransomware,remains a significant cybersecurity challenge,encrypting victim data and demanding a ransom,often leaving the data irretrievable even if payment is made.This study proposes an...Ransomware,particularly crypto-ransomware,remains a significant cybersecurity challenge,encrypting victim data and demanding a ransom,often leaving the data irretrievable even if payment is made.This study proposes an early detection approach to mitigate such threats by identifying ransomware activity before the encryption process begins.The approach employs a two-tiered approach:a signature-based method using hashing techniques to match known threats and a dynamic behavior-based analysis leveraging Cuckoo Sandbox and machine learning algorithms.A critical feature is the integration of the most effective Application Programming Interface call monitoring,which analyzes system-level interactions such as file encryption,key generation,and registry modifications.This enables the detection of both known and zero-day ransomware variants,overcoming limitations of traditional methods.The proposed technique was evaluated using classifiers such as Random Forest,Support Vector Machine,and K-Nearest Neighbors,achieving a detection accuracy of 98%based on 26 key ransomware attributes with an 80:20 training-to-testing ratio and 10-fold cross-validation.By combining minimal feature sets with robust behavioral analysis,the proposed method outperforms existing solutions and addresses current challenges in ransomware detection,thereby enhancing cybersecurity resilience.展开更多
文摘在万物互联的云时代,云应用程序编程接口(API)是数字经济建设和服务化软件开发的关键数字基础设施。然而,云API数量的持续增长给用户决策和推广带来挑战,设计有效的推荐方法成为亟待解决的重要问题。现有研究多利用调用偏好、搜索关键词或二者结合进行建模,主要解决为给定Mashup推荐合适云API的问题,未考虑开发者对个性化高阶互补云API的实际需求。该文提出一种基于个性化张量分解的高阶互补云API推荐方法(Personalized Tensor Decomposition based High-order Complementary cloud API Recommendation,PTDHCR)。首先,将Mashup与云API之间的调用关系,以及云API与云API之间的互补关系建模为三维张量,并利用RECAL张量分解技术对这两种关系进行共同学习,以挖掘云API之间的个性化非对称互补关系。然后,考虑到不同互补关系对推荐结果的影响程度不同,构建个性化高阶互补感知网络,充分利用Mashup、查询云API以及候选云API的多模态特征,动态计算Mashup对不同查询和候选云API之间互补关系的关注程度。在此基础上,将个性化互补关系拓展到高阶,得到候选云API与查询云API集合的整体个性化互补性。最后,利用两个真实云API数据集进行实验,结果表明,相较于传统方法,PTDHCR在挖掘个性化互补关系和推荐方面具有较大的优势。
基金funded by the National University of Sciences and Technology(NUST)supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF),funded by the Ministry of Education(2021R1IIA3049788).
文摘Ransomware,particularly crypto-ransomware,remains a significant cybersecurity challenge,encrypting victim data and demanding a ransom,often leaving the data irretrievable even if payment is made.This study proposes an early detection approach to mitigate such threats by identifying ransomware activity before the encryption process begins.The approach employs a two-tiered approach:a signature-based method using hashing techniques to match known threats and a dynamic behavior-based analysis leveraging Cuckoo Sandbox and machine learning algorithms.A critical feature is the integration of the most effective Application Programming Interface call monitoring,which analyzes system-level interactions such as file encryption,key generation,and registry modifications.This enables the detection of both known and zero-day ransomware variants,overcoming limitations of traditional methods.The proposed technique was evaluated using classifiers such as Random Forest,Support Vector Machine,and K-Nearest Neighbors,achieving a detection accuracy of 98%based on 26 key ransomware attributes with an 80:20 training-to-testing ratio and 10-fold cross-validation.By combining minimal feature sets with robust behavioral analysis,the proposed method outperforms existing solutions and addresses current challenges in ransomware detection,thereby enhancing cybersecurity resilience.
