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
针对恶意软件利用环境感知能力来逃避分析系统检测的现状,深入研究基于系统应用程序接口(Application Program Interface,API)的环境感知技术,并实现全面检测环境感知API的自动化工具EAFinder(Environment-Aware API Finder)。EAFinder...针对恶意软件利用环境感知能力来逃避分析系统检测的现状,深入研究基于系统应用程序接口(Application Program Interface,API)的环境感知技术,并实现全面检测环境感知API的自动化工具EAFinder(Environment-Aware API Finder)。EAFinder能够枚举所有的系统API,并在真机和模拟器中进行自动化调用,最终通过比较API在不同环境中的可访问性和返回值的差异,检测出环境感知API。实验结果显示EAFinder在Android 9至13上共检测出344个API,排除误报后得到323个可用于环境感知的API。将其按使用方式分为独立使用、基于阈值使用和组合使用三类,并抽样测试了各类API的有效性,结果显示利用这些API能以97%的准确率区分真实设备和模拟器。展开更多
Commercial organisations commonly use operational research tools to solve vehicle routing problems. This practice is less commonplace in charity and voluntary organisations. In this paper, we provide an elementary app...Commercial organisations commonly use operational research tools to solve vehicle routing problems. This practice is less commonplace in charity and voluntary organisations. In this paper, we provide an elementary approach for solving the Vehicle Routing Problem (VRP) that we believe can be easily implemented in these types of organisations. The proposed model leverages mixed integer linear programming to optimize the pickup sequence of all customers, each with distinct time windows and locations, transporting them to a final destination using a fleet of vehicles. To ensure ease of implementation, the model utilises Python, a user-friendly programming language, and integrates with the Google Maps API, which simplifies data input by eliminating the need for manual entry of travel times between locations. Troubleshooting methods are incorporated into the model design to ensure easy debugging of the model’s infeasibilities. Additionally, a computation time analysis is conducted to evaluate the efficiency of the code. A node partitioning approach is also discussed, which aims to reduce computational times, especially when handling larger datasets, ensuring this model is realistic and practical for real-world application. By implementing this optimized routing strategy, logistics companies or organisations can expect significant improvements in their day-to-day operations, with minimal computational cost or need for specialised expertise. This includes reduced travel times, minimized fuel consumption, and thus lower operational costs, while ensuring punctuality and meeting the demands of all passengers.展开更多
应用程序接口(Application Programming Interfaces,API)在现代软件开发中起着重要的作用,开发人员经常需要为他们的编程任务搜索合适的API。但是随着信息产业的发展,API参考文档变得越发庞大,传统的搜索方式会因为互联网上的冗余和错...应用程序接口(Application Programming Interfaces,API)在现代软件开发中起着重要的作用,开发人员经常需要为他们的编程任务搜索合适的API。但是随着信息产业的发展,API参考文档变得越发庞大,传统的搜索方式会因为互联网上的冗余和错误信息给工程师的查询带来不便。与此同时,由于编程任务的自然语言描述与API文档中的描述之间存在词汇和知识上的差距,很难找到合适的API。基于这些问题,提出一种融合领域知识的API推荐算法ARDSQ(Recommendation base on Documentation and Solved Question)。ARDSQ能够根据工程师对某个功能的自然语言描述去知识库里检索到最为贴近的API。实验表明,与两种先进的API推荐算法(BIKER,DeepAPILearning)比较,ARDSQ在推荐系统关键评价指数(Hit-n,MRR,MAP)上都有较大的优势。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.
文摘针对恶意软件利用环境感知能力来逃避分析系统检测的现状,深入研究基于系统应用程序接口(Application Program Interface,API)的环境感知技术,并实现全面检测环境感知API的自动化工具EAFinder(Environment-Aware API Finder)。EAFinder能够枚举所有的系统API,并在真机和模拟器中进行自动化调用,最终通过比较API在不同环境中的可访问性和返回值的差异,检测出环境感知API。实验结果显示EAFinder在Android 9至13上共检测出344个API,排除误报后得到323个可用于环境感知的API。将其按使用方式分为独立使用、基于阈值使用和组合使用三类,并抽样测试了各类API的有效性,结果显示利用这些API能以97%的准确率区分真实设备和模拟器。
文摘Commercial organisations commonly use operational research tools to solve vehicle routing problems. This practice is less commonplace in charity and voluntary organisations. In this paper, we provide an elementary approach for solving the Vehicle Routing Problem (VRP) that we believe can be easily implemented in these types of organisations. The proposed model leverages mixed integer linear programming to optimize the pickup sequence of all customers, each with distinct time windows and locations, transporting them to a final destination using a fleet of vehicles. To ensure ease of implementation, the model utilises Python, a user-friendly programming language, and integrates with the Google Maps API, which simplifies data input by eliminating the need for manual entry of travel times between locations. Troubleshooting methods are incorporated into the model design to ensure easy debugging of the model’s infeasibilities. Additionally, a computation time analysis is conducted to evaluate the efficiency of the code. A node partitioning approach is also discussed, which aims to reduce computational times, especially when handling larger datasets, ensuring this model is realistic and practical for real-world application. By implementing this optimized routing strategy, logistics companies or organisations can expect significant improvements in their day-to-day operations, with minimal computational cost or need for specialised expertise. This includes reduced travel times, minimized fuel consumption, and thus lower operational costs, while ensuring punctuality and meeting the demands of all passengers.
文摘应用程序接口(Application Programming Interfaces,API)在现代软件开发中起着重要的作用,开发人员经常需要为他们的编程任务搜索合适的API。但是随着信息产业的发展,API参考文档变得越发庞大,传统的搜索方式会因为互联网上的冗余和错误信息给工程师的查询带来不便。与此同时,由于编程任务的自然语言描述与API文档中的描述之间存在词汇和知识上的差距,很难找到合适的API。基于这些问题,提出一种融合领域知识的API推荐算法ARDSQ(Recommendation base on Documentation and Solved Question)。ARDSQ能够根据工程师对某个功能的自然语言描述去知识库里检索到最为贴近的API。实验表明,与两种先进的API推荐算法(BIKER,DeepAPILearning)比较,ARDSQ在推荐系统关键评价指数(Hit-n,MRR,MAP)上都有较大的优势。
