Yong Zeng, Jiale Liu, Tong Dong, Qingqi Pei, Jianfeng Ma, Yao Liu
Facial recognition technology has been developed and widely used for decades. However, it has also made privacy concerns and researchers’ expectations for facial recognition privacy-preserving technologies. To provide privacy, detailed or semantic contents in face images should be obfuscated. However, face recognition algorithms have to be tailor-designed according to current obfuscation methods, as a result the face recognition service provider has to update its commercial off-the-shelf(COTS) products for each obfuscation method. Meanwhile, current obfuscation methods have no clearly quantified explanation. This paper presents a universal face obfuscation method for a family of face recognition algorithms using global or local structure of eigenvector space. By specific mathematical explanations, we show that the upper bound of the distance between the original and obfuscated face images is smaller than the given recognition threshold. Experiments show that the recognition degradation is 0% for global structure based and 0.3%-5.3% for local structure based, respectively. Meanwhile, we show that even if an attacker knows the whole obfuscation method, he/she has to enumerate all the possible roots of a polynomial with an obfuscation coefficient, which is computationally infeasible to reconstruct original faces. So our method shows a good performance in both privacy and recognition accuracy without modifying recognition algorithms.
{"title":"Eyes See Hazy while Algorithms Recognize Who You Are","authors":"Yong Zeng, Jiale Liu, Tong Dong, Qingqi Pei, Jianfeng Ma, Yao Liu","doi":"10.1145/3632292","DOIUrl":"https://doi.org/10.1145/3632292","url":null,"abstract":"Facial recognition technology has been developed and widely used for decades. However, it has also made privacy concerns and researchers’ expectations for facial recognition privacy-preserving technologies. To provide privacy, detailed or semantic contents in face images should be obfuscated. However, face recognition algorithms have to be tailor-designed according to current obfuscation methods, as a result the face recognition service provider has to update its commercial off-the-shelf(COTS) products for each obfuscation method. Meanwhile, current obfuscation methods have no clearly quantified explanation. This paper presents a universal face obfuscation method for a family of face recognition algorithms using global or local structure of eigenvector space. By specific mathematical explanations, we show that the upper bound of the distance between the original and obfuscated face images is smaller than the given recognition threshold. Experiments show that the recognition degradation is 0% for global structure based and 0.3%-5.3% for local structure based, respectively. Meanwhile, we show that even if an attacker knows the whole obfuscation method, he/she has to enumerate all the possible roots of a polynomial with an obfuscation coefficient, which is computationally infeasible to reconstruct original faces. So our method shows a good performance in both privacy and recognition accuracy without modifying recognition algorithms.","PeriodicalId":56050,"journal":{"name":"ACM Transactions on Privacy and Security","volume":"116 37","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135137594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Han Cao, Qindong Sun, Yaqi Li, Rong Geng, Xiaoxiong Wang
The existence of adversarial image makes us have to doubt the credibility of artificial intelligence system. Attackers can use carefully processed adversarial images to carry out a variety of attacks. Inspired by the theory of image compressed sensing, this paper proposes a new black-box attack, (mathcal {N}text{-HSA}_{LF} ) . It uses covariance matrix adaptive evolution strategy (CMA-ES) to learn the distribution of adversarial perturbation in low frequency domain, reducing the dimensionality of solution space. And sep-CMA-ES is used to set the covariance matrix as a diagonal matrix, which further reduces the dimensions that need to be updated for the covariance matrix of multivariate Gaussian distribution learned in attacks, thereby reducing the computational cost of attack. And on this basis, we propose history-driven mean update and current optimal solution-guided improvement strategies to avoid the evolution of distribution to a worse direction. The experimental results show that the proposed (mathcal {N}text{-HSA}_{LF} ) can achieve a higher attack success rate with fewer queries on attacking both CNN-based and transformer-based target models under L 2 -norm and L ∞ -norm constraints of perturbation. We also conduct an ablation study and the results show that the proposed improved strategies can effectively reduce the number of visits to the target model when making adversarial examples for hard examples. In addition, our attack is able to make the integrated defense strategy of GRIP-GAN and noise-embedded training ineffective to a certain extent.
{"title":"Efficient History-Driven Adversarial Perturbation Distribution Learning in Low Frequency Domain","authors":"Han Cao, Qindong Sun, Yaqi Li, Rong Geng, Xiaoxiong Wang","doi":"10.1145/3632293","DOIUrl":"https://doi.org/10.1145/3632293","url":null,"abstract":"The existence of adversarial image makes us have to doubt the credibility of artificial intelligence system. Attackers can use carefully processed adversarial images to carry out a variety of attacks. Inspired by the theory of image compressed sensing, this paper proposes a new black-box attack, (mathcal {N}text{-HSA}_{LF} ) . It uses covariance matrix adaptive evolution strategy (CMA-ES) to learn the distribution of adversarial perturbation in low frequency domain, reducing the dimensionality of solution space. And sep-CMA-ES is used to set the covariance matrix as a diagonal matrix, which further reduces the dimensions that need to be updated for the covariance matrix of multivariate Gaussian distribution learned in attacks, thereby reducing the computational cost of attack. And on this basis, we propose history-driven mean update and current optimal solution-guided improvement strategies to avoid the evolution of distribution to a worse direction. The experimental results show that the proposed (mathcal {N}text{-HSA}_{LF} ) can achieve a higher attack success rate with fewer queries on attacking both CNN-based and transformer-based target models under L 2 -norm and L ∞ -norm constraints of perturbation. We also conduct an ablation study and the results show that the proposed improved strategies can effectively reduce the number of visits to the target model when making adversarial examples for hard examples. In addition, our attack is able to make the integrated defense strategy of GRIP-GAN and noise-embedded training ineffective to a certain extent.","PeriodicalId":56050,"journal":{"name":"ACM Transactions on Privacy and Security","volume":"110 s425","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135342518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Logging is a key mechanism in the security of computer systems. Beyond supporting important forward security properties, it is critical that logging withstands both failures and intentional tampering to prevent subtle attacks leaving the system in an inconsistent state with inconclusive evidence. We propose new techniques combining forward security with crash recovery for secure log data storage. As the support of specifically forward integrity and the online nature of logging prevent the use of conventional coding, we propose and analyze a coding scheme resolving these unique design constraints. Specifically, our coding enables forward integrity, online encoding, and most importantly a constant number of operations per encoding. It adds a new log item by (mathsf {XOR} ) ing it to k cells of a table. If up to a certain threshold of cells is modified by the adversary, or lost due to a crash, we still guarantee recovery of all stored log items. The main advantage of the coding scheme is its efficiency and compatibility with forward integrity. The key contribution of the paper is the use of spectral graph theory techniques to prove that k is constant in the number n of all log items ever stored and small in practice, e.g., k = 5. Moreover, we prove that to cope with up to (sqrt {n} ) modified or lost log items, storage expansion is constant in n and small in practice. For k = 5, the size of the table is only (12% ) more than the simple concatenation of all n items. We propose and evaluate original techniques to scale the computation cost of recovery to several GBytes of security logs. We instantiate our scheme into an abstract data structure which allows to either detect adversarial modifications to log items or treat modifications like data loss in a system crash. The data structure can recover lost log items, thereby effectively reverting adversarial modifications.
{"title":"Forward Security with Crash Recovery for Secure Logs","authors":"Erik-Oliver Blass, Guevara Noubir","doi":"10.1145/3631524","DOIUrl":"https://doi.org/10.1145/3631524","url":null,"abstract":"Logging is a key mechanism in the security of computer systems. Beyond supporting important forward security properties, it is critical that logging withstands both failures and intentional tampering to prevent subtle attacks leaving the system in an inconsistent state with inconclusive evidence. We propose new techniques combining forward security with crash recovery for secure log data storage. As the support of specifically forward integrity and the online nature of logging prevent the use of conventional coding, we propose and analyze a coding scheme resolving these unique design constraints. Specifically, our coding enables forward integrity, online encoding, and most importantly a constant number of operations per encoding. It adds a new log item by (mathsf {XOR} ) ing it to k cells of a table. If up to a certain threshold of cells is modified by the adversary, or lost due to a crash, we still guarantee recovery of all stored log items. The main advantage of the coding scheme is its efficiency and compatibility with forward integrity. The key contribution of the paper is the use of spectral graph theory techniques to prove that k is constant in the number n of all log items ever stored and small in practice, e.g., k = 5. Moreover, we prove that to cope with up to (sqrt {n} ) modified or lost log items, storage expansion is constant in n and small in practice. For k = 5, the size of the table is only (12% ) more than the simple concatenation of all n items. We propose and evaluate original techniques to scale the computation cost of recovery to several GBytes of security logs. We instantiate our scheme into an abstract data structure which allows to either detect adversarial modifications to log items or treat modifications like data loss in a system crash. The data structure can recover lost log items, thereby effectively reverting adversarial modifications.","PeriodicalId":56050,"journal":{"name":"ACM Transactions on Privacy and Security","volume":"39 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135818730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Li Tang, Qingqing Ye, Haibo Hu, Qiao Xue, Yaxin Xiao, Jin Li
With the rapid growth of DeepFake video techniques, it becomes increasingly challenging to identify them visually, posing a huge threat to our society. Unfortunately, existing detection schemes are limited to exploiting the artifacts left by DeepFake manipulations, so they struggle to keep pace with the ever-improving DeepFake models. In this work, we propose DeepMark, a scalable and robust framework for detecting DeepFakes. It imprints essential visual features of a video into DeepMark Meta (DMM), and uses it to detect DeepFake manipulations by comparing the extracted visual features with the ground truth in DMM. Therefore, DeepMark is future-proof because a DeepFake video must aim to alter some visual feature, no matter how “natural” it looks. Furthermore, DMM also contains a signature for verifying the integrity of the above features. And an essential link to the features as well as their signature is attached with error correction codes and embedded in the video watermark. To improve the efficiency of DMM creation, we also present a threshold-based feature selection scheme and a deduced face detection scheme. Experimental results demonstrate the effectiveness and efficiency of DeepMark on DeepFake video detection under various datasets and parameter settings.
随着DeepFake视频技术的快速发展，在视觉上识别它们变得越来越困难，对我们的社会构成了巨大的威胁。不幸的是，现有的检测方案仅限于利用DeepFake操纵留下的工件，因此它们很难跟上不断改进的DeepFake模型的步伐。在这项工作中，我们提出了DeepMark，一个可扩展和鲁棒的框架，用于检测DeepFakes。它将视频的基本视觉特征刻印到DeepMark Meta (DMM)中，并通过将提取的视觉特征与DMM中的ground truth进行比较来检测DeepFake的操作。因此，DeepMark是面向未来的，因为DeepFake视频必须旨在改变一些视觉特征，无论它看起来多么“自然”。此外，DMM还包含一个签名，用于验证上述特性的完整性。在特征及其签名的关键环节上附加纠错码并嵌入到视频水印中。为了提高DMM的创建效率，我们还提出了一种基于阈值的特征选择方案和一种推导的人脸检测方案。实验结果证明了DeepMark在不同数据集和参数设置下对DeepFake视频检测的有效性和高效性。
{"title":"DeepMark: A Scalable and Robust Framework for DeepFake Video Detection","authors":"Li Tang, Qingqing Ye, Haibo Hu, Qiao Xue, Yaxin Xiao, Jin Li","doi":"10.1145/3629976","DOIUrl":"https://doi.org/10.1145/3629976","url":null,"abstract":"With the rapid growth of DeepFake video techniques, it becomes increasingly challenging to identify them visually, posing a huge threat to our society. Unfortunately, existing detection schemes are limited to exploiting the artifacts left by DeepFake manipulations, so they struggle to keep pace with the ever-improving DeepFake models. In this work, we propose DeepMark, a scalable and robust framework for detecting DeepFakes. It imprints essential visual features of a video into DeepMark Meta (DMM), and uses it to detect DeepFake manipulations by comparing the extracted visual features with the ground truth in DMM. Therefore, DeepMark is future-proof because a DeepFake video must aim to alter some visual feature, no matter how “natural” it looks. Furthermore, DMM also contains a signature for verifying the integrity of the above features. And an essential link to the features as well as their signature is attached with error correction codes and embedded in the video watermark. To improve the efficiency of DMM creation, we also present a threshold-based feature selection scheme and a deduced face detection scheme. Experimental results demonstrate the effectiveness and efficiency of DeepMark on DeepFake video detection under various datasets and parameter settings.","PeriodicalId":56050,"journal":{"name":"ACM Transactions on Privacy and Security","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135372097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
JavaScript is often rated as the most popular programming language for the development of both client-side and server-side applications. Because of its popularity, JavaScript has become a frequent target for attackers who exploit vulnerabilities in the source code to take control over the application. To address these JavaScript security issues, such vulnerabilities must be identified first. Existing studies in vulnerable code detection in JavaScript mostly consider package-level vulnerability tracking and measurements. However, such package-level analysis is largely imprecise as real-world services that include a vulnerable package may not use the vulnerable functions in the package. Moreover, even the inclusion of a vulnerable function may not lead to a security problem, if the function cannot be triggered with exploitable inputs. In this paper, we develop a vulnerability detection framework that uses vulnerable pattern recognition and textual similarity methods to detect vulnerable functions in real-world JavaScript projects, combined with a static multi-file taint analysis mechanism to further assess the impact of the vulnerabilities on the whole project (i.e., whether the vulnerability can be exploited in a given project). We compose a comprehensive dataset of 1,360 verified vulnerable JavaScript functions using the Snyk vulnerability database and the VulnCode-DB project. From this ground-truth dataset, we build our vulnerable patterns for two common vulnerability types: prototype pollution and Regular Expression Denial of Service (ReDoS). With our framework, we analyze 9,205,654 functions (from 3,000 NPM packages, 1892 websites and 557 Chrome Web extensions), and detect 117,601 prototype pollution and 7,333 ReDoS vulnerabilities. By further processing all 5,839 findings from NPM packages with our taint analyzer, we verify the exploitability of 290 zero-day cases across 134 NPM packages. In addition, we conduct an in-depth contextual analysis of the findings in 17 popular/critical projects and study the practical security exposure of 20 functions. With our semi-automated vulnerability reporting functionality, we disclosed all verified findings to project owners. We also obtained 25 published CVEs for our findings, 19 of them rated as “Critical” severity, and six rated as “High” severity. Additionally, we obtained 169 CVEs that are currently “Reserved” (as of Apr. 2023). As evident from the results, our approach can shift JavaScript vulnerability detection from the coarse package/library level to the function level, and thus improve the accuracy of detection and aid timely patching.
{"title":"On Detecting and Measuring Exploitable JavaScript Functions in Real-World Applications","authors":"Maryna Kluban, Mohammad Mannan, Amr Youssef","doi":"10.1145/3630253","DOIUrl":"https://doi.org/10.1145/3630253","url":null,"abstract":"JavaScript is often rated as the most popular programming language for the development of both client-side and server-side applications. Because of its popularity, JavaScript has become a frequent target for attackers who exploit vulnerabilities in the source code to take control over the application. To address these JavaScript security issues, such vulnerabilities must be identified first. Existing studies in vulnerable code detection in JavaScript mostly consider package-level vulnerability tracking and measurements. However, such package-level analysis is largely imprecise as real-world services that include a vulnerable package may not use the vulnerable functions in the package. Moreover, even the inclusion of a vulnerable function may not lead to a security problem, if the function cannot be triggered with exploitable inputs. In this paper, we develop a vulnerability detection framework that uses vulnerable pattern recognition and textual similarity methods to detect vulnerable functions in real-world JavaScript projects, combined with a static multi-file taint analysis mechanism to further assess the impact of the vulnerabilities on the whole project (i.e., whether the vulnerability can be exploited in a given project). We compose a comprehensive dataset of 1,360 verified vulnerable JavaScript functions using the Snyk vulnerability database and the VulnCode-DB project. From this ground-truth dataset, we build our vulnerable patterns for two common vulnerability types: prototype pollution and Regular Expression Denial of Service (ReDoS). With our framework, we analyze 9,205,654 functions (from 3,000 NPM packages, 1892 websites and 557 Chrome Web extensions), and detect 117,601 prototype pollution and 7,333 ReDoS vulnerabilities. By further processing all 5,839 findings from NPM packages with our taint analyzer, we verify the exploitability of 290 zero-day cases across 134 NPM packages. In addition, we conduct an in-depth contextual analysis of the findings in 17 popular/critical projects and study the practical security exposure of 20 functions. With our semi-automated vulnerability reporting functionality, we disclosed all verified findings to project owners. We also obtained 25 published CVEs for our findings, 19 of them rated as “Critical” severity, and six rated as “High” severity. Additionally, we obtained 169 CVEs that are currently “Reserved” (as of Apr. 2023). As evident from the results, our approach can shift JavaScript vulnerability detection from the coarse package/library level to the function level, and thus improve the accuracy of detection and aid timely patching.","PeriodicalId":56050,"journal":{"name":"ACM Transactions on Privacy and Security","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134906837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Memory forensics is concerned with the acquisition and analysis of copies of volatile memory (memory dumps). Based on an empirical assessment of observable inconsistencies in 360 memory dumps of a running Linux system, we confirm a state of overwhelming inconsistency in memory forensics: Almost a third of these dumps had an empty process list and was therefore obviously incomplete. Out of those dumps that were analyzable, almost every second dump showed some form of inconsistency that potentially impacts the interpretation of the dump in a forensic investigation. These results are based on a new way to estimate the level of causal consistency of a memory dump. The factors influencing these inconsistencies are less clear but in general correlate with the level of concurrency (system load and number of threads).
{"title":"An Experimental Assessment of Inconsistencies in Memory Forensics","authors":"Jenny Ottmann, Frank Breitinger, Felix Freiling","doi":"10.1145/3628600","DOIUrl":"https://doi.org/10.1145/3628600","url":null,"abstract":"Memory forensics is concerned with the acquisition and analysis of copies of volatile memory (memory dumps). Based on an empirical assessment of observable inconsistencies in 360 memory dumps of a running Linux system, we confirm a state of overwhelming inconsistency in memory forensics: Almost a third of these dumps had an empty process list and was therefore obviously incomplete. Out of those dumps that were analyzable, almost every second dump showed some form of inconsistency that potentially impacts the interpretation of the dump in a forensic investigation. These results are based on a new way to estimate the level of causal consistency of a memory dump. The factors influencing these inconsistencies are less clear but in general correlate with the level of concurrency (system load and number of threads).","PeriodicalId":56050,"journal":{"name":"ACM Transactions on Privacy and Security","volume":"24 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135567344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shen Wang, Mahshid Delavar, Muhammad Ajmal Azad, Farshad Nabizadeh, Steve Smith, Feng Hao
Caller ID spoofing is a global industry problem and often acts as a critical enabler for telephone fraud. To address this problem, the Federal Communications Commission (FCC) has mandated telecom providers in the US to implement STIR/SHAKEN, an industry-driven solution based on digital signatures. STIR/SHAKEN relies on a public key infrastructure (PKI) to manage digital certificates, but scaling up this PKI for the global telecom industry is extremely difficult, if not impossible. Furthermore, it only works with IP-based systems (e.g., SIP), leaving the traditional non-IP systems (e.g., SS7) unprotected. So far the alternatives to the STIR/SHAKEN have not been sufficiently studied. In this paper, we propose a PKI-free solution, called Caller ID Verification (CIV). CIV authenticates the caller ID based on a challenge-response process instead of digital signatures, hence requiring no PKI. It supports both IP and non-IP systems. Perhaps counter-intuitively, we show that number spoofing can be leveraged, in conjunction with Dual-Tone Multi-Frequency (DTMF), to efficiently implement the challenge-response process, i.e., using spoofing to fight against spoofing. We implement CIV for VoIP, cellular, and landline phones across heterogeneous networks (SS7/SIP) by only updating the software on the user’s phone. This is the first caller ID authentication solution with working prototypes for all three types of telephone systems in the current telecom architecture. Finally, we show how the implementation of CIV can be optimized by integrating it into telecom clouds as a service, which users may subscribe to.
{"title":"Spoofing Against Spoofing: Towards Caller ID Verification In Heterogeneous Telecommunication Systems","authors":"Shen Wang, Mahshid Delavar, Muhammad Ajmal Azad, Farshad Nabizadeh, Steve Smith, Feng Hao","doi":"10.1145/3625546","DOIUrl":"https://doi.org/10.1145/3625546","url":null,"abstract":"Caller ID spoofing is a global industry problem and often acts as a critical enabler for telephone fraud. To address this problem, the Federal Communications Commission (FCC) has mandated telecom providers in the US to implement STIR/SHAKEN, an industry-driven solution based on digital signatures. STIR/SHAKEN relies on a public key infrastructure (PKI) to manage digital certificates, but scaling up this PKI for the global telecom industry is extremely difficult, if not impossible. Furthermore, it only works with IP-based systems (e.g., SIP), leaving the traditional non-IP systems (e.g., SS7) unprotected. So far the alternatives to the STIR/SHAKEN have not been sufficiently studied. In this paper, we propose a PKI-free solution, called Caller ID Verification (CIV). CIV authenticates the caller ID based on a challenge-response process instead of digital signatures, hence requiring no PKI. It supports both IP and non-IP systems. Perhaps counter-intuitively, we show that number spoofing can be leveraged, in conjunction with Dual-Tone Multi-Frequency (DTMF), to efficiently implement the challenge-response process, i.e., using spoofing to fight against spoofing. We implement CIV for VoIP, cellular, and landline phones across heterogeneous networks (SS7/SIP) by only updating the software on the user’s phone. This is the first caller ID authentication solution with working prototypes for all three types of telephone systems in the current telecom architecture. Finally, we show how the implementation of CIV can be optimized by integrating it into telecom clouds as a service, which users may subscribe to.","PeriodicalId":56050,"journal":{"name":"ACM Transactions on Privacy and Security","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135537407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
System auditing is an essential tool for detecting malicious events and conducting forensic analysis. Although used extensively on general-purpose systems, auditing frameworks have not been designed with consideration for the unique constraints and properties of Real-Time Systems (RTS). System auditing could provide tremendous benefits for security-critical RTS. However, a naïve deployment of auditing on RTS could violate the temporal requirements of the system while also rendering auditing incomplete and ineffectual. To ensure effective auditing that meets the computational needs of recording complete audit information while adhering to the temporal requirements of the RTS, it is essential to carefully integrate auditing into the real-time (RT) schedule. This work adapts the Linux Audit framework for use in RT Linux by leveraging the common properties of such systems, such as special purpose and predictability. Ellipsis , an efficient system for auditing RTS is devised that learns the expected benign behaviors of the system and generates succinct descriptions of the expected activity. Evaluations using varied RT applications show that Ellipsis reduces the volume of audit records generated during benign activity by up to 97.55%, while recording detailed logs for suspicious activities. Empirical analyses establish that the auditing infrastructure adheres to the properties of predictability and isolation that are important to RTS. Furthermore, the schedulability of RT task sets under audit is comprehensively analyzed to enable the safe integration of auditing in RT task schedules.
{"title":"System Auditing for Real-Time Systems","authors":"Ayoosh Bansal, Anant Kandikuppa, Monowar Hasan, Chien-Ying Chen, Adam Bates, Sibin Mohan","doi":"10.1145/3625229","DOIUrl":"https://doi.org/10.1145/3625229","url":null,"abstract":"System auditing is an essential tool for detecting malicious events and conducting forensic analysis. Although used extensively on general-purpose systems, auditing frameworks have not been designed with consideration for the unique constraints and properties of Real-Time Systems (RTS). System auditing could provide tremendous benefits for security-critical RTS. However, a naïve deployment of auditing on RTS could violate the temporal requirements of the system while also rendering auditing incomplete and ineffectual. To ensure effective auditing that meets the computational needs of recording complete audit information while adhering to the temporal requirements of the RTS, it is essential to carefully integrate auditing into the real-time (RT) schedule. This work adapts the Linux Audit framework for use in RT Linux by leveraging the common properties of such systems, such as special purpose and predictability. Ellipsis , an efficient system for auditing RTS is devised that learns the expected benign behaviors of the system and generates succinct descriptions of the expected activity. Evaluations using varied RT applications show that Ellipsis reduces the volume of audit records generated during benign activity by up to 97.55%, while recording detailed logs for suspicious activities. Empirical analyses establish that the auditing infrastructure adheres to the properties of predictability and isolation that are important to RTS. Furthermore, the schedulability of RT task sets under audit is comprehensively analyzed to enable the safe integration of auditing in RT task schedules.","PeriodicalId":56050,"journal":{"name":"ACM Transactions on Privacy and Security","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136061810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dohyun Kim, Mangi Cho, Hocheol Shin, Jaehoon Kim, Juhwan Noh, Yongdae Kim
Photoelectric sensors are utilized in a range of safety-critical applications, such as medical devices and autonomous vehicles. However, the public exposure of the input channel of a photoelectric sensor makes it vulnerable to malicious inputs. Several studies have suggested possible attacks on photoelectric sensors by injecting malicious signals. While a few defense techniques have been proposed against such attacks, they could be either bypassed or used for limited purposes. In this study, we propose Lightbox, a novel defense system to detect sensor attacks on photoelectric sensors based on signal fingerprinting. Lightbox uses the spectrum of the received light as a feature to distinguish the attacker’s malicious signals from the authentic signal, which is a signal from the sensor’s light source. We evaluated Lightbox against 1) a saturation attacker, 2) a simple spoofing attacker, and 3) a sophisticated attacker who is aware of Lightbox and can combine multiple light sources to mimic the authentic light source. Lightbox achieved the overall accuracy over 99% for the saturation attacker and simple spoofing attacker, and robustness against a sophisticated attacker. We also evaluated Lightbox considering various environments such as transmission medium, background noise, and input waveform. Finally, we demonstrate the practicality of Lightbox with experiments using a single-board computer after further reducing the training time.
{"title":"Lightbox: Sensor Attack Detection for Photoelectric Sensors via Spectrum Fingerprinting","authors":"Dohyun Kim, Mangi Cho, Hocheol Shin, Jaehoon Kim, Juhwan Noh, Yongdae Kim","doi":"10.1145/3615867","DOIUrl":"https://doi.org/10.1145/3615867","url":null,"abstract":"Photoelectric sensors are utilized in a range of safety-critical applications, such as medical devices and autonomous vehicles. However, the public exposure of the input channel of a photoelectric sensor makes it vulnerable to malicious inputs. Several studies have suggested possible attacks on photoelectric sensors by injecting malicious signals. While a few defense techniques have been proposed against such attacks, they could be either bypassed or used for limited purposes. In this study, we propose Lightbox, a novel defense system to detect sensor attacks on photoelectric sensors based on signal fingerprinting. Lightbox uses the spectrum of the received light as a feature to distinguish the attacker’s malicious signals from the authentic signal, which is a signal from the sensor’s light source. We evaluated Lightbox against 1) a saturation attacker, 2) a simple spoofing attacker, and 3) a sophisticated attacker who is aware of Lightbox and can combine multiple light sources to mimic the authentic light source. Lightbox achieved the overall accuracy over 99% for the saturation attacker and simple spoofing attacker, and robustness against a sophisticated attacker. We also evaluated Lightbox considering various environments such as transmission medium, background noise, and input waveform. Finally, we demonstrate the practicality of Lightbox with experiments using a single-board computer after further reducing the training time.","PeriodicalId":56050,"journal":{"name":"ACM Transactions on Privacy and Security","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46395049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tommaso Paladini, Francesco Monti, Mario Polino, Michele Carminati, S. Zanero
Machine learning (ML) models are vulnerable to adversarial machine learning (AML) attacks. Unlike other contexts, the fraud detection domain is characterized by inherent challenges that make conventional approaches hardly applicable. In this paper, we extend the application of AML techniques to the fraud detection task by studying poisoning attacks and their possible countermeasures. First, we present a novel approach for performing poisoning attacks that overcomes the fraud detection domain-specific constraints. It generates fraudulent candidate transactions and tests them against a machine learning-based Oracle, which simulates the target fraud detection system aiming at evading it. Misclassified fraudulent candidate transactions are then integrated into the target detection system’s training set, poisoning its model and shifting its decision boundary. Second, we propose a novel approach that extends the adversarial training technique to mitigate AML attacks: during the training phase of the detection system, we generate artificial frauds by modifying random original legitimate transactions; then, we include them in the training set with the correct label. By doing so, we instruct our model to recognize evasive transactions before an attack occurs. Using two real bank datasets, we evaluate the security of several state-of-the-art fraud detection systems by deploying our poisoning attack with different degrees of attacker’s knowledge and attacking strategies. The experimental results show that our attack works even when the attacker has minimal knowledge of the target system. Then, we demonstrate that the proposed countermeasure can mitigate adversarial attacks by reducing the stolen amount of money up to 100%.
{"title":"Fraud Detection Under Siege: Practical Poisoning Attacks and Defense Strategies","authors":"Tommaso Paladini, Francesco Monti, Mario Polino, Michele Carminati, S. Zanero","doi":"10.1145/3613244","DOIUrl":"https://doi.org/10.1145/3613244","url":null,"abstract":"Machine learning (ML) models are vulnerable to adversarial machine learning (AML) attacks. Unlike other contexts, the fraud detection domain is characterized by inherent challenges that make conventional approaches hardly applicable. In this paper, we extend the application of AML techniques to the fraud detection task by studying poisoning attacks and their possible countermeasures. First, we present a novel approach for performing poisoning attacks that overcomes the fraud detection domain-specific constraints. It generates fraudulent candidate transactions and tests them against a machine learning-based Oracle, which simulates the target fraud detection system aiming at evading it. Misclassified fraudulent candidate transactions are then integrated into the target detection system’s training set, poisoning its model and shifting its decision boundary. Second, we propose a novel approach that extends the adversarial training technique to mitigate AML attacks: during the training phase of the detection system, we generate artificial frauds by modifying random original legitimate transactions; then, we include them in the training set with the correct label. By doing so, we instruct our model to recognize evasive transactions before an attack occurs. Using two real bank datasets, we evaluate the security of several state-of-the-art fraud detection systems by deploying our poisoning attack with different degrees of attacker’s knowledge and attacking strategies. The experimental results show that our attack works even when the attacker has minimal knowledge of the target system. Then, we demonstrate that the proposed countermeasure can mitigate adversarial attacks by reducing the stolen amount of money up to 100%.","PeriodicalId":56050,"journal":{"name":"ACM Transactions on Privacy and Security","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48675684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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