F. Martinelli, F. Mercaldo, Vittoria Nardone, A. Santone
Android operating system is constantly overwhelmed by new sophisticated threats and new zero-day attacks. While aggressive malware, for instance malicious behaviors able to cipher data files or lock the GUI, are not worried to circumvention users by infection (that can try to disinfect the device), there exist malware with the aim to perform malicious actions stealthy, i.e., trying to not manifest their presence to the users. This kind of malware is less recognizable, because users are not aware of their presence. In this paper we propose FormalDroid, a tool able to detect silent malicious beaviours and to localize the malicious payload in Android application. Evaluating real-world malware samples we obtain an accuracy equal to 0.94.
{"title":"How Discover a Malware using Model Checking","authors":"F. Martinelli, F. Mercaldo, Vittoria Nardone, A. Santone","doi":"10.1145/3052973.3055157","DOIUrl":"https://doi.org/10.1145/3052973.3055157","url":null,"abstract":"Android operating system is constantly overwhelmed by new sophisticated threats and new zero-day attacks. While aggressive malware, for instance malicious behaviors able to cipher data files or lock the GUI, are not worried to circumvention users by infection (that can try to disinfect the device), there exist malware with the aim to perform malicious actions stealthy, i.e., trying to not manifest their presence to the users. This kind of malware is less recognizable, because users are not aware of their presence. In this paper we propose FormalDroid, a tool able to detect silent malicious beaviours and to localize the malicious payload in Android application. Evaluating real-world malware samples we obtain an accuracy equal to 0.94.","PeriodicalId":20540,"journal":{"name":"Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72626396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Let us consider a scenario that a data holder (e.g., a hospital) encrypts a data (e.g., a medical record) which relates a keyword (e.g., a disease name), and sends its ciphertext to a server. We here suppose not only the data but also the keyword should be kept private. A receiver sends a query to the server (e.g., average of body weights of cancer patients). Then, the server performs the homomorphic operation to the ciphertexts of the corresponding medical records, and returns the resultant ciphertext. In this scenario, the server should NOT be allowed to perform the homomorphic operation against ciphertexts associated with different keywords. If such a mis-operation happens, then medical records of different diseases are unexpectedly mixed. However, in the conventional homomorphic encryption, there is no way to prevent such an unexpected homomorphic operation, and this fact may become visible after decrypting a ciphertext, or as the most serious case it might be never detected. To circumvent this problem, in this paper, we propose mis-operation resistant homomorphic encryption, where even if one performs the homomorphic operations against ciphertexts associated with keywords ω' and ω, where ω -ω', the evaluation algorithm detects this fact. Moreover, even if one (intentionally or accidentally) performs the homomorphic operations against such ciphertexts, a ciphertext associated with a random keyword is generated, and the decryption algorithm rejects it. So, the receiver can recognize such a mis-operation happens in the evaluation phase. In addition to mis-operation resistance, we additionally adopt secure search functionality for keywords since it is desirable when one would like to delegate homomorphic operations to a third party. So, we call the proposed primitive mis-operation resistant searchable homomorphic encryption (MR-SHE). We also give our implementation result of inner products of encrypted vectors. In the case when both vectors are encrypted, the running time of the receiver is millisecond order for relatively small-dimensional (e.g., 26) vectors. In the case when one vector is encrypted, the running time of the receiver is approximately 5 msec even for relatively high-dimensional (e.g., 213) vectors.
{"title":"Mis-operation Resistant Searchable Homomorphic Encryption","authors":"K. Emura, Takuya Hayashi, N. Kunihiro, Jun Sakuma","doi":"10.1145/3052973.3053015","DOIUrl":"https://doi.org/10.1145/3052973.3053015","url":null,"abstract":"Let us consider a scenario that a data holder (e.g., a hospital) encrypts a data (e.g., a medical record) which relates a keyword (e.g., a disease name), and sends its ciphertext to a server. We here suppose not only the data but also the keyword should be kept private. A receiver sends a query to the server (e.g., average of body weights of cancer patients). Then, the server performs the homomorphic operation to the ciphertexts of the corresponding medical records, and returns the resultant ciphertext. In this scenario, the server should NOT be allowed to perform the homomorphic operation against ciphertexts associated with different keywords. If such a mis-operation happens, then medical records of different diseases are unexpectedly mixed. However, in the conventional homomorphic encryption, there is no way to prevent such an unexpected homomorphic operation, and this fact may become visible after decrypting a ciphertext, or as the most serious case it might be never detected. To circumvent this problem, in this paper, we propose mis-operation resistant homomorphic encryption, where even if one performs the homomorphic operations against ciphertexts associated with keywords ω' and ω, where ω -ω', the evaluation algorithm detects this fact. Moreover, even if one (intentionally or accidentally) performs the homomorphic operations against such ciphertexts, a ciphertext associated with a random keyword is generated, and the decryption algorithm rejects it. So, the receiver can recognize such a mis-operation happens in the evaluation phase. In addition to mis-operation resistance, we additionally adopt secure search functionality for keywords since it is desirable when one would like to delegate homomorphic operations to a third party. So, we call the proposed primitive mis-operation resistant searchable homomorphic encryption (MR-SHE). We also give our implementation result of inner products of encrypted vectors. In the case when both vectors are encrypted, the running time of the receiver is millisecond order for relatively small-dimensional (e.g., 26) vectors. In the case when one vector is encrypted, the running time of the receiver is approximately 5 msec even for relatively high-dimensional (e.g., 213) vectors.","PeriodicalId":20540,"journal":{"name":"Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79645274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Taehun Kim, Hyeonmin Ha, Seoyoon Choi, Jaeyeon Jung, Byung-Gon Chun
To protect customers' sensitive information, many mobile financial applications include steps to probe the runtime environment and abort their execution if the environment is deemed to have been tampered with. This paper investigates the security of such self-defense mechanisms used in 76 popular financial Android apps in the Republic of Korea. Our investigation found that existing tools fail to analyze these Android apps effectively because of their highly obfuscated code and complex, non-traditional control flows. We overcome this challenge by extracting a call graph with a self-defense mechanism, from a detailed runtime trace record of a target app's execution. To generate the call graph, we identify the causality between the system APIs (Android APIs and system calls) used to check device rooting and app integrity, and those used to stop an app's execution. Our analysis of 76 apps shows that we can pinpoint methods to bypass a self-defense mechanism using a causality graph in most cases. We successfully bypassed self-defense mechanisms in 67 out of 73 apps that check device rooting and 39 out of 44 apps that check app integrity. While analyzing the self-defense mechanisms, we found that many apps rely on third-party security libraries for their self-defense mechanisms. Thus we present in-depth studies of the top five security libraries. Our results demonstrate the necessity of a platform-level solution for integrity checks.
{"title":"Breaking Ad-hoc Runtime Integrity Protection Mechanisms in Android Financial Apps","authors":"Taehun Kim, Hyeonmin Ha, Seoyoon Choi, Jaeyeon Jung, Byung-Gon Chun","doi":"10.1145/3052973.3053018","DOIUrl":"https://doi.org/10.1145/3052973.3053018","url":null,"abstract":"To protect customers' sensitive information, many mobile financial applications include steps to probe the runtime environment and abort their execution if the environment is deemed to have been tampered with. This paper investigates the security of such self-defense mechanisms used in 76 popular financial Android apps in the Republic of Korea. Our investigation found that existing tools fail to analyze these Android apps effectively because of their highly obfuscated code and complex, non-traditional control flows. We overcome this challenge by extracting a call graph with a self-defense mechanism, from a detailed runtime trace record of a target app's execution. To generate the call graph, we identify the causality between the system APIs (Android APIs and system calls) used to check device rooting and app integrity, and those used to stop an app's execution. Our analysis of 76 apps shows that we can pinpoint methods to bypass a self-defense mechanism using a causality graph in most cases. We successfully bypassed self-defense mechanisms in 67 out of 73 apps that check device rooting and 39 out of 44 apps that check app integrity. While analyzing the self-defense mechanisms, we found that many apps rely on third-party security libraries for their self-defense mechanisms. Thus we present in-depth studies of the top five security libraries. Our results demonstrate the necessity of a platform-level solution for integrity checks.","PeriodicalId":20540,"journal":{"name":"Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90938894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ruowen Wang, Ahmed M. Azab, W. Enck, Ninghui Li, P. Ning, Xun Chen, Wenbo Shen, Yueqiang Cheng
SEAndroid is a mandatory access control (MAC) framework that can confine faulty applications on Android. Nevertheless, the effectiveness of SEAndroid enforcement depends on the employed policy. The growing complexity of Android makes it difficult for policy engineers to have complete domain knowledge on every system functionality. As a result, policy engineers sometimes craft over-permissive and ineffective policy rules, which unfortunately increased the attack surface of the Android system and have allowed multiple real-world privilege escalation attacks. We propose SPOKE, an SEAndroid Policy Knowledge Engine, that systematically extracts domain knowledge from rich-semantic functional tests and further uses the knowledge for characterizing the attack surface of SEAndroid policy rules. Our attack surface analysis is achieved by two steps: 1) It reveals policy rules that cannot be justified by the collected domain knowledge. 2) It identifies potentially over-permissive access patterns allowed by those unjustified rules as the attack surface. We evaluate SPOKE using 665 functional tests targeting 28 different categories of functionalities developed by Samsung Android Team. SPOKE successfully collected 12,491 access patterns for the 28 categories as domain knowledge, and used the knowledge to reveal 320 unjustified policy rules and 210 over-permissive access patterns defined by those rules, including one related to the notorious libstagefright vulnerability. These findings have been confirmed by policy engineers.
{"title":"SPOKE: Scalable Knowledge Collection and Attack Surface Analysis of Access Control Policy for Security Enhanced Android","authors":"Ruowen Wang, Ahmed M. Azab, W. Enck, Ninghui Li, P. Ning, Xun Chen, Wenbo Shen, Yueqiang Cheng","doi":"10.1145/3052973.3052991","DOIUrl":"https://doi.org/10.1145/3052973.3052991","url":null,"abstract":"SEAndroid is a mandatory access control (MAC) framework that can confine faulty applications on Android. Nevertheless, the effectiveness of SEAndroid enforcement depends on the employed policy. The growing complexity of Android makes it difficult for policy engineers to have complete domain knowledge on every system functionality. As a result, policy engineers sometimes craft over-permissive and ineffective policy rules, which unfortunately increased the attack surface of the Android system and have allowed multiple real-world privilege escalation attacks. We propose SPOKE, an SEAndroid Policy Knowledge Engine, that systematically extracts domain knowledge from rich-semantic functional tests and further uses the knowledge for characterizing the attack surface of SEAndroid policy rules. Our attack surface analysis is achieved by two steps: 1) It reveals policy rules that cannot be justified by the collected domain knowledge. 2) It identifies potentially over-permissive access patterns allowed by those unjustified rules as the attack surface. We evaluate SPOKE using 665 functional tests targeting 28 different categories of functionalities developed by Samsung Android Team. SPOKE successfully collected 12,491 access patterns for the 28 categories as domain knowledge, and used the knowledge to reveal 320 unjustified policy rules and 210 over-permissive access patterns defined by those rules, including one related to the notorious libstagefright vulnerability. These findings have been confirmed by policy engineers.","PeriodicalId":20540,"journal":{"name":"Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89838821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mobile apps need optimal performance and responsiveness to rise amongst numerous rivals on the market. Further, some apps like media streaming or gaming apps cannot even function properly with a performance below a certain threshold. In this work, we present the first performance degradation attack on Android OS that can target rival apps using a combination of logical channel leakages and low-level architectural bottlenecks in the underlying hardware. To show the viability of the attack, we design a proof-of-concept app and test it on various mobile platforms. The attack runs covertly and brings the target to the level of unresponsiveness. With less than 10% CPU time in the worst case, it requires minimal computational effort to run as a background service, and requires only the UsageStats permission from the user. We quantify the impact of our attack using 11 popular benchmark apps, running 44 different tests.} The measured QoS degradation varies across platforms and applications, reaching a maximum of 90% in some cases. The attack combines the leakage from logical channels with low-level architectural bottlenecks to design a malicious app that can covertly degrade Quality of Service (QoS) of any targeted app. Furthermore, our attack code has a small footprint and is not detected by the Android system as malicious. Finally, our app can pass the Google Play Store malware scanner, Google Bouncer, as well as the top malware scanners in the Play Store.
{"title":"Hit by the Bus: QoS Degradation Attack on Android","authors":"Mehmet Sinan Inci, T. Eisenbarth, B. Sunar","doi":"10.1145/3052973.3053028","DOIUrl":"https://doi.org/10.1145/3052973.3053028","url":null,"abstract":"Mobile apps need optimal performance and responsiveness to rise amongst numerous rivals on the market. Further, some apps like media streaming or gaming apps cannot even function properly with a performance below a certain threshold. In this work, we present the first performance degradation attack on Android OS that can target rival apps using a combination of logical channel leakages and low-level architectural bottlenecks in the underlying hardware. To show the viability of the attack, we design a proof-of-concept app and test it on various mobile platforms. The attack runs covertly and brings the target to the level of unresponsiveness. With less than 10% CPU time in the worst case, it requires minimal computational effort to run as a background service, and requires only the UsageStats permission from the user. We quantify the impact of our attack using 11 popular benchmark apps, running 44 different tests.} The measured QoS degradation varies across platforms and applications, reaching a maximum of 90% in some cases. The attack combines the leakage from logical channels with low-level architectural bottlenecks to design a malicious app that can covertly degrade Quality of Service (QoS) of any targeted app. Furthermore, our attack code has a small footprint and is not detected by the Android system as malicious. Finally, our app can pass the Google Play Store malware scanner, Google Bouncer, as well as the top malware scanners in the Play Store.","PeriodicalId":20540,"journal":{"name":"Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87652500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Barhamgi, Mu Yang, Chia-Mu Yu, Y. Yu, A. Bandara, D. Benslimane, B. Nuseibeh
In this paper we present our ongoing work to build an approach to empower users of IoT-based cyber physical systems to protect their privacy by themselves. Our approach allows users to identify the privacy risks involved in sharing private data with a data consumer, assess the value of their private data based on identified risks and take a pragmatic data sharing decision balancing the risks with the benefits generated by the sharing. Our approach features a knowledgebase, called the Privacy Oracle, that exploits the power of the Semantic Web to determine how raw metadata can be combined by data consumers to infer privacy-sensitive information as well as the privacy risks associated with the disclosure of inferred information.
{"title":"Enabling End-Users to Protect their Privacy","authors":"M. Barhamgi, Mu Yang, Chia-Mu Yu, Y. Yu, A. Bandara, D. Benslimane, B. Nuseibeh","doi":"10.1145/3052973.3055154","DOIUrl":"https://doi.org/10.1145/3052973.3055154","url":null,"abstract":"In this paper we present our ongoing work to build an approach to empower users of IoT-based cyber physical systems to protect their privacy by themselves. Our approach allows users to identify the privacy risks involved in sharing private data with a data consumer, assess the value of their private data based on identified risks and take a pragmatic data sharing decision balancing the risks with the benefits generated by the sharing. Our approach features a knowledgebase, called the Privacy Oracle, that exploits the power of the Semantic Web to determine how raw metadata can be combined by data consumers to infer privacy-sensitive information as well as the privacy risks associated with the disclosure of inferred information.","PeriodicalId":20540,"journal":{"name":"Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85489033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We address the problem of ciphertext-policy attribute-based encryption with fine access control, a cryptographic primitive which has many concrete application scenarios such as Pay-TV, e-Health, Cloud Storage and so on. In this context we improve on previous LSSS based techniques by building on previous work of Hohenberger and Waters at PKC'13 and proposing a construction that achieves ciphertext size linear in the minimum between the size of the boolean access formula and the number of its clauses. Our construction also supports fast decryption. We also propose two interesting extensions: the first one aims at reducing storage and computation at the user side and is useful in the context of lightweight devices or devices using a cloud operator. The second proposes the use of multiple authorities to mitigate key escrow by the authority.
{"title":"A Ciphertext-Policy Attribute-based Encryption Scheme With Optimized Ciphertext Size And Fast Decryption","authors":"Q. Malluhi, Abdullatif Shikfa, V. Trinh","doi":"10.1145/3052973.3052987","DOIUrl":"https://doi.org/10.1145/3052973.3052987","url":null,"abstract":"We address the problem of ciphertext-policy attribute-based encryption with fine access control, a cryptographic primitive which has many concrete application scenarios such as Pay-TV, e-Health, Cloud Storage and so on. In this context we improve on previous LSSS based techniques by building on previous work of Hohenberger and Waters at PKC'13 and proposing a construction that achieves ciphertext size linear in the minimum between the size of the boolean access formula and the number of its clauses. Our construction also supports fast decryption. We also propose two interesting extensions: the first one aims at reducing storage and computation at the user side and is useful in the context of lightweight devices or devices using a cloud operator. The second proposes the use of multiple authorities to mitigate key escrow by the authority.","PeriodicalId":20540,"journal":{"name":"Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82383525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Simon Eberz, Kasper Bonne Rasmussen, Vincent Lenders, I. Martinovic
In recent years, behavioral biometrics have become a popular approach to support continuous authentication systems. Most generally, a continuous authentication system can make two types of errors: false rejects and false accepts. Based on this, the most commonly reported metrics to evaluate systems are the False Reject Rate (FRR) and False Accept Rate (FAR). However, most papers only report the mean of these measures with little attention paid to their distribution. This is problematic as systematic errors allow attackers to perpetually escape detection while random errors are less severe. Using 16 biometric datasets we show that these systematic errors are very common in the wild. We show that some biometrics (such as eye movements) are particularly prone to systematic errors, while others (such as touchscreen inputs) show more even error distributions. Our results also show that the inclusion of some distinctive features lowers average error rates but significantly increases the prevalence of systematic errors. As such, blind optimization of the mean EER (through feature engineering or selection) can sometimes lead to lower security. Following this result we propose the Gini Coefficient (GC) as an additional metric to accurately capture different error distributions. We demonstrate the usefulness of this measure both to compare different systems and to guide researchers during feature selection. In addition to the selection of features and classifiers, some non- functional machine learning methodologies also affect error rates. The most notable examples of this are the selection of training data and the attacker model used to develop the negative class. 13 out of the 25 papers we analyzed either include imposter data in the negative class or randomly sample training data from the entire dataset, with a further 6 not giving any information on the methodology used. Using real-world data we show that both of these decisions lead to significant underestimation of error rates by 63% and 81%, respectively. This is an alarming result, as it suggests that researchers are either unaware of the magnitude of these effects or might even be purposefully attempting to over-optimize their EER without actually improving the system.
{"title":"Evaluating Behavioral Biometrics for Continuous Authentication: Challenges and Metrics","authors":"Simon Eberz, Kasper Bonne Rasmussen, Vincent Lenders, I. Martinovic","doi":"10.1145/3052973.3053032","DOIUrl":"https://doi.org/10.1145/3052973.3053032","url":null,"abstract":"In recent years, behavioral biometrics have become a popular approach to support continuous authentication systems. Most generally, a continuous authentication system can make two types of errors: false rejects and false accepts. Based on this, the most commonly reported metrics to evaluate systems are the False Reject Rate (FRR) and False Accept Rate (FAR). However, most papers only report the mean of these measures with little attention paid to their distribution. This is problematic as systematic errors allow attackers to perpetually escape detection while random errors are less severe. Using 16 biometric datasets we show that these systematic errors are very common in the wild. We show that some biometrics (such as eye movements) are particularly prone to systematic errors, while others (such as touchscreen inputs) show more even error distributions. Our results also show that the inclusion of some distinctive features lowers average error rates but significantly increases the prevalence of systematic errors. As such, blind optimization of the mean EER (through feature engineering or selection) can sometimes lead to lower security. Following this result we propose the Gini Coefficient (GC) as an additional metric to accurately capture different error distributions. We demonstrate the usefulness of this measure both to compare different systems and to guide researchers during feature selection. In addition to the selection of features and classifiers, some non- functional machine learning methodologies also affect error rates. The most notable examples of this are the selection of training data and the attacker model used to develop the negative class. 13 out of the 25 papers we analyzed either include imposter data in the negative class or randomly sample training data from the entire dataset, with a further 6 not giving any information on the methodology used. Using real-world data we show that both of these decisions lead to significant underestimation of error rates by 63% and 81%, respectively. This is an alarming result, as it suggests that researchers are either unaware of the magnitude of these effects or might even be purposefully attempting to over-optimize their EER without actually improving the system.","PeriodicalId":20540,"journal":{"name":"Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74201004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christian Wressnegger, Kevin Freeman, Fabian Yamaguchi, Konrad Rieck
Although anti-virus software has significantly evolved over the last decade, classic signature matching based on byte patterns is still a prevalent concept for identifying security threats. Anti-virus signatures are a simple and fast detection mechanism that can complement more sophisticated analysis strategies. However, if signatures are not designed with care, they can turn from a defensive mechanism into an instrument of attack. In this paper, we present a novel method for automatically deriving signatures from anti-virus software and discuss how the extracted signatures can be used to attack sensible data with the aid of the virus scanner itself. To this end, we study the practicability of our approach using four commercial products and exemplary demonstrate anti-virus assisted attacks in three different scenarios.
{"title":"Automatically Inferring Malware Signatures for Anti-Virus Assisted Attacks","authors":"Christian Wressnegger, Kevin Freeman, Fabian Yamaguchi, Konrad Rieck","doi":"10.1145/3052973.3053002","DOIUrl":"https://doi.org/10.1145/3052973.3053002","url":null,"abstract":"Although anti-virus software has significantly evolved over the last decade, classic signature matching based on byte patterns is still a prevalent concept for identifying security threats. Anti-virus signatures are a simple and fast detection mechanism that can complement more sophisticated analysis strategies. However, if signatures are not designed with care, they can turn from a defensive mechanism into an instrument of attack. In this paper, we present a novel method for automatically deriving signatures from anti-virus software and discuss how the extracted signatures can be used to attack sensible data with the aid of the virus scanner itself. To this end, we study the practicability of our approach using four commercial products and exemplary demonstrate anti-virus assisted attacks in three different scenarios.","PeriodicalId":20540,"journal":{"name":"Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88097344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Session details: Vulnerability Analysis","authors":"Manuel Egele","doi":"10.1145/3248554","DOIUrl":"https://doi.org/10.1145/3248554","url":null,"abstract":"","PeriodicalId":20540,"journal":{"name":"Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89648662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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