Pub Date : 2023-11-06DOI: 10.1186/s42400-023-00176-7
JiaShuo Liu, JiongJiong Ren, ShaoZhen Chen
Abstract In CRYPTO 2019, Gohr opens up a new direction for cryptanalysis. He successfully applied deep learning to differential cryptanalysis against the NSA block cipher SPECK32/64, achieving higher accuracy than traditional differential distinguishers. Until now, one of the mainstream research directions is increasing the training sample size and utilizing different neural networks to improve the accuracy of neural distinguishers. This conversion mindset may lead to a huge number of parameters, heavy computing load, and a large number of memory in the distinguishers training process. However, in the practical application of cryptanalysis, the applicability of the attacks method in a resource-constrained environment is very important. Therefore, we focus on the cost optimization and aim to reduce network parameters for differential neural cryptanalysis.In this paper, we propose two cost-optimized neural distinguisher improvement methods from the aspect of data format and network structure, respectively. Firstly, we obtain a partial output difference neural distinguisher using only 4-bits training data format which is constructed with a new advantage bits search algorithm based on two key improvement conditions. In addition, we perform an interpretability analysis of the new neural distinguishers whose results are mainly reflected in the relationship between the neural distinguishers, truncated differential, and advantage bits. Secondly, we replace the traditional convolution with the depthwise separable convolution to reduce the training cost without affecting the accuracy as much as possible. Overall, the number of training parameters can be reduced by less than 50% by using our new network structure for training neural distinguishers. Finally, we apply the network structure to the partial output difference neural distinguishers. The combinatorial approach have led to a further reduction in the number of parameters (approximately 30% of Gohr’s distinguishers for SPECK).
{"title":"A deep learning aided differential distinguisher improvement framework with more lightweight and universality","authors":"JiaShuo Liu, JiongJiong Ren, ShaoZhen Chen","doi":"10.1186/s42400-023-00176-7","DOIUrl":"https://doi.org/10.1186/s42400-023-00176-7","url":null,"abstract":"Abstract In CRYPTO 2019, Gohr opens up a new direction for cryptanalysis. He successfully applied deep learning to differential cryptanalysis against the NSA block cipher SPECK32/64, achieving higher accuracy than traditional differential distinguishers. Until now, one of the mainstream research directions is increasing the training sample size and utilizing different neural networks to improve the accuracy of neural distinguishers. This conversion mindset may lead to a huge number of parameters, heavy computing load, and a large number of memory in the distinguishers training process. However, in the practical application of cryptanalysis, the applicability of the attacks method in a resource-constrained environment is very important. Therefore, we focus on the cost optimization and aim to reduce network parameters for differential neural cryptanalysis.In this paper, we propose two cost-optimized neural distinguisher improvement methods from the aspect of data format and network structure, respectively. Firstly, we obtain a partial output difference neural distinguisher using only 4-bits training data format which is constructed with a new advantage bits search algorithm based on two key improvement conditions. In addition, we perform an interpretability analysis of the new neural distinguishers whose results are mainly reflected in the relationship between the neural distinguishers, truncated differential, and advantage bits. Secondly, we replace the traditional convolution with the depthwise separable convolution to reduce the training cost without affecting the accuracy as much as possible. Overall, the number of training parameters can be reduced by less than 50% by using our new network structure for training neural distinguishers. Finally, we apply the network structure to the partial output difference neural distinguishers. The combinatorial approach have led to a further reduction in the number of parameters (approximately 30% of Gohr’s distinguishers for SPECK).","PeriodicalId":36402,"journal":{"name":"Cybersecurity","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135585283","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}
Pub Date : 2023-11-05DOI: 10.1186/s42400-023-00179-4
Yuyao Ge, Zhongguo Yang, Lizhe Chen, Yiming Wang, Chengyang Li
Abstract Adversarial attack for time-series classification model is widely explored and many attack methods are proposed. But there is not a method of attack based on the data itself. In this paper, we innovatively proposed a black-box sparse attack method based on data location. Our method directly attack the sensitive points in the time-series data according to statistical features extract from the dataset. At first, we have validated the transferability of sensitive points among DNNs with different structures. Secondly, we use the statistical features extract from the dataset and the sensitive rate of each point as the training set to train the predictive model. Then, predicting the sensitive rate of test set by predictive model. Finally, perturbing according to the sensitive rate. The attack is limited by constraining the L0 norm to achieve one-point attack. We conduct experiments on several datasets to validate the effectiveness of this method.
{"title":"Attack based on data: a novel perspective to attack sensitive points directly","authors":"Yuyao Ge, Zhongguo Yang, Lizhe Chen, Yiming Wang, Chengyang Li","doi":"10.1186/s42400-023-00179-4","DOIUrl":"https://doi.org/10.1186/s42400-023-00179-4","url":null,"abstract":"Abstract Adversarial attack for time-series classification model is widely explored and many attack methods are proposed. But there is not a method of attack based on the data itself. In this paper, we innovatively proposed a black-box sparse attack method based on data location. Our method directly attack the sensitive points in the time-series data according to statistical features extract from the dataset. At first, we have validated the transferability of sensitive points among DNNs with different structures. Secondly, we use the statistical features extract from the dataset and the sensitive rate of each point as the training set to train the predictive model. Then, predicting the sensitive rate of test set by predictive model. Finally, perturbing according to the sensitive rate. The attack is limited by constraining the L0 norm to achieve one-point attack. We conduct experiments on several datasets to validate the effectiveness of this method.","PeriodicalId":36402,"journal":{"name":"Cybersecurity","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135724676","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}
Pub Date : 2023-11-04DOI: 10.1186/s42400-023-00173-w
Baolong Jin, Rui Xue
Abstract Unique shortest vector problem (uSVP) plays an important role in lattice based cryptography. Many cryptographic schemes based their security on it. For the cofidence of those applications, it is essential to clarify the complexity of uSVP with different parameters. However, proving the NP-hardness of uSVP appears quite hard. To the state of the art, we are even not able to prove the NP-hardness of uSVP with constant parameters. In this work, we gave a lower bound for the hardness of uSVP with constant parameters, i.e. we proved that uSVP is at least as hard as gap shortest vector problem (GapSVP) with gap of $$O(sqrt{n/log (n)})$$ O(n/log(n)) , which is in $$NP cap coAM$$ NP∩coAM . Unlike previous works, our reduction works for paramters in a bigger range, especially when the constant hidden by the big- O in GapSVP is smaller than 1. Graphical abstract
唯一最短向量问题(uSVP)在基于格的密码学中占有重要地位。许多加密方案的安全性都基于它。为了这些应用的可信度，有必要澄清使用不同参数的uSVP的复杂性。然而，证明uSVP的np硬度似乎相当困难。就目前的技术水平而言，我们甚至无法证明恒定参数下uSVP的np硬度。在这项工作中，我们给出了恒定参数下uSVP的硬度下界，即我们证明了uSVP至少与gap为$$O(sqrt{n/log (n)})$$ O (n / log (n))的gap最短向量问题(GapSVP)一样难，即$$NP cap coAM$$ n P∩c O a M。与以往的工作不同，我们的约简适用于更大范围的参数，特别是当GapSVP中大O隐藏的常数小于1时。图形摘要
{"title":"Improved lower bound for the complexity of unique shortest vector problem","authors":"Baolong Jin, Rui Xue","doi":"10.1186/s42400-023-00173-w","DOIUrl":"https://doi.org/10.1186/s42400-023-00173-w","url":null,"abstract":"Abstract Unique shortest vector problem (uSVP) plays an important role in lattice based cryptography. Many cryptographic schemes based their security on it. For the cofidence of those applications, it is essential to clarify the complexity of uSVP with different parameters. However, proving the NP-hardness of uSVP appears quite hard. To the state of the art, we are even not able to prove the NP-hardness of uSVP with constant parameters. In this work, we gave a lower bound for the hardness of uSVP with constant parameters, i.e. we proved that uSVP is at least as hard as gap shortest vector problem (GapSVP) with gap of $$O(sqrt{n/log (n)})$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msqrt> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>/</mml:mo> <mml:mo>log</mml:mo> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:msqrt> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> , which is in $$NP cap coAM$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>N</mml:mi> <mml:mi>P</mml:mi> <mml:mo>∩</mml:mo> <mml:mi>c</mml:mi> <mml:mi>o</mml:mi> <mml:mi>A</mml:mi> <mml:mi>M</mml:mi> </mml:mrow> </mml:math> . Unlike previous works, our reduction works for paramters in a bigger range, especially when the constant hidden by the big- O in GapSVP is smaller than 1. Graphical abstract","PeriodicalId":36402,"journal":{"name":"Cybersecurity","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135773609","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}
Pub Date : 2023-11-03DOI: 10.1186/s42400-023-00163-y
Ziad Hussein, May A. Salama, Sahar A. El-Rahman
Abstract Blockchain technology has gained widespread adoption in recent years due to its ability to enable secure and transparent record-keeping and data transfer. A critical aspect of blockchain technology is the use of consensus algorithms, which allow distributed nodes in the network to agree on the state of the blockchain. In this review paper, we examine various consensus algorithms that are used in blockchain systems, including proof-of-work, proof-of-stake, and hybrid approaches. We go over the trade-offs and factors to think about when choosing a consensus algorithm, such as energy efficiency, decentralization, and security. We also look at the strengths and weaknesses of each algorithm as well as their potential impact on the scalability and adoption of blockchain technology.
{"title":"Evolution of blockchain consensus algorithms: a review on the latest milestones of blockchain consensus algorithms","authors":"Ziad Hussein, May A. Salama, Sahar A. El-Rahman","doi":"10.1186/s42400-023-00163-y","DOIUrl":"https://doi.org/10.1186/s42400-023-00163-y","url":null,"abstract":"Abstract Blockchain technology has gained widespread adoption in recent years due to its ability to enable secure and transparent record-keeping and data transfer. A critical aspect of blockchain technology is the use of consensus algorithms, which allow distributed nodes in the network to agree on the state of the blockchain. In this review paper, we examine various consensus algorithms that are used in blockchain systems, including proof-of-work, proof-of-stake, and hybrid approaches. We go over the trade-offs and factors to think about when choosing a consensus algorithm, such as energy efficiency, decentralization, and security. We also look at the strengths and weaknesses of each algorithm as well as their potential impact on the scalability and adoption of blockchain technology.","PeriodicalId":36402,"journal":{"name":"Cybersecurity","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135868159","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}
Pub Date : 2023-11-02DOI: 10.1186/s42400-023-00160-1
Peng Liu, Wenzhe Ye, Haiying Duan, Xianxian Li, Shuyi Zhang, Chuanjian Yao, Yongnan Li
Abstract Vulnerability reports are essential for improving software security since they record key information on vulnerabilities. In a report, CWE denotes the weakness of the vulnerability and thus helps quickly understand the cause of the vulnerability. Therefore, CWE assignment is useful for categorizing newly discovered vulnerabilities. In this paper, we propose an automatic CWE assignment method with graph neural networks. First, we prepare a dataset that contains 3394 real world vulnerabilities from Linux, OpenSSL, Wireshark and many other software programs. Then, we extract statements with vulnerability syntax features from these vulnerabilities and use program slicing to slice them according to the categories of syntax features. On top of slices, we represent these slices with graphs that characterize the data dependency and control dependency between statements. Finally, we employ the graph neural networks to learn the hidden information from these graphs and leverage the Siamese network to compute the similarity between vulnerability functions, thereby assigning CWE IDs for these vulnerabilities. The experimental results show that the proposed method is effective compared to existing methods.
{"title":"Graph neural network based approach to automatically assigning common weakness enumeration identifiers for vulnerabilities","authors":"Peng Liu, Wenzhe Ye, Haiying Duan, Xianxian Li, Shuyi Zhang, Chuanjian Yao, Yongnan Li","doi":"10.1186/s42400-023-00160-1","DOIUrl":"https://doi.org/10.1186/s42400-023-00160-1","url":null,"abstract":"Abstract Vulnerability reports are essential for improving software security since they record key information on vulnerabilities. In a report, CWE denotes the weakness of the vulnerability and thus helps quickly understand the cause of the vulnerability. Therefore, CWE assignment is useful for categorizing newly discovered vulnerabilities. In this paper, we propose an automatic CWE assignment method with graph neural networks. First, we prepare a dataset that contains 3394 real world vulnerabilities from Linux, OpenSSL, Wireshark and many other software programs. Then, we extract statements with vulnerability syntax features from these vulnerabilities and use program slicing to slice them according to the categories of syntax features. On top of slices, we represent these slices with graphs that characterize the data dependency and control dependency between statements. Finally, we employ the graph neural networks to learn the hidden information from these graphs and leverage the Siamese network to compute the similarity between vulnerability functions, thereby assigning CWE IDs for these vulnerabilities. The experimental results show that the proposed method is effective compared to existing methods.","PeriodicalId":36402,"journal":{"name":"Cybersecurity","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135876427","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}
Abstract Due to the importance of Critical Infrastructure (CI) in a nation’s economy, they have been lucrative targets for cyber attackers. These critical infrastructures are usually Cyber-Physical Systems such as power grids, water, and sewage treatment facilities, oil and gas pipelines, etc. In recent times, these systems have suffered from cyber attacks numerous times. Researchers have been developing cyber security solutions for CIs to avoid lasting damages. According to standard frameworks, cyber security based on identification, protection, detection, response, and recovery are at the core of these research. Detection of an ongoing attack that escapes standard protection such as firewall, anti-virus, and host/network intrusion detection has gained importance as such attacks eventually affect the physical dynamics of the system. Therefore, anomaly detection in physical dynamics proves an effective means to implement defense-in-depth. PASAD is one example of anomaly detection in the sensor/actuator data, representing such systems’ physical dynamics. We present EPASAD, which improves the detection technique used in PASAD to detect these micro-stealthy attacks, as our experiments show that PASAD’s spherical boundary-based detection fails to detect. Our method EPASAD overcomes this by using Ellipsoid boundaries, thereby tightening the boundaries in various dimensions, whereas a spherical boundary treats all dimensions equally. We validate EPASAD using the dataset produced by the TE-process simulator and the C-town datasets. The results show that EPASAD improves PASAD’s average recall by 5.8% and 9.5% for the two datasets, respectively.
{"title":"EPASAD: ellipsoid decision boundary based Process-Aware Stealthy Attack Detector","authors":"Vikas Maurya, Rachit Agarwal, Saurabh Kumar, Sandeep Shukla","doi":"10.1186/s42400-023-00162-z","DOIUrl":"https://doi.org/10.1186/s42400-023-00162-z","url":null,"abstract":"Abstract Due to the importance of Critical Infrastructure (CI) in a nation’s economy, they have been lucrative targets for cyber attackers. These critical infrastructures are usually Cyber-Physical Systems such as power grids, water, and sewage treatment facilities, oil and gas pipelines, etc. In recent times, these systems have suffered from cyber attacks numerous times. Researchers have been developing cyber security solutions for CIs to avoid lasting damages. According to standard frameworks, cyber security based on identification, protection, detection, response, and recovery are at the core of these research. Detection of an ongoing attack that escapes standard protection such as firewall, anti-virus, and host/network intrusion detection has gained importance as such attacks eventually affect the physical dynamics of the system. Therefore, anomaly detection in physical dynamics proves an effective means to implement defense-in-depth. PASAD is one example of anomaly detection in the sensor/actuator data, representing such systems’ physical dynamics. We present EPASAD, which improves the detection technique used in PASAD to detect these micro-stealthy attacks, as our experiments show that PASAD’s spherical boundary-based detection fails to detect. Our method EPASAD overcomes this by using Ellipsoid boundaries, thereby tightening the boundaries in various dimensions, whereas a spherical boundary treats all dimensions equally. We validate EPASAD using the dataset produced by the TE-process simulator and the C-town datasets. The results show that EPASAD improves PASAD’s average recall by 5.8% and 9.5% for the two datasets, respectively.","PeriodicalId":36402,"journal":{"name":"Cybersecurity","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135216665","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}
Pub Date : 2023-10-08DOI: 10.1186/s42400-023-00188-3
Jianfu Huang, Ye Luo, Qinggan Fu, Yincen Chen, Chao Wang, Ling Song
Abstract Small-state stream ciphers (SSCs), which violate the principle that the state size should exceed the key size by a factor of two, still demonstrate robust security properties while maintaining a lightweight design. These ciphers can be classified into several constructions and their basic security requirement is to resist generic attacks, i.e., the time–memory–data tradeoff (TMDTO) attack. In this paper, we investigate the security of small-state constructions in the multi-user setting. Based on it, the TMDTO distinguishing attack and the TMDTO key recovery attack are developed for such a setting. It is shown that SSCs which continuously use the key can not resist the TMDTO distinguishing attack. Moreover, SSCs based on the continuous-IV-key-use construction cannot withstand the TMDTO key recovery attack when the key length is shorter than the IV length, no matter whether the keystream length is limited or not. Finally, we apply these two generic attacks to TinyJAMBU and DRACO in the multi-user setting. The TMDTO distinguishing attack on TinyJAMBU with a 128-bit key can be mounted with time, memory, and data complexities of $$2^{64}$$ 264 , $$2^{48}$$ 248 , and $$2^{32}$$ 232 , respectively. This attack is comparable with a recent work on ToSC 2022, where partial key bits of TinyJAMBU are recovered with more than $$2^{50}$$ 250 users (or keys). As DRACO’s IV length is smaller than its key length, it is vulnerable to the TMDTO key recovery attack. The resulting attack has a time and memory complexity of both $$2^{112}$$ 2112 , which means DRACO does not provide 128-bit security in the multi-user setting.
{"title":"Generic attacks on small-state stream cipher constructions in the multi-user setting","authors":"Jianfu Huang, Ye Luo, Qinggan Fu, Yincen Chen, Chao Wang, Ling Song","doi":"10.1186/s42400-023-00188-3","DOIUrl":"https://doi.org/10.1186/s42400-023-00188-3","url":null,"abstract":"Abstract Small-state stream ciphers (SSCs), which violate the principle that the state size should exceed the key size by a factor of two, still demonstrate robust security properties while maintaining a lightweight design. These ciphers can be classified into several constructions and their basic security requirement is to resist generic attacks, i.e., the time–memory–data tradeoff (TMDTO) attack. In this paper, we investigate the security of small-state constructions in the multi-user setting. Based on it, the TMDTO distinguishing attack and the TMDTO key recovery attack are developed for such a setting. It is shown that SSCs which continuously use the key can not resist the TMDTO distinguishing attack. Moreover, SSCs based on the continuous-IV-key-use construction cannot withstand the TMDTO key recovery attack when the key length is shorter than the IV length, no matter whether the keystream length is limited or not. Finally, we apply these two generic attacks to TinyJAMBU and DRACO in the multi-user setting. The TMDTO distinguishing attack on TinyJAMBU with a 128-bit key can be mounted with time, memory, and data complexities of $$2^{64}$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>64</mml:mn> </mml:msup> </mml:math> , $$2^{48}$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>48</mml:mn> </mml:msup> </mml:math> , and $$2^{32}$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>32</mml:mn> </mml:msup> </mml:math> , respectively. This attack is comparable with a recent work on ToSC 2022, where partial key bits of TinyJAMBU are recovered with more than $$2^{50}$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>50</mml:mn> </mml:msup> </mml:math> users (or keys). As DRACO’s IV length is smaller than its key length, it is vulnerable to the TMDTO key recovery attack. The resulting attack has a time and memory complexity of both $$2^{112}$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>112</mml:mn> </mml:msup> </mml:math> , which means DRACO does not provide 128-bit security in the multi-user setting.","PeriodicalId":36402,"journal":{"name":"Cybersecurity","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135197954","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}
Abstract Bitcoin is a decentralized P2P cryptocurrency. It supports users to use pseudonyms instead of network addresses to send and receive transactions at the data layer, hiding users’ real network identities. Traditional transaction tracing attack cuts through the network layer to directly associate each transaction with the network address that issued it, thus revealing the sender’s network identity. But this attack can be mitigated by Bitcoin’s network layer privacy protections. Since Bitcoin protects the unlinkability of Bitcoin addresses and there may be a many-to-one relationship between addresses and nodes, transactions sent from the same node via different addresses are seen as coming from different nodes because attackers can only use addresses as node identifiers. In this paper, we proposed the evicting and filling attack to expose the correlations between addresses and cluster transactions sent from different addresses of the same node. The attack exploited the unisolation of Bitcoin’s incoming connection processing mechanism. In particular, an attacker can utilize the shared connection pool and deterministic connection eviction strategy to infer the correlation between incoming and evicting connections, as well as the correlation between releasing and filling connections. Based on inferred results, different addresses of the same node with these connections can be linked together, whether they are of the same or different network types. We designed a multi-step attack procedure, and set reasonable attack parameters through analyzing the factors that affect the attack efficiency and accuracy. We mounted this attack on both our self-run nodes and multi-address nodes in real Bitcoin network, achieving an average accuracy of 96.9% and 82%, respectively. Furthermore, we found that the attack is also applicable to Zcash, Litecoin, Dogecoin, Bitcoin Cash, and Dash. We analyzed the cost of network-wide attacks, the application scenario, and proposed countermeasures of this attack.
{"title":"Evicting and filling attack for linking multiple network addresses of Bitcoin nodes","authors":"Huashuang Yang, Jinqiao Shi, Yue Gao, Xuebin Wang, Yanwei Sun, Ruisheng Shi, Dongbin Wang","doi":"10.1186/s42400-023-00182-9","DOIUrl":"https://doi.org/10.1186/s42400-023-00182-9","url":null,"abstract":"Abstract Bitcoin is a decentralized P2P cryptocurrency. It supports users to use pseudonyms instead of network addresses to send and receive transactions at the data layer, hiding users’ real network identities. Traditional transaction tracing attack cuts through the network layer to directly associate each transaction with the network address that issued it, thus revealing the sender’s network identity. But this attack can be mitigated by Bitcoin’s network layer privacy protections. Since Bitcoin protects the unlinkability of Bitcoin addresses and there may be a many-to-one relationship between addresses and nodes, transactions sent from the same node via different addresses are seen as coming from different nodes because attackers can only use addresses as node identifiers. In this paper, we proposed the evicting and filling attack to expose the correlations between addresses and cluster transactions sent from different addresses of the same node. The attack exploited the unisolation of Bitcoin’s incoming connection processing mechanism. In particular, an attacker can utilize the shared connection pool and deterministic connection eviction strategy to infer the correlation between incoming and evicting connections, as well as the correlation between releasing and filling connections. Based on inferred results, different addresses of the same node with these connections can be linked together, whether they are of the same or different network types. We designed a multi-step attack procedure, and set reasonable attack parameters through analyzing the factors that affect the attack efficiency and accuracy. We mounted this attack on both our self-run nodes and multi-address nodes in real Bitcoin network, achieving an average accuracy of 96.9% and 82%, respectively. Furthermore, we found that the attack is also applicable to Zcash, Litecoin, Dogecoin, Bitcoin Cash, and Dash. We analyzed the cost of network-wide attacks, the application scenario, and proposed countermeasures of this attack.","PeriodicalId":36402,"journal":{"name":"Cybersecurity","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135254981","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}
Pub Date : 2023-10-06DOI: 10.1186/s42400-023-00180-x
Chuyi Yan, Chen Zhang, Meng Shen, Ning Li, Jinhao Liu, Yinhao Qi, Zhigang Lu, Yuling Liu
Abstract Ethereum’s high attention, rich business, certain anonymity, and untraceability have attracted a group of attackers. Cybercrime on it has become increasingly rampant, among which scam behavior is convenient, cryptic, antagonistic and resulting in large economic losses. So we consider the scam behavior on Ethereum and investigate it at the node interaction level. Based on the life cycle and risk identification points we found, we propose an automatic detection model named Aparecium . First, a graph generation method which focus on the scam life cycle is adopted to mitigate the sparsity of the scam behaviors. Second, the life cycle patterns are delicate modeled because of the crypticity and antagonism of Ethereum scam behaviors. Conducting experiments in the wild Ethereum datasets, we prove Aparecium is effective which the precision, recall and F1-score achieve at 0.977, 0.957 and 0.967 respectively.
{"title":"Aparecium: understanding and detecting scam behaviors on Ethereum via biased random walk","authors":"Chuyi Yan, Chen Zhang, Meng Shen, Ning Li, Jinhao Liu, Yinhao Qi, Zhigang Lu, Yuling Liu","doi":"10.1186/s42400-023-00180-x","DOIUrl":"https://doi.org/10.1186/s42400-023-00180-x","url":null,"abstract":"Abstract Ethereum’s high attention, rich business, certain anonymity, and untraceability have attracted a group of attackers. Cybercrime on it has become increasingly rampant, among which scam behavior is convenient, cryptic, antagonistic and resulting in large economic losses. So we consider the scam behavior on Ethereum and investigate it at the node interaction level. Based on the life cycle and risk identification points we found, we propose an automatic detection model named Aparecium . First, a graph generation method which focus on the scam life cycle is adopted to mitigate the sparsity of the scam behaviors. Second, the life cycle patterns are delicate modeled because of the crypticity and antagonism of Ethereum scam behaviors. Conducting experiments in the wild Ethereum datasets, we prove Aparecium is effective which the precision, recall and F1-score achieve at 0.977, 0.957 and 0.967 respectively.","PeriodicalId":36402,"journal":{"name":"Cybersecurity","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135303610","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}
Pub Date : 2023-10-05DOI: 10.1186/s42400-023-00174-9
Mir Nazish, M. Tariq Banday, Insha Syed, Sheena Banday
Abstract It is challenging to devise lightweight cryptographic primitives efficient in both hardware and software that can provide an optimum level of security to diverse Internet of Things applications running on low-end constrained devices. Therefore, an efficient hardware design approach that requires some specific hardware resource may not be efficient if implemented in software. Substitution bit Permutation Network based ciphers such as PRESENT and GIFT are efficient, lightweight cryptographic hardware design approaches. These ciphers introduce confusion and diffusion by employing a 4 × 4 static substitution box and bit permutations. The bit-wise permutation is realised by simple rerouting, which is most cost-effective to implement in hardware, resulting in negligible power consumption. However, this method is highly resource-consuming in software, particularly for large block-sized ciphers, with each single-bit permutation requiring multiple sub-operations. This paper proposes a novel software-based design approach for permutation operation in Substitution bit Permutation Network based ciphers using a bit-banding feature. The conventional permutation using bit rotation and the proposed approach have been implemented, analysed and compared for GIFT and PRESENT ciphers on ARM Cortex-M3-based LPC1768 development platform with KEIL MDK used as an Integrated Development Environment. The real-time performance comparison between conventional and the proposed approaches in terms of memory (RAM/ROM) footprint, power, energy and execution time has been carried out using ULINKpro and ULINKplus debug adapters for various code and speed optimisation scenarios. The proposed approach substantially reduces execution time, energy and power consumption for both PRESENT and GIFT ciphers, thus demonstrating the efficiency of the proposed method for Substitution bit Permutation Network based symmetric block ciphers.
{"title":"An efficient permutation approach for SbPN-based symmetric block ciphers","authors":"Mir Nazish, M. Tariq Banday, Insha Syed, Sheena Banday","doi":"10.1186/s42400-023-00174-9","DOIUrl":"https://doi.org/10.1186/s42400-023-00174-9","url":null,"abstract":"Abstract It is challenging to devise lightweight cryptographic primitives efficient in both hardware and software that can provide an optimum level of security to diverse Internet of Things applications running on low-end constrained devices. Therefore, an efficient hardware design approach that requires some specific hardware resource may not be efficient if implemented in software. Substitution bit Permutation Network based ciphers such as PRESENT and GIFT are efficient, lightweight cryptographic hardware design approaches. These ciphers introduce confusion and diffusion by employing a 4 × 4 static substitution box and bit permutations. The bit-wise permutation is realised by simple rerouting, which is most cost-effective to implement in hardware, resulting in negligible power consumption. However, this method is highly resource-consuming in software, particularly for large block-sized ciphers, with each single-bit permutation requiring multiple sub-operations. This paper proposes a novel software-based design approach for permutation operation in Substitution bit Permutation Network based ciphers using a bit-banding feature. The conventional permutation using bit rotation and the proposed approach have been implemented, analysed and compared for GIFT and PRESENT ciphers on ARM Cortex-M3-based LPC1768 development platform with KEIL MDK used as an Integrated Development Environment. The real-time performance comparison between conventional and the proposed approaches in terms of memory (RAM/ROM) footprint, power, energy and execution time has been carried out using ULINKpro and ULINKplus debug adapters for various code and speed optimisation scenarios. The proposed approach substantially reduces execution time, energy and power consumption for both PRESENT and GIFT ciphers, thus demonstrating the efficiency of the proposed method for Substitution bit Permutation Network based symmetric block ciphers.","PeriodicalId":36402,"journal":{"name":"Cybersecurity","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134947604","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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