Pub Date : 2026-01-15DOI: 10.1109/tifs.2026.3654865
Xiuwen Liu, Yanjiao Chen, Shanchen Pang
{"title":"Decision Boundary-aware Counterfactual Learning against Model Extraction Attacks on Graph Neural Networks","authors":"Xiuwen Liu, Yanjiao Chen, Shanchen Pang","doi":"10.1109/tifs.2026.3654865","DOIUrl":"https://doi.org/10.1109/tifs.2026.3654865","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"39 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Bridging Lab and Industry: Practical SPA-GPT on Cryptosystems Boosted by LSTM and Simulated Annealing","authors":"Ziyu Wang, Yaoling Ding, An Wang, Congming Wei, Jingqi Zhang, Liehuang Zhu","doi":"10.1109/tifs.2026.3654798","DOIUrl":"https://doi.org/10.1109/tifs.2026.3654798","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"177 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2026-01-14DOI: 10.1109/tifs.2026.3651978
Shuhan Qi, Qinglin Zhao, Zijie Liu, MengChu Zhou, Meng Shen, Peiyun Zhang, Yi Sun
{"title":"Modeling the Performance-Security Trade-off of Gasper’s Block Proposal Mechanism Under Latency-Driven Attacks","authors":"Shuhan Qi, Qinglin Zhao, Zijie Liu, MengChu Zhou, Meng Shen, Peiyun Zhang, Yi Sun","doi":"10.1109/tifs.2026.3651978","DOIUrl":"https://doi.org/10.1109/tifs.2026.3651978","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"60 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"RIS-Assisted Integrated Communication and Secret Key Generation in Quasi-Static Environments","authors":"Zheyuan Deng, Xiaoyan Hu, Keming Ma, Liang Jin, Boming Li, Jinghua Qu","doi":"10.1109/tifs.2026.3654398","DOIUrl":"https://doi.org/10.1109/tifs.2026.3654398","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"267 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2026-01-14DOI: 10.1109/tifs.2026.3654388
Sk Tanzir Mehedi, Chadni Islam, Gowri Ramachandran, Raja Jurdak
{"title":"DySec: A Machine Learning-based Dynamic Analysis for Detecting Malicious Packages in PyPI Ecosystem","authors":"Sk Tanzir Mehedi, Chadni Islam, Gowri Ramachandran, Raja Jurdak","doi":"10.1109/tifs.2026.3654388","DOIUrl":"https://doi.org/10.1109/tifs.2026.3654388","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"5 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2026-01-12DOI: 10.1109/TIFS.2026.3651997
Di Li;Chun Li;Yufeng Tang;Yupeng Zhang;Zheng Gong
Inconsistencies in manufacturing features, sampling settings, and cryptographic implementations amongst the profiling and target devices can lead to the failure of profiling side-channel analysis (SCA). Various techniques, such as preprocessing, multi-device training, and transfer learning, have been proposed to mitigate this portability problem in profiling SCA. However, many techniques of block ciphers, such as tweaks, key-dependent components, and customized elements, might have uncertain effects from the perspective of cryptographic implementations, requiring further insightful analysis on their impact on portability. This paper investigates the portability of profiling SCA from a case study using adjustable implementations of block ciphers. First, we theoretically analyze the variation in leakage distribution under adjustable implementations. To support our theoretical results, a dataset of deep-learning SCA is built from AES, Pilsung, and Skinny. Specifically, we reveal how to reverse the parameterized components and recover the key from these adjustable implementations. According to our experiment on an 8-bit AVR microcontroller, the computational complexities of the attacks based on our model are less than $9times 2^{16}$ within 4500 traces. Moreover, the effectiveness of our proposed method is demonstrated under the combinatorial effect with adjustable implementations and device characteristics. Our case study provides insights into the results of adjustable implementations of block ciphers, which strengthens both the theoretical and practical understanding of the portability of profiling SCA.
{"title":"Portability of Profiling Side-Channel Analysis: A Case Study Using Adjustable Implementations of Block Ciphers","authors":"Di Li;Chun Li;Yufeng Tang;Yupeng Zhang;Zheng Gong","doi":"10.1109/TIFS.2026.3651997","DOIUrl":"https://doi.org/10.1109/TIFS.2026.3651997","url":null,"abstract":"Inconsistencies in manufacturing features, sampling settings, and cryptographic implementations amongst the profiling and target devices can lead to the failure of profiling side-channel analysis (SCA). Various techniques, such as preprocessing, multi-device training, and transfer learning, have been proposed to mitigate this portability problem in profiling SCA. However, many techniques of block ciphers, such as tweaks, key-dependent components, and customized elements, might have uncertain effects from the perspective of cryptographic implementations, requiring further insightful analysis on their impact on portability. This paper investigates the portability of profiling SCA from a case study using adjustable implementations of block ciphers. First, we theoretically analyze the variation in leakage distribution under adjustable implementations. To support our theoretical results, a dataset of deep-learning SCA is built from AES, Pilsung, and Skinny. Specifically, we reveal how to reverse the parameterized components and recover the key from these adjustable implementations. According to our experiment on an 8-bit AVR microcontroller, the computational complexities of the attacks based on our model are less than <inline-formula> <tex-math>$9times 2^{16}$ </tex-math></inline-formula> within 4500 traces. Moreover, the effectiveness of our proposed method is demonstrated under the combinatorial effect with adjustable implementations and device characteristics. Our case study provides insights into the results of adjustable implementations of block ciphers, which strengthens both the theoretical and practical understanding of the portability of profiling SCA.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"1021-1035"},"PeriodicalIF":8.0,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2026-01-12DOI: 10.1109/TIFS.2026.3653580
Xunqiang Lan;Xiao Tang;Ruonan Zhang;Bin Li;Qinghe Du;Dusit Niyato;Zhu Han
Blockchain plays a crucial role in ensuring the security and integrity of decentralized systems, with the proof-of-work (PoW) mechanism being fundamental for achieving distributed consensus. As PoW blockchains see broader adoption, an increasingly diverse set of miners with varying computing capabilities participate in the network. In this paper, we consider the PoW blockchain mining, where the miners are associated with resource uncertainties. To characterize the uncertainty computing resources at different mining participants, we establish an ambiguous set representing uncertainty of resource distributions. Then, the networked mining is formulated as a non-cooperative game, where distributionally robust performance is calculated for each individual miner to tackle the resource uncertainties. We prove the existence of the equilibrium of the distributionally robust mining game. To derive the equilibrium, we propose the conditional value-at-risk (CVaR)-based reinterpretation of the best response of each miner. We then solve the individual strategy with alternating optimization, which facilitates the iteration among miners towards the game equilibrium. Furthermore, we consider the case that the ambiguity of resource distribution reduces to Gaussian distribution and the case that another uncertainties vanish, and then characterize the properties of the equilibrium therein along with a distributed algorithm to achieve the equilibrium. Simulation results show that the proposed approaches effectively converge to the equilibrium, and effectively tackle the uncertainties in blockchain mining to achieve a robust performance guarantee.
{"title":"Distributionally Robust Game for Proof-of-Work Blockchain Mining Under Resource Uncertainties","authors":"Xunqiang Lan;Xiao Tang;Ruonan Zhang;Bin Li;Qinghe Du;Dusit Niyato;Zhu Han","doi":"10.1109/TIFS.2026.3653580","DOIUrl":"https://doi.org/10.1109/TIFS.2026.3653580","url":null,"abstract":"Blockchain plays a crucial role in ensuring the security and integrity of decentralized systems, with the proof-of-work (PoW) mechanism being fundamental for achieving distributed consensus. As PoW blockchains see broader adoption, an increasingly diverse set of miners with varying computing capabilities participate in the network. In this paper, we consider the PoW blockchain mining, where the miners are associated with resource uncertainties. To characterize the uncertainty computing resources at different mining participants, we establish an ambiguous set representing uncertainty of resource distributions. Then, the networked mining is formulated as a non-cooperative game, where distributionally robust performance is calculated for each individual miner to tackle the resource uncertainties. We prove the existence of the equilibrium of the distributionally robust mining game. To derive the equilibrium, we propose the conditional value-at-risk (CVaR)-based reinterpretation of the best response of each miner. We then solve the individual strategy with alternating optimization, which facilitates the iteration among miners towards the game equilibrium. Furthermore, we consider the case that the ambiguity of resource distribution reduces to Gaussian distribution and the case that another uncertainties vanish, and then characterize the properties of the equilibrium therein along with a distributed algorithm to achieve the equilibrium. Simulation results show that the proposed approaches effectively converge to the equilibrium, and effectively tackle the uncertainties in blockchain mining to achieve a robust performance guarantee.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"1036-1049"},"PeriodicalIF":8.0,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anonymous submissions inspire people to speak up since hiding their identities can protect them from negative influence by their own words. However, the abuse of anonymity may bring harassment to those public submission callers. Existing works only handle DoS attacks or block harassment senders in an active manner, which behave poorly in the early prevention of uncharacterized harassment. In this paper, we propose MsgFliter, a sender-anonymous messaging system with proactive anti-harassment mechanism. Our core idea is to prevent unanswered senders from sending messages continually while keeping their identities, messages, and sender types secret. To meet the functionality and security requirements of MsgFliter, we propose the Anti-Harassment Anonymous Authentication (AHAA) protocol. We associate messages from the same sender through linkable tags and invalidate linkability when a message is replied to. To achieve session indistinguishability, we further combine the proposed anonymous authentication with zero-knowledge proofs of disjunctive relations. We implement MsgFliter and compare its performance with related solutions. Experimental results show that our solution is practicable.
{"title":"MsgFilter: Proactive Anti-Harassment Sender-Anonymous Messaging System","authors":"Siqin Li;Kun He;Min Shi;Yajing Huang;Ruiying Du;Jing Chen","doi":"10.1109/TIFS.2025.3650417","DOIUrl":"10.1109/TIFS.2025.3650417","url":null,"abstract":"Anonymous submissions inspire people to speak up since hiding their identities can protect them from negative influence by their own words. However, the abuse of anonymity may bring harassment to those public submission callers. Existing works only handle DoS attacks or block harassment senders in an active manner, which behave poorly in the early prevention of uncharacterized harassment. In this paper, we propose MsgFliter, a sender-anonymous messaging system with proactive anti-harassment mechanism. Our core idea is to prevent unanswered senders from sending messages continually while keeping their identities, messages, and sender types secret. To meet the functionality and security requirements of MsgFliter, we propose the Anti-Harassment Anonymous Authentication (AHAA) protocol. We associate messages from the same sender through linkable tags and invalidate linkability when a message is replied to. To achieve session indistinguishability, we further combine the proposed anonymous authentication with zero-knowledge proofs of disjunctive relations. We implement MsgFliter and compare its performance with related solutions. Experimental results show that our solution is practicable.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"741-756"},"PeriodicalIF":8.0,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145893717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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