Explore the top tech conferences to attend in 2026. Discover key dates, locations, and must-see events in AI, cloud, cybersecurity, IT, and emerging tech.
The post Top Tech Conferences to Attend in 2026 appeared first on TechRepublic.
Explore the top tech conferences to attend in 2026. Discover key dates, locations, and must-see events in AI, cloud, cybersecurity, IT, and emerging tech.
The post Top Tech Conferences to Attend in 2026 appeared first on TechRepublic.
Session 4B: Audio Security
Authors, Creators & Presenters:
PAPER
VoiceRadar: Voice Deepfake Detection using Micro-Frequency And Compositional Analysis
Recent advancements in synthetic speech generation, including text-to-speech (TTS) and voice conversion (VC) models, allow the generation of convincing synthetic voices, often referred to as audio deepfakes. These deepfakes pose a growing threat as adversaries can use them to impersonate individuals, particularly prominent figures, on social media or bypass voice authentication systems, thus having a broad societal impact. The inability of state-of-the-art verification systems to detect voice deepfakes effectively is alarming. We propose a novel audio deepfake detection method, VoiceRadar, that augments machine learning with physical models to approximate frequency dynamics and oscillations in audio samples. This significantly enhances detection capabilities. VoiceRadar leverages two main physical models: (i) the Doppler effect to understand frequency changes in audio samples and (ii) drumhead vibrations to decompose complex audio signals into component frequencies. VoiceRadar identifies subtle variations, or micro-frequencies, in the audio signals by applying these models. These micro-frequencies are aggregated to compute the observed frequency, capturing the unique signature of the audio. This observed frequency is integrated into the machine learning algorithm's loss function, enabling the algorithm to recognize distinct patterns that differentiate human-produced audio from AI-generated audio. We constructed a new diverse dataset to comprehensively evaluate VoiceRadar, featuring samples from leading TTS and VC models. Our results demonstrate that VoiceRadar outperforms existing methods in accurately identifying AI-generated audio samples, showcasing its potential as a robust tool for audio deepfake detection.
ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.
Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.
The post NDSS 2025 – VoiceRadar: Voice Deepfake Detection Using Micro-Frequency And Compositional Analysis appeared first on Security Boulevard.
Session4A: IoT Security
Authors, Creators & Presenters: Eman Maali (Imperial College London), Omar Alrawi (Georgia Institute of Technology), Julie McCann (Imperial College London)
PAPER
Evaluating Machine Learning-Based IoT Device Identification Models for Security Applications
With the proliferation of IoT devices, network device identification is essential for effective network management and security. Many exhibit performance degradation despite the potential of machine learning-based IoT device identification solutions. Degradation arises from the assumption of static IoT environments that do not account for the diversity of real-world IoT networks, as devices operate in various modes and evolve over time. In this paper, we evaluate current IoT device identification solutions using curated datasets and representative features across different settings. We consider key factors that affect real-world device identification, including modes of operation, spatio-temporal variations, and traffic sampling, and organise them into a set of attributes by which we can evaluate current solutions. We then use machine learning explainability techniques to pinpoint the key causes of performance degradation. This evaluation uncovers empirical evidence of what continuously identifies devices, provides valuable insights, and practical recommendations for network operators to improve their IoT device identification in operational deployments
ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.
Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.
The post NDSS 2025 – Machine Learning-Based loT Device Identification Models For Security Applications appeared first on Security Boulevard.
Session4A: IoT Security
Authors, Creators & Presenters: Haoqiang Wang, Yiwei Fang (Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences; Indiana University Bloomington), Yichen Liu (Indiana University Bloomington), Ze Jin (Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences; Indiana University Bloomington), Emma Delph (Indiana University Bloomington), Xiaojiang Du (Stevens Institute of Technology), Qixu Liu (Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences), Luyi Xing (Indiana University Bloomington)
PAPER
Hidden and Lost Control: on Security Design Risks in IoT User-Facing Matter Controller
Matter is emerging as an IoT industry--unifying standard, aiming to enhance the interoperability among diverse smart home products, enabling them to work securely and seamlessly together. With many popular IoT vendors increasingly supporting Matter in consumer IoT products, we perform a systematic study to investigate how and whether vendors can integrate Matter securely into IoT systems and how well Matter as a standard supports vendors' secure integration. By analyzing Matter development model in the wild, we reveal a new kind of design flaw in user-facing Matter control capabilities and interfaces, called UMCCI flaws, which are exploitable vulnerabilities in the design space and seriously jeopardize necessary control and surveillance capabilities of Matter-enabled devices for IoT users. Therefore we built an automatic tool called UMCCI Checker, enhanced by the large-language model in UI analysis, which enables automatically detecting UMCCI flaws without relying on real IoT devices. Our tool assisted us with studying and performing proof-of-concept attacks on 11 real Matter devices of 8 popular vendors to confirm that the UMCCI flaws are practical and common. We reported UMCCI flaws to related vendors, which have been acknowledged by CSA, Apple, Tuya, Aqara, etc. To help CSA and vendors better understand and avoid security flaws in developing and integrating IoT standards like Matter, we identify two categories of root causes and propose immediate fix recommendations.
ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.
Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.
The post NDSS 2025 – Hidden And Lost Control: On Security Design Risks In loT User-Facing Matter Controller appeared first on Security Boulevard.
Session4A: IoT Security
Authors, Creators & Presenters: Hangtian Liu (Information Engineering University), Lei Zheng (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University), Shuitao Gan (Laboratory for Advanced Computing and Intelligence Engineering), Chao Zhang (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University), Zicong Gao (Information Engineering University), Hongqi Zhang (Henan Key Laboratory of Information Security), Yishun Zeng (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University), Zhiyuan Jiang (National University of Defense Technology), Jiahai Yang (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University)
PAPER
EAGLEYE: Exposing Hidden Web Interfaces in IoT Devices via Routing Analysis [https://www.ndss-symposium.org/wp-con...](https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbEEzMmJxSkNwUUhDUkMteHZraTQ1blZ5Sk0zUXxBQ3Jtc0tuZldzQXZxQXJaOGt0VDU2RGNPdGVSbnMzcWxiTVZ1UmJsTzcyaUlCTFdvbmhoWnZRdWQ0UlJiUEs4ekR1UXNCNF9KQmp4UGxKOG5kMHdBdHBiaWh6ckxFaGphY0JVRDZDQ21jUWcyREx2Qy1XVTJqWQ&q=https%3A%2F%2Fwww.ndss-symposium.org%2Fwp-content%2Fuploads%2F2025-399-paper.pdf&v=qXDD2iiIeCg) Hidden web interfaces, i.e., undisclosed access channels in IoT devices, introduce great security risks and have resulted in severe attacks in recent years. However, the definition of such threats is vague, and few solutions are able to discover them. Due to their hidden nature, traditional bug detection solutions (e.g., taint analysis, fuzzing) are hard to detect them. In this paper, we present a novel solution EAGLEYE to automatically expose hidden web interfaces in IoT devices. By analyzing input requests to public interfaces, we first identify routing tokens within the requests, i.e., those values (e.g., actions or file names) that are referenced and used as index by the firmware code (routing mechanism) to find associated handler functions. Then, we utilize modern large language models to analyze the contexts of such routing tokens and deduce their common pattern, and then infer other candidate values (e.g., other actions or file names) of these tokens. Lastly, we perform a hidden-interface directed black-box fuzzing, which mutates the routing tokens in input requests with these candidate values as the high-quality dictionary. We have implemented a prototype of EAGLEYE and evaluated it on 13 different commercial IoT devices. EAGLEYE successfully found 79 hidden interfaces, 25X more than the state-of-the-art (SOTA) solution IoTScope. Among them, we further discovered 29 unknown vulnerabilities including backdoor, XSS (cross-site scripting), command injection, and information leakage, and have received 7 CVEs.
ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.
Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.
The post NDSS 2025 – EAGLEYE: Exposing Hidden Web Interfaces In loT Devices Via Routing Analysis appeared first on Security Boulevard.
Session4A: IoT Security
Authors, Creators & Presenters: Christopher Ellis (The Ohio State University), Yue Zhang (Drexel University), Mohit Kumar Jangid (The Ohio State University), Shixuan Zhao (The Ohio State University), Zhiqiang Lin (The Ohio State University)
PAPER
Deanonymizing Device Identities via Side-channel Attacks in Exclusive-use IoTs & Mitigation Wireless technologies like Bluetooth Low Energy (BLE) and Wi-Fi are essential to the Internet of Things (IoT), facilitating seamless device communication without physical connections. However, this convenience comes at a cost--exposed data exchanges that are susceptible to observation by attackers, leading to serious security and privacy threats such as device tracking. Although protocol designers have traditionally relied on strategies like address and identity randomization as a countermeasure, our research reveals that these attacks remain a significant threat due to a historically overlooked, fundamental flaw in exclusive-use wireless communication. We define exclusive-use as a scenario where devices are designed to provide functionality solely to an associated or paired device. The unique communication patterns inherent in these relationships create an observable boolean side-channel that attackers can exploit to discover whether two devices "trust" each other. This information leak allows for the deanonymization of devices, enabling tracking even in the presence of modern countermeasures. We introduce our tracking attacks as IDBleed and demonstrate that BLE and Wi-Fi protocols that support confidentiality, integrity, and authentication remain vulnerable to deanonymization due to this fundamental flaw in exclusive-use communication patterns. Finally, we propose and quantitatively evaluate a generalized, privacy-preserving mitigation we call Anonymization Layer to find a negligible 2% approximate overhead in performance and power consumption on tested smartphones and PCs.
ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.
Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.
The post NDSS 2025 – Deanonymizing Device Identities Via Side-Channel Attacks In Exclusive-Use IoTs appeared first on Security Boulevard.
SESSION
Session 3D: AI Safety
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Authors, Creators & Presenters: Yan Pang (University of Virginia), Aiping Xiong (Penn State University), Yang Zhang (CISPA Helmholtz Center for Information Security), Tianhao Wang (University of Virginia)
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PAPER
Towards Understanding Unsafe Video Generation
Video generation models (VGMs) have demonstrated the capability to synthesize high-quality output. It is important to understand their potential to produce unsafe content, such as violent or terrifying videos. In this work, we provide a comprehensive understanding of unsafe video generation.
First, to confirm the possibility that these models could indeed generate unsafe videos, we choose unsafe content generation prompts collected from 4chan and Lexica, and three open-source SOTA VGMs to generate unsafe videos. After filtering out duplicates and poorly generated content, we created an initial set of 2112 unsafe videos from an original pool of 5607 videos. Through clustering and thematic coding analysis of these generated videos, we identify 5 unsafe video categories: Distorted/Weird, Terrifying, Pornographic, Violent/Bloody, and Political. With IRB approval, we then recruit online participants to help label the generated videos. Based on the annotations submitted by 403 participants, we identified 937 unsafe videos from the initial video set. With the labeled information and the corresponding prompts, we created the first dataset of unsafe videos generated by VGMs. We then study possible defense mechanisms to prevent the generation of unsafe videos. Existing defense methods in image generation focus on filtering either input prompt or output results. We propose a new approach called sysname, which works within the model's internal sampling process. sysname can achieve 0.90 defense accuracy while reducing time and computing resources by 10 times when sampling a large number of unsafe prompts. Our experiment includes three open-source SOTA video diffusion models, each achieving accuracy rates of 0.99, 0.92, and 0.91, respectively. Additionally, our method was tested with adversarial prompts and on image-to-video diffusion models, and achieved nearly 1.0 accuracy on both settings. Our method also shows its interoperability by improving the performance of other defenses when combined with them.
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ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.
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Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.
The post NDSS 2025 – Towards Understanding Unsafe Video Generation appeared first on Security Boulevard.
SESSION
Session 3D: AI Safety
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Authors, Creators & Presenters: Haotian Zhu (Nanjing University of Science and Technology), Shuchao Pang (Nanjing University of Science and Technology), Zhigang Lu (Western Sydney University), Yongbin Zhou (Nanjing University of Science and Technology), Minhui Xue (CSIRO's Data61)
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PAPER
GAP-Diff: Protecting JPEG-Compressed Images From Diffusion-Based Facial Customization
Text-to-image diffusion model's fine-tuning technology allows people to easily generate a large number of customized photos using limited identity images. Although this technology is easy to use, its misuse could lead to violations of personal portraits and privacy, with false information and harmful content potentially causing further harm to individuals. Several methods have been proposed to protect faces from customization via adding protective noise to user images by disrupting the fine-tuned models.
Unfortunately, simple pre-processing techniques like JPEG compression, a normal pre-processing operation performed by modern social networks, can easily erase the protective effects of existing methods. To counter JPEG compression and other potential pre-processing, we propose GAP-Diff, a framework of Generating data with Adversarial Perturbations for text-to-image Diffusion models using unsupervised learning-based optimization, including three functional modules. Specifically, our framework learns robust representations against JPEG compression by backpropagating gradient information through a pre-processing simulation module while learning adversarial characteristics for disrupting fine-tuned text-to-image diffusion models. Furthermore, we achieve an adversarial mapping from clean images to protected images by designing adversarial losses against these fine-tuning methods and JPEG compression, with stronger protective noises within milliseconds. Facial benchmark experiments, compared to state-of-the-art protective methods, demonstrate that GAP-Diff significantly enhances the resistance of protective noise to JPEG compression, thereby better safeguarding user privacy and copyrights in the digital world.
ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.
Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.
The post NDSS 2025 – GAP-Diff: Protecting JPEG-Compressed Images From Diffusion-Based Facial Customization appeared first on Security Boulevard.
SESSION
Session 3D: Al Safety
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Authors, Creators & Presenters: Yutong Wu (Nanyang Technological University), Jie Zhang (Centre for Frontier AI Research, Agency for Science, Technology and Research (A*STAR), Singapore), Florian Kerschbaum (University of Waterloo), Tianwei Zhang (Nanyang Technological University)
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PAPER
THEMIS: Regulating Textual Inversion for Personalized Concept Censorship
Personalization has become a crucial demand in the Generative AI technology. As the pre-trained generative model (e.g., stable diffusion) has fixed and limited capability, it is desirable for users to customize the model to generate output with new or specific concepts. Fine-tuning the pre-trained model is not a promising solution, due to its high requirements of computation resources and data. Instead, the emerging personalization approaches make it feasible to augment the generative model in a lightweight manner. However, this also induces severe threats if such advanced techniques are misused by malicious users, such as spreading fake news or defaming individual reputations. Thus, it is necessary to regulate personalization models (i.e., achieve concept censorship) for their development and advancement. In this paper, we focus on the regulation of a popular personalization technique dubbed textbf{Textual Inversion (TI)}, which can customize Text-to-Image (T2I) generative models with excellent performance. TI crafts the word embedding that contains detailed information about a specific object. Users can easily add the word embedding to their local T2I model, like the public Stable Diffusion (SD) model, to generate personalized images. The advent of TI has brought about a new business model, evidenced by the public platforms for sharing and selling word embeddings (e.g., Civitai [1]). Unfortunately, such platforms also allow malicious users to misuse the word embeddings to generate unsafe content, causing damages to the concept owners. We propose THEMIS to achieve the personalized concept censorship. Its key idea is to leverage the backdoor technique for good by injecting positive backdoors into the TI embeddings. Briefly, the concept owner selects some sensitive words as triggers during the training of TI, which will be censored for normal use. In the subsequent generation stage, if a malicious user combines the sensitive words with the personalized embeddings as final prompts, the T2I model will output a pre-defined target image rather than images including the desired malicious content. To demonstrate the effectiveness of THEMIS, we conduct extensive experiments on the TI embeddings with Latent Diffusion and Stable Diffusion, two prevailing open-sourced T2I models. The results demonstrate that THEMIS is capable of preventing Textual Inversion from cooperating with sensitive words meanwhile guaranteeing its pristine utility. Furthermore, THEMIS is general to different uses of sensitive words, including different locations, synonyms, and combinations of sensitive words. It can also resist different types of potential and adaptive attacks. Ablation studies are also conducted to verify our design.
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ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.
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Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.
The post NDSS 2025 – THEMIS: Regulating Textual Inversion For Personalized Concept Censorship appeared first on Security Boulevard.
SESSION
Session 3D: Al Safety
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Authors, Creators & Presenters: Miaomiao Wang (Shanghai University), Guang Hua (Singapore Institute of Technology), Sheng Li (Fudan University), Guorui Feng (Shanghai University)
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PAPER
A Key-Driven Framework for Identity-Preserving Face Anonymization
Virtual faces are crucial content in the metaverse. Recently, attempts have been made to generate virtual faces for privacy protection. Nevertheless, these virtual faces either permanently remove the identifiable information or map the original identity into a virtual one, which loses the original identity forever. In this study, we first attempt to address the conflict between privacy and identifiability in virtual faces, where a key-driven face anonymization and authentication recognition (KFAAR) framework is proposed. Concretely, the KFAAR framework consists of a head posture-preserving virtual face generation (HPVFG) module and a key-controllable virtual face authentication (KVFA) module. The HPVFG module uses a user key to project the latent vector of the original face into a virtual one. Then it maps the virtual vectors to obtain an extended encoding, based on which the virtual face is generated. By simultaneously adding a head posture and facial expression correction module, the virtual face has the same head posture and facial expression as the original face. During the authentication, we propose a KVFA module to directly recognize the virtual faces using the correct user key, which can obtain the original identity without exposing the original face image. We also propose a multi-task learning objective to train HPVFG and KVFA. Extensive experiments demonstrate the advantages of the proposed HPVFG and KVFA modules, which effectively achieve both facial anonymity and identifiability.
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ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.
Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.
The post NDSS 2025 – A Key-Driven Framework For Identity-Preserving Face Anonymization appeared first on Security Boulevard.
SESSION
Session 3C: Mobile Security
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Authors, Creators & Presenters: Runze Zhang (Georgia Institute of Technology), Mingxuan Yao (Georgia Institute of Technology), Haichuan Xu (Georgia Institute of Technology), Omar Alrawi (Georgia Institute of Technology), Jeman Park (Kyung Hee University), Brendan Saltaformaggio (Georgia Institute of Technology)
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PAPER
Hitchhiking Vaccine: Enhancing Botnet Remediation With Remote Code Deployment Reuse
For decades, law enforcement and commercial entities have attempted botnet takedowns with mixed success. These efforts, relying on DNS sink-holing or seizing C&C infrastructure, require months of preparation and often omit the cleanup of left-over infected machines. This allows botnet operators to push updates to the bots and re-establish their control. In this paper, we expand the goal of malware takedowns to include the covert and timely removal of frontend bots from infected devices. Specifically, this work proposes seizing the malware's built-in update mechanism to distribute crafted remediation payloads. Our research aims to enable this necessary but challenging remediation step after obtaining legal permission. We developed ECHO, an automated malware forensics pipeline that extracts payload deployment routines and generates remediation payloads to disable or remove the frontend bots on infected devices. Our study of 702 Android malware shows that 523 malware can be remediated via ECHO's takedown approach, ranging from covertly warning users about malware infection to uninstalling the malware.
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ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.
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Our thanks to the **[Network and Distributed System Security (NDSS) Symposium][1]** for publishing their Creators, Authors and Presenter’s superb **[NDSS Symposium 2025 Conference][2]** content on the **[organization’s’][1]** **[YouTube][3]** channel.
The post NDSS 2025 – Hitchhiking Vaccine: Enhancing Botnet Remediation With Remote Code Deployment Reuse appeared first on Security Boulevard.
SESSION
Session 3C: Mobile Security
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Authors, Creators & Presenters: Chang Yue (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China), Kai Chen (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China), Zhixiu Guo (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China), Jun Dai, Xiaoyan Sun (Department of Computer Science, Worcester Polytechnic Institute), Yi Yang (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China)
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PAPER
What's Done Is Not What's Claimed: Detecting and Interpreting Inconsistencies in App Behaviors
The widespread use of mobile apps meets user needs but also raises security concerns. Current security analysis methods often fall short in addressing user concerns as they do not parse app behavior from the user's standpoint, leading to users not fully understanding the risks within the apps and unknowingly exposing themselves to privacy breaches. On one hand, their analysis and results are usually presented at the code level, which may not be comprehensible to users. On the other hand, they neglect to account for the users' perceptions of the app behavior. In this paper, we aim to extract user-related behaviors from apps and explain them to users in a comprehensible natural language form, enabling users to perceive the gap between their expectations and the app's actual behavior, and assess the risks within the inconsistencies independently. Through experiments, our tool InconPreter is shown to effectively extract inconsistent behaviors from apps and provide accurate and reasonable explanations. InconPreter achieves an inconsistency identification precision of 94.89% on our labeled dataset, and a risk analysis accuracy of 94.56% on widely used Android malware datasets. When applied to real-world (wild) apps, InconPreter identifies 1,664 risky inconsistent behaviors from 413 apps out of 10,878 apps crawled from Google Play, including the leakage of location, SMS, and contact information, as well as unauthorized audio recording, etc., potentially affecting millions of users. Moreover, InconPreter can detect some behaviors that are not identified by previous tools, such as unauthorized location disclosure in various scenarios (e.g. taking photos, chatting, and enabling mobile hotspots, etc.). We conduct a thorough analysis of the discovered behaviors to deepen the understanding of inconsistent behaviors, thereby helping users better manage their privacy and providing insights for privacy design in further app development.
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ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.
-----------
Our thanks to the **[Network and Distributed System Security (NDSS) Symposium][1]** for publishing their Creators, Authors and Presenter’s superb **[NDSS Symposium 2025 Conference][2]** content on the **[organization’s’][1]** **[YouTube][3]** channel.
The post NDSS 2025 – Detecting And Interpreting Inconsistencies In App Behaviors appeared first on Security Boulevard.
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SESSION
Session 3C: Mobile Security
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Authors, Creators & Presenters: Yuqing Yang (The Ohio State University), Yue Zhang (Drexel University), Zhiqiang Lin (The Ohio State University)
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PAPER
Understanding Miniapp Malware: Identification, Dissection, and Characterization
Super apps, serving as centralized platforms that manage user information and integrate third-party miniapps, have revolutionized mobile computing but also introduced significant security risks from malicious miniapps. Despite the mandatory miniapp vetting enforced to the built-in miniapp store, the threat of evolving miniapp malware persists, engaging in a continual cat-and-mouse game with platform security measures. However, compared with traditional paradigms such as mobile and web computing, there has been a lack of miniapp malware dataset available for the community to explore, hindering the generation of crucial insights and the development of robust detection techniques. In response to this, this paper addresses the scarcely explored territory of malicious miniapp analysis, dedicating over three year to identifying, dissecting, and examining the risks posed by these miniapps, resulting in the first miniapp malware dataset now available to aid future studies to enhance the security of super app ecosystems. To build the dataset, our primary focus has been on the WeChat platform, the largest super app, hosting millions of miniapps and serving a billion users. Over an extensive period, we collected over 4.5 million miniapps, identifying a subset (19, 905) as malicious through a rigorous cross-check process: 1) applying static signatures derived from real-world cases, and 2) confirming that the miniapps were delisted and removed from the market by the platform. With these identified samples, we proceed to characterize them, focusing on their lifecycle including propagation, activation, as well as payload execution. Additionally, we analyzed the collected malware samples using real-world cases to demonstrate their practical security impact. Our findings reveal that these malware frequently target user privacy, leverage social network sharing capabilities to disseminate unauthorized services, and manipulate the advertisement-based revenue model to illicitly generate profits. These actions result in significant privacy and financial harm to both users and the platform.
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ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.
-----------
Our thanks to the **[Network and Distributed System Security (NDSS) Symposium][1]** for publishing their Creators, Authors and Presenter’s superb **[NDSS Symposium 2025 Conference][2]** content on the **[organization’s’][1]** **[YouTube][3]** channel.
The post NDSS 2025 – Understanding Miniapp Malware: Identification, Dissection, And Characterization appeared first on Security Boulevard.
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