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Location-based pairwise key predistribution protocols have been developed as promising solutions for addressing security concerns in wireless sensor networks. These methods efficiently distribute symmetric keys among sensor nodes based on their locations, thereby enhancing security. However, prior designs often assumed ideal node locations without giving sufficient consideration to possible deployment errors. Such an error, causing a node to move out of its intended grid cell location, can significantly impact network connectivity. In real-world scenarios, these errors may arise due to 3D terrain features, such as slopes and other physical environmental factors. Unfortunately, these factors have often been overlooked by existing location-based schemes, indicating the necessity for revisiting these methods. In this paper, we bridge this gap by simulating node deployments across various 3D terrains while considering physical factors. We scrutinize the correlation between physical environmental factors and deployment errors and evaluate the performance of both computation-based and storage-based protocols. Furthermore, we identify conditions that minimize deployment errors, thereby enhancing the performance of location-based pairwise key predistribution protocols. Our experimental results show that terrain features and external physical factors significantly influence the performance of location-based protocols, and their consideration is necessary.

As Generative Adversarial Networks advance, deepfakes have become increasingly realistic, thereby escalating societal, economic, and political threats. In confronting these heightened risks, the research community has identified two promising defensive strategies: proactive deepfake disruption and reactive deepfake detection. Typically, proactive and reactive defenses coexist, each addressing the shortcomings of the other. However, this paper brings to the fore a critical yet overlooked issue associated with the simultaneous deployment of these deepfake countermeasures. Genuine images gathered from the Internet, already imbued with disrupting perturbations, can lead to data poisoning in the training datasets of deepfake detection models, thereby severely affecting detection accuracy. We propose an improved training framework to address this problem in deepfake detection models. Our approach involves purifying the disrupting perturbations in disruptive images using a backward process of the denoising diffusion probabilistic model (DDPM). Images purified using our DDPM-based technique closely mimic the original, unperturbed images, thereby enabling the successful generation of deepfake images for training purposes. Moreover, our purification process outperforms DiffPure, a prominent adversarial purification method, in terms of speed. While conventional defensive techniques struggle to preserve detection accuracy in the face of a poisoned training dataset, our framework markedly reduces this accuracy drop, thus achieving superior performance across a range of detection models. Our experiments demonstrate that deepfake detection models trained using our framework exhibit an increase in detection accuracy ranging from 11.24%p to 45.72%p when compared to models trained with the DiffPure method. Our implementation is available at https://github.com/seclab-yonsei/Anti-disrupt.

The confidentiality threat against training data has become a significant security problem in neural language models. Recent studies have shown that memorized training data can be extracted by injecting well-chosen prompts into generative language models. While these attacks have achieved remarkable success in the English-based Transformer architecture, it is unclear whether they are still effective in other language domains. This paper studies the effectiveness of attacks against Korean models and the potential for attack improvements that might be beneficial for future defense studies. The contribution of this study is two-fold. First, we perform a membership inference attack against the state-of-the-art Korean GPT model. We found approximate training data with 20% to 90% precision in the top-100 samples and confirmed that the proposed attack technique for naive GPT is valid across the language domains. Second, in this process, we observed that the redundancy of the selected sentences could hardly be detected with the existing attack method. Since the information appearing in a few documents is more likely to be meaningful, it is desirable to increase the uniqueness of the sentences to improve the effectiveness of the attack. Thus, we propose a deduplication strategy to replace the traditional word-level similarity metric with the BPE token level. Our proposed strategy reduces 6% to 22% of the underestimated samples among selected ones, thereby improving precision by up to 7%p. As a result, we show that considering both language- and model-specific characteristics is essential to improve the effectiveness of attack strategies. We also discuss possible mitigations against the MI attacks on the general language models.

Firmware vulnerabilities raise serious security concerns with the rapid growth in connected embedded devices. Fuzzing is an effective dynamic testing technique to find those vulnerabilities; however, firmware fuzzing is very limited by hardware dependence, such as on-chip and off-chip peripherals. The latest elegant approaches are making substantial progress in hardware-independent firmware fuzzing, but there is room for further improvement. We observe that hardware-independent peripheral modeling is scalable but slow at the register level; in contrast, at the abstract function level, it is fast but has limited scalability. Firmware fuzzing is still challenging in terms of achieving both scalability and efficiency. To address this problem, we present a dual-level approach that leverages register level modeling and selective function level modeling in a hybrid manner. Our method starts firmware fuzzing at the register level and connects peripheral handlers while executing hardware abstraction layer functions. We evaluate our method in terms of efficiency, scalability, and effectiveness with four real-world firmware and demonstrate the possibility of relatively fast and scalable firmware fuzzing that combines the benefits of the two levels.

The OS kernel, which has full system privileges, is an attractive attack surface. A kernel fuzzer that targets system calls in fuzzing is a popular tool for discovering kernel bugs that can induce kernel privilege escalation attacks. To the best of our knowledge, the relevance of code coverage, which is obtained by fuzzing, to the system call has not been studied yet. For instance, modern coverage-guided kernel fuzzers, such as Syzkaller, estimate code coverage by comparing the entire set of executed basic blocks (or edges) regardless of the system call relevancy. Our insight is that the system call relevancy could be an essential performance indicator for realizing kernel fuzzing. In this regard, this study aims to assess the system call-related code coverage of kernel fuzzers. For this purpose, we have developed a practical assessment system that leverages the Intel PT and KCOV and assessed the Linux kernel fuzzers, such as Syzkaller, Trinity, and ext4 fuzzer. The experiments on different kernel versions demonstrated that approximately 32,000–47,000 functions are implemented in the Linux kernel, and approximately 9.7–15.2% are related to the system call. Our finding is that fuzzers that achieve higher code coverage in conventional metrics do not execute more basic blocks related to system calls. Thus, we recommend that kernel fuzzers use both system call-related functions and regular basic blocks in coverage metrics to assess fuzzing performance or to improve coverage feedback.

Wireless sensor networks (WSNs) have played an important role in the Internet of Things (IoT), and the 5G network is being considered as a major candidate for IoT's communication network with the advent of 5G commercialization. The potential of integrating WSNs and 5G in the IoT is expected to allow IoT to penetrate deeply into our daily lives and to provide various services that are convenient, but at the same time, it also brings new security threats. From this aspect, user authentication and key agreement are essential for secure end-to-end communication. As IoT devices, including sensors, collect and process more and more personal information, both anonymous authentication and authorization are also required to protect the privacy and to prevent anyone without privileges from accessing private data. Recently, Adavoudi-Jolfaei et al. proposed an anonymous three-factor authentication and access control scheme for real-time applications in WSNs. However, we found that this scheme does not provide sensor-node anonymity and suffers from user collusion and desynchronization attacks. In this paper, we introduce a system architecture by considering the integration of WSNs and 5G for IoT. Based on a cryptanalysis of Adavoudi-Jolfaei et al.'s scheme and the system architecture, we propose an elliptic curve cryptography (ECC)-based privacy-preserving authentication, authorization, and key agreement scheme for WSNs in 5G-integrated IoT. We conduct a formal and informal security analysis in order to demonstrate that the proposed scheme withstands various security attacks and guarantees all desired security features, overcoming the drawbacks of Adavoudi-Jolfaei et al.'s scheme. Finally, a performance and comparative analysis with the related schemes indicate that the proposed scheme is both efficient and more secure.

We are facing a big data world, embedded with interconnected IoT (Internet of Things) devices that generate large volumes of data. They pose a significant challenge to academia and industries focused on digital security: A variety of new malware and other threats are emerging at a fast pace, and existing preventive methods are struggling to deal with them within the golden time by depending solely on the known attack signature. Recently, there has been considerable advancement in computing, particularly in the field of Artificial Intelligence (AI). Advanced technologies such as Machine Learning and Deep Learning are being actively deployed in cyber security, and new results and issues have been reported.

Android pattern lock is still popularly used for mobile user authentication. Unfortunately, however, many concerns have been raised regarding its security and usability. User-created patterns tend to be simply structured or reduced to a small set. Complex patterns are hard to memorize. Input patterns are susceptible to various attacks, such as guessing attacks, smudge attacks, and shoulder surfing attacks. This paper presents a novel mechanism based on the pattern lock, in which behavioral biometrics are employed to address these problems. Our basic idea starts from turning the lock pattern into public knowledge rather than a secret and leveraging touch dynamics. Users do not need to create their own lock patterns or memorize them. Instead, our system shows a public pattern along with guidance on how to draw it. All the user needs to do for authentication is to draw the pattern as shown. For adversaries, the above-mentioned attacks are rendered useless by this new mechanism. Specifically, we study how to generate the public patterns and how to perform authentication. We considered segments, angles, directions, and turns as units for constructing the lock patterns, and established the public pattern criteria. The results are utilized to generate four public patterns in our experiment. For authentication, we achieved equal error rates (EERs) as low as 2.66% (sitting), 3.53% (walking), and 5.83% (combined). Furthermore, the results of our additional experiments demonstrated that our system preserved performance over time (F1-score = 89.88%, SD = 4.60%), and was sufficiently secure against camera-based recording attacks (FAR = 3.25%).

The integration of 5G networks and wireless sensor networks (WSNs) is critical in the new era of the Internet of Things (IoT), for a wide range of applications. However, despite the potential advantages of this integration, there are concerns about unforeseen security threats that may impact our daily lives. Authenticated key agreement is an essential security feature for secure communication between users and IoT devices, and for protecting IoT applications from security threats. An IoT notion-based authentication and key agreement scheme was recently proposed for heterogeneous WSNs, claiming to provide user anonymity and mutual authentication, as well as the ability to withstand several types of attacks. In this paper, we examine several security weaknesses of the aforementioned scheme. Next, we design a network architecture suitable for the integration of 5G networks and WSNs. Based on the network architecture, we propose a two-factor authentication and key agreement scheme in 5G-integrated WSNs for the IoT that can resist various attacks, including those identified earlier, and that can preserve security requirements, including unlinkability. Finally, we evaluate the security and performance of the proposed scheme and compare our scheme with other related schemes.

When a wearable device such as a smartwatch is paired with a host device, e.g., a smartphone, it is inevitable that a certain amount of data stored in the host device will be copied to the wearable device for initialization and personalization purposes. This incident may frequently occur with the Internet of Things. However, it is not well known, particularly among users, what type of data and how much of it is actually being copied or retained as a result of device pairing. Thus, it is unclear whether users are properly managing smartphones and smartwatches based on their stored data. In this regard, we coined a new term called data transfusion to describe the phenomenon in which a user experiences data transfer while pairing but without having any knowledge regarding the data. To the best of our knowledge, there are no previous studies that deal with how much sensitive data are transfused regardless of user consent, and how users perceive and behave toward such a phenomenon for smartwatches. As this is a significant issue with the Internet of Things, we tackle this problem in two ways. We first conduct an experimental study of data extraction from commodity devices such as in Android Wear, watchOS, and Tizen platforms, followed by a survey of 205 smartwatch users. The experimental study reveals that large amounts of sensitive data are being transfused without sufficient user notification. The survey demonstrates that users have lower risk perceptions for smartwatches than for smartphones in terms of security and privacy, but they tend to set the same passcode on both devices when needed. Based on the results, we perform risk assessment and discuss possible mitigation measures that involve volatile transfusion for securing the Internet of Things.

The Industrial Internet of Things (IIoT) could enhance automation and analytics in industrial environments. Despite the promising benefits of IIoT, securely managing software updates is a challenging problem for those critical applications. This is due to at least the intrinsic lack of software protection mechanisms in legacy industrial systems. In this paper, to address the challenges in building a secure software supply chain for industrial environments, we propose a new approach that leverages distributed watchdogs with blockchain systems in protecting software supply chains. For this purpose, we bind every entity with a unique identity in the blockchain and employ the blockchain as a delegated authenticator by mapping every reporting action to a non-fungible token transfer. Moreover, we present a detailed specification to clearly define the behavior of systems and to apply model checking.

To meet password selection criteria of a server, a user occasionally needs to provide multiple choices of password candidates to an on-line password meter, but such user-chosen candidates tend to be derived from the user’s previous passwords—the meter may have a high chance to acquire information about a user’s passwords employed for various purposes. A third party password metering service may worsen this threat. In this paper, we first explore a new on-line password meter concept that does not necessitate the exposure of user’s passwords for evaluating user-chosen password candidates in the server side. Our basic idea is straightforward; to adapt fully homomorphic encryption (FHE) schemes to build such a system but its performance achievement is greatly challenging. Optimization techniques are necessary for performance achievement in practice. We employ various performance enhancement techniques and implement the NIST (National Institute of Standards and Technology) metering method as seminal work in this field. Our experiment results demonstrate that the running time of the proposed meter is around 60 s in a conventional desktop server, expecting better performance in high-end hardware, with an FHE scheme in HElib library where parameters support at least 80-bit security. We believe the proposed method can be further explored and used for a password metering in case that password secrecy is very important—the user’s password candidates should not be exposed to the meter and also an internal mechanism of password metering should not be disclosed to users and any other third parties.

Wireless sensor networks are widely used in many mission critical applications such as environment monitoring, military monitoring, and healthcare. The highly sensitive nature of collected information and various potential security threats make security in wireless sensor networks a crucial concern. For resource-constrained nature of these special networks, the design of lightweight cryptography has become active research topic over the last several years. In this article, we provide experimental performance analysis of several lightweight block ciphers and message authentication codes to help for choosing better algorithms for wireless sensor networks in terms of efficiency. Unlike the previous simulation-based benchmarking projects, we employ representative three real-world devices of wireless sensor networks: 8-bit AVR (Arduino Uno), 16-bit MSP (Tmote), and 32-bit ARM (Raspberry Pi 2) microcontrollers. We also introduce an efficient implementation of rotations that can improve the performance of lightweight block ciphers with 32-bit rotation operations on both 8-bit AVR and 16-bit MSP microcontrollers and verify this methodology. We evaluate and compare performance of selected block ciphers and message authentication codes to recommend better solutions for wireless sensor networks. Based on our results, we show that the simulation-based results could be inaccurate and different from results of real implementations. As a result of our experimental performance analysis, we identify lightweight block ciphers and message authentication codes suitable for wireless sensor networks and provide their feasibilities.

A wireless sensor network (WSN) is used for a smart home system’s backbone that monitors home environment and controls smart home devices to manage lighting, heating, security and surveillance. However, despite its convenience and potential benefits, there are concerns about various security threats that may infringe on privacy and threaten our home life. For protecting WSNs for smart homes from those threats, authentication and key agreement are basic security requirements. There have been a large number of proposed authentication and key agreement scheme for WSNs. In 2017, Jung et al. proposed an efficient and security enhanced anonymous authentication with key agreement scheme by employing biometrics information as the third authentication factor. They claimed that their scheme resists on various security attacks and satisfies basic security requirements. However, we have discovered that Jung et al.’s scheme possesses some security weaknesses. Their scheme cannot guarantee security of the secret key of gateway node and security of session key and protection against user tracking attack, information leakage attack, and user impersonation attack. In this paper, we describe how those security weaknesses occur and propose a lightweight three-factor authentication and key agreement scheme in WSNs for smart homes, as an improved version of Jung et al.’s scheme. We then present a detailed analysis of the security and performance of the proposed scheme and compare the analysis results with other related schemes.

Limited capabilities of tiny sensor node make it difficult to build a secure wireless sensor network. To cope with this problem, location-aware key predistribution schemes have been studied but without considering irregularity of real sensing fields. They were designed for ideal sensing fields divided into uniform regular polygons but false is the real sensing field, where we face irregular obstacles, undulations, and boundaries. In this paper, we tackle this problem from practical sense and first introduce the general deployment model, comparable to the previous uniform deployment model. To construct a secure WSN in the general deployment model, we present a new framework called G en D ep , which consists of two phases: group placement and key management. In the group placement phase, we find an optimal position for sensor groups so that both connectivity and global coverage are satisfied for secure communications. In the key management phase, we devise a new structure called extended groups to partition the whole deployment area into irregular subareas. G en D ep can build secure and flexible WSNs by using the existing key predistribution schemes even designed in the uniform deployment model. To ensure practicality of GenDep, we also conduct rigorous analysis and simulation in this paper.

Rapid advances in modern computing and information technology have enabled millions of people to interact online via various social network and gaming services. The widespread adoption of such online services have made possible analysis of large-scale archival data containing detailed human interactions, presenting a very promising opportunity to understand the rich and complex human behavior. In collaboration with a leading global provider of Massively Multiplayer Online Role-Playing Games (MMORPGs), here we present a network science-based analysis of the interplay between distinct types of user interaction networks in the virtual world. We find that their properties depend critically on the nature of the context-interdependence of the interactions, highlighting the complex and multilayered nature of human interactions, a robust understanding of which we believe may prove instrumental in the designing of more realistic future virtual arenas as well as provide novel insights to the science of collective human behavior.

In the environments where the source nodes are close to one another and generate a lot of sensory data traffic with redundancy, transmitting all sensory data by individual nodes not only wastes the scarce wireless bandwidth, but also consumes a lot of battery energy. Instead of each source node sending sensory data to its sink for aggregation (the so-called client/server computing), Qi et al. in 2003 proposed a mobile agent (MA)-based distributed sensor network (MADSN) for collaborative signal and information processing, which considerably reduces the sensory data traffic and query latency as well. However, MADSN is based on the assumption that the operation of mobile agent is only carried out within one hop in a clustering-based architecture. This paper considers MA in multihop environments and adopts directed diffusion (DD) to dispatch MA. The gradient in DD gives a hint to efficiently forward the MA among target sensors. The mobile agent paradigm in combination with the DD framework is dubbed mobile agent-based directed diffusion (MADD). With appropriate parameters set, extensive simulation shows that MADD exhibits better performance than original DD (in the client/server paradigm) in terms of packet delivery ratio, energy consumption, and end-to-end delivery latency.