Conventional Attacks Research Articles

Dying to bankrupt: the effect of suicide terrorism on foreign direct investment

ABSTRACT Why do terrorist groups perpetrate suicide attacks? Political scientists maintain that terrorist groups adopt suicide terrorism for its strategic benefits. Yet previous studies have not demonstrated that suicide terrorism outperforms conventional terrorism. In fact, the most prominent studies do not even systematically compare the strategic effects of suicide terrorism to conventional terrorism to justify the cost. To date, the presumed strategic utility of suicide terrorism remains elusive. Although economists have assessed the impact of terrorism broadly on various economic measures, our study is the first to rigorously compare the effects of suicide terrorism to conventional terrorism on foreign direct investment. Consistent with bargaining theory, we find robust evidence that suicide terrorism “works” by depressing more than conventional attacks FDI in the target country. This effect is strong across PCSE model specifications, particularly in target countries weaker at counterterrorism. The study has important implications for both terrorism studies and international business.

NHAPMAD: Novel hybrid approaches for privacy‐preserved multiple attacks detection

SummaryDetecting anomalies is crucial for maintaining security in Wireless Sensor Networks (WSNs), as they are susceptible to various attacks that compromise nodes and yield inaccurate outcomes. Conventional attack detection approaches face challenges like high false positives, vulnerability to complex attacks, and limited adaptability to changing attacks due to predefined patterns. Additionally, the computational strain on resource‐constrained nodes hampers network efficiency, demanding innovative and resilient security solutions. Therefore, this research work introduces Novel Hybrid Approaches for Privacy‐preserved Multiple Attacks Detection (NHAPMAD) framework in both network attack detection system and host attack detection system. This topology bolsters the network's defense against various types of attacks, thereby ensuring the preservation of sensitive data privacy. Federated learning has been incorporated into the NHAPMAD framework to protect user privacy in WSNs. The introduced framework provides better anomaly detection and reduces false alarms. Additionally, the introduced model protects sensitive data, ensuring the security and integrity of the entire WSN, promoting a stable and dependable operating environment. The introduced method exhibits superior performance across various metrics like overall accuracy (99.6%), F1‐score (99.59%), recall (99.47%), precision (99.63%), detection rate (99.5%), processing time (0.0009 s), etc. compared to traditional approaches, marking a significant advancement in the realm of WSN security.

IDSUML: Intrusion Detection System Using Machine Learning

Abstract: Network and system security is essential to modern digital communication. There will be multiple successful attempts by hackers and other intruders to obtain illegal access to networks and internet services. The development of secure systems is continuously accompanied by new dangers and the corresponding responses. Using intrusion detection systems (IDS) is one choice. An intrusion detection system. Main objective is to safeguard its resources from possible dangers. These acts are categorized as either potential or conventional attacks since it is capable of analysing and forecasting user behaviour. To find network intrusions, we typically employ Support Vector Machines (SVM) and Rough Pure Mathematics (RST). RST is used to preprocess and decrease the amount of data when packets are retrieved from the network. The RST-selected options provided to the SVM model enable it to provide many views and an explanation. A good application of the approach is to reduce the density of knowledge in a certain field. By comparing the data with Principal element analysis, the experiments show how RST and SVM schema may lower the false positive rate and increase accuracy (PCA)

Toward Next Generation Quantum-Safe eIDs and eMRTDs: A Survey

Security mechanisms of Electronic Personal Documents (eCards) depend on (asymmetric) cryptography that is and always has been subject to the threat of compromise, be it from conventional attacks or quantum computers. With Post-Quantum Cryptography (PQC), we now have alternative building blocks at hand that can be leveraged to protect against both kind of attacks. Thus, PQC should be incorporated into eCard ecosystems, yet it is not clear how this is done best. In the work at hand, we review the state of currently used crypto-systems for eCard security, as well as their possible quantum-secure replacements. Further, we identify and categorize respective challenges that need to be addressed, present and assess existing approaches for their solution, and formulate research questions for open issues. By providing an overview of the situation, we help unraveling the issue and pave the way toward quantum-safe electronic Identity Documents and electronic Machine-Readable Travel Documents.

A Strategic Path for Digital Transformation in Cyber Warfare for African Militaries

Digital disruption has changed the battlefield and increased its complexity for the war fighter. The modern battlefield continues to increase this complexity, due to the evolution of components that constitute military capability. The technologies, processes and the users are such components. The modern battlefield relies on advanced technologies tapping on high connectivity, are more lethal, precise, and autonomous. Due to this evolution, areas once thought to be safe from conventional attacks are increasingly becoming vulnerable. This evolution of technology and shorter development curves have also increased the prominence of the cyberspace, as a domain of war. However, many militaries, especially in Africa are still operating legacy systems and struggling with modernizing their systems to take advantage of the digital evolution. This paper, therefore, uses a systematic literature review and benchmarking focusing on selected super cyber power nations’ indices to propose a strategic path for African militaries to drive digital transformation in their operational environments. The roadmap is proposed to stimulate the establishment and enhancement of African militaries’ cyber warfighting capabilities in the digital age. The objectives of this digital transformation path include establishing a digital backbone, where all the sensors, effectors and the deciders are plugged to share information and intelligence.

Robust zero‐watermarking algorithm based on discrete wavelet transform and daisy descriptors for encrypted medical image

AbstractIn the intricate network environment, the secure transmission of medical images faces challenges such as information leakage and malicious tampering, significantly impacting the accuracy of disease diagnoses by medical professionals. To address this problem, the authors propose a robust feature watermarking algorithm for encrypted medical images based on multi‐stage discrete wavelet transform (DWT), Daisy descriptor, and discrete cosine transform (DCT). The algorithm initially encrypts the original medical image through DWT‐DCT and Logistic mapping. Subsequently, a 3‐stage DWT transformation is applied to the encrypted medical image, with the centre point of the LL3 sub‐band within its low‐frequency component serving as the sampling point. The Daisy descriptor matrix for this point is then computed. Finally, a DCT transformation is performed on the Daisy descriptor matrix, and the low‐frequency portion is processed using the perceptual hashing algorithm to generate a 32‐bit binary feature vector for the medical image. This scheme utilises cryptographic knowledge and zero‐watermarking technique to embed watermarks without modifying medical images and can extract the watermark from test images without the original image, which meets the basic requirements of medical image watermarking. The embedding and extraction of watermarks are accomplished in a mere 0.160 and 0.411s, respectively, with minimal computational overhead. Simulation results demonstrate the robustness of the algorithm against both conventional attacks and geometric attacks, with a notable performance in resisting rotation attacks.

Generating Semantic Adversarial Examples via Feature Manipulation in Latent Space.

The susceptibility of deep neural networks (DNNs) to adversarial intrusions, exemplified by adversarial examples, is well-documented. Conventional attacks implement unstructured, pixel-wise perturbations to mislead classifiers, which often results in a noticeable departure from natural samples and lacks human-perceptible interpretability. In this work, we present an adversarial attack strategy that implements fine-granularity, semantic-meaning-oriented structural perturbations. Our proposed methodology manipulates the semantic attributes of images through the use of disentangled latent codes. We engineer adversarial perturbations by manipulating either a single latent code or a combination thereof. To this end, we propose two unsupervised semantic manipulation strategies: one based on vector-disentangled representation and the other on feature map-disentangled representation, taking into consideration the complexity of the latent codes and the smoothness of the reconstructed images. Our empirical evaluations, conducted extensively on real-world image data, showcase the potency of our attacks, particularly against black-box classifiers. Furthermore, we establish the existence of a universal semantic adversarial example that is agnostic to specific images.

Stealthy attack detection based on controlled invariant subspace for autonomous vehicles

Autonomous driving offers a convenient and comfortable means of transportation, while the addition of external communication interfaces renders the vehicle communication more vulnerable to cyber attacks. In contrast to conventional attacks, well-designed stealthy attacks are more adept at evading conventional detectors. To address this problem, this paper proposes a robust detector based on controlled invariant subspace to identify stealthy attacks. This paper stands out not only by relying on the vehicle dynamics model for system state estimation, but rather by employing a real-time trajectory space generation method. Our detector develops a rapidly expanding random tree method based on offline incremental learning to generate trajectory subspace, and any trajectories that diverge from this subspace are identified as anomalies. Additionally, this paper designs distance measurement method that combines the linear quadratic regulation algorithm with a radial basis function neural network while also taking into account the vehicle's dynamic performance constraints. To assess the theoretical performance of the proposed detector, this paper presents rigorous proofs that demonstrate the asymptotical optimality of the generated trajectory and the robustness of the stealthy attack detection method based on controlled invariant subspace. Finally, simulation results demonstrate that the proposed robust detector can effectively identify stealthy attacks and outperforms advanced techniques in terms of accuracy and false-positive rate.

Robust Reversible Watermarking by Fractional Order Zernike Moments and Pseudo-Zernike Moments

Robust reversible watermarking (RRW) is one of the most popular areas in information hiding. Existing schemes have two drawbacks: 1) schemes that can resist conventional attacks often fail to resist geometric attacks, and 2) schemes that can resist geometric attacks often are not robust against conventional attacks and have poor stability. Inspired by the high robustness of fractional-order orthogonal moments (FoOM) and the good feature of resistance to geometric attacks of Zernike moments and pseudo-Zernike moments (ZM/PZM), in this research, FoOM is used to optimize ZM/PZM to obtain FoZM/FoPZM (namely, fractional-order Zernike moments and fractional-order pseudo-Zernike moments). Furthermore, a denoiser is proposed to preprocess the watermarks to improve the robustness against geometric and conventional attacks, the amount of extracted auxiliary information is decreases and the extraction process of auxiliary information is designed to be more stable. Specifically, first, the source of the difference between the watermarked image and the carrier image is identified, that difference is represented with less information, and then that information is embedded into the cover image as auxiliary information. Second, the watermark is embedded in the low-order FoZM/FoPZM component. Finally, the watermarked image is denoised using a denoiser before extracting the watermark. The experimental results show that the scheme has good stability and strong robustness. Compared with existing methods, the proposed scheme has a small and stable auxiliary information size, strong robustness to noise attacks such as Gaussian noise and salt-and-pepper noise attacks, and better resistance to geometric attacks such as rotation and scaling attacks.

Eclipsing Security: An In-Depth Analysis of Advanced Persistent Threats

Abstract—Advanced Persistent Threats (APTs) pose a significant challenge in today's cybersecurity landscape due to their persistent, targeted, and secretive nature. In today's highly connected digital world, it's crucial to understand and tackle the risks linked to Advanced Persistent Threats (APTs). In this research paper, titled "Eclipsing Security: An In-Depth Analysis of Advanced Persistent Threats," we comprehensively explore the lifecycle, attack methodology, and practical aspects of APTs. Our focus extends to understanding how these threats gain access, the key tactics employed, and the technological aspects involved. Additionally, we also delve into how APTs differ from conventional attacks, emphasizing their unique characteristics that distinguish them from other cyber threats. Our goal is to provide valuable insights to the cybersecurity community, offering practical strategies to strengthen defenses against these elusive and enduring threats. Keywords—Advanced Persistent Threats (APTs), Cybersecurity, Lifecycle Analysis, Attack Methodology, Practical Implementation, Legal and Regulatory Frameworks, Case Studies, Mitigation and Defense Strengthening.

Semi-quantum dialogue scheme based on hyperentangled Bell states

Quantum dialogue enables two communicators to send their own secret information to each other, but most of the current quantum dialogue protocols have high requirements for the hardware equipment and capabilities of the communicators. In order to reduce the cost of participant, a semi-quantum dialogue scheme based on hyperentangled Bell states is studied in this paper. In our protocol, we combines the idea of semi-quantum key distribution and semi-quantum secure direct communication to realize the exchange of secret information between Alice and Bob, Alice’s information is transmitted to Bob through semi-quantum secure direct communication, while Bob’s message is extracted by Alice through semi-quantum key distribution and one-time pad encryption. This is different from the previous semi-quantum dialogue protocols, and is a new way of thinking. The security analysis shows that our protocol can not only resist the conventional attack strategies, but also has not the drawback of ‘information leakage’ problem. Compared with the existing semi-quantum dialogue protocols, it has some advantages, such as high communication efficiency, saving classical participant’s consumption and security. Although this protocol requires the preparation and analysis of hyperentangled Bell state, which are slightly more complex, it will be shown that all these are achievable by current technology. Our protocol is fully implementable, so it has good application prospects.

The Fortification Dilemma: Border Control and Rebel Violence

AbstractWhere cross‐border sanctuaries enable rebels to marshal external support, classical theories of counterinsurgency extol the strategic value of border fortification. By sealing borders, counterinsurgents can erode transnational militants’ resources, degrading the quality of rebellion. Extending resource‐centric theories of conflict, I posit a fortification dilemma inherent in this strategy. Externally supplied rebels can afford conventional attacks and civilian victimization. When border fortifications interdict their foreign logistics, insurgents compensate by cultivating greater local support. In turn, rebels prefer more irregular attacks and cooperative relations with civilians. Hence, counterinsurgent border fortification trades off reduced rebel capabilities for greater competition over local hearts and minds. I test this theory using declassified microdata on border fortification and violence in Iraq. Results highlight the central link between border control and cross‐border militancy, and show how governments can contest the transnational dimensions of civil wars, such as external rebel sponsorship.

Detection of Unknown DDoS Attack Using Convolutional Neural Networks Featuring Geometrical Metric

DDoS attacks remain a persistent cybersecurity threat, blocking services to legitimate users and causing significant damage to reputation, finances, and potential customers. For the detection of DDoS attacks, machine learning techniques such as supervised learning have been extensively employed, but their effectiveness declines when the framework confronts patterns exterior to the dataset. In addition, DDoS attack schemes continue to improve, rendering conventional data model-based training ineffectual. We have developed a novelty open-set recognition framework for DDoS attack detection to overcome the challenges of traditional methods. Our framework is built on a Convolutional Neural Network (CNN) construction featuring geometrical metric (CNN-Geo), which utilizes deep learning techniques to enhance accuracy. In addition, we have integrated an incremental learning module that can efficiently incorporate novel unknown traffic identified by telecommunication experts through the monitoring process. This unique approach provides an effective solution for identifying and alleviating DDoS. The module continuously improves the model’s performance by incorporating new knowledge and adapting to new attack patterns. The proposed model can detect unknown DDoS attacks with a detection rate of over 99% on conventional attacks from CICIDS2017. The model’s accuracy is further enhanced by 99.8% toward unknown attacks with the open datasets CICDDoS2019.

Variation Enhanced Attacks Against RRAM-Based Neuromorphic Computing System

The RRAM-based neuromorphic computing system has amassed explosive interests for its superior data processing capability and energy efficiency than traditional architectures, and thus being widely used in many data-centric applications. The reliability and security issues of the NCS therefore become an essential problem. In this paper, we systematically investigated the adversarial threats to the RRAM-based NCS and observed that the RRAM hardware feature can be leveraged to strengthen the attack effect, which has not been granted sufficient attention by previous algorithmic attack methods. Thus, we proposed two types of hardware-aware attack methods with respect to different attack scenarios and objectives. The first is adversarial attack, VADER, which perturbs the input samples to mislead the prediction of neural networks. The second is fault injection attack, EFI, which perturbs the network parameter space such that a specified sample will be classified to a target label, while maintaining the prediction accuracy on other samples. Both attack methods leverage the RRAM properties to improve the performance compared with the conventional attack methods. Experimental results show that our hardware-aware attack methods can achieve nearly 100% attack success rate with extremely low operational cost, while maintaining the attack stealthiness.

From Attack to Identification: MEMS Sensor Fingerprinting Using Acoustic Signals

A device pretending to be behaving normally can carry out malicious acts that result in the leakage of sensitive information, invasion of privacy, damage property, or even loss of life. The first step to detect these malicious acts is to identify whether a device is authorized or not. There are existing methods for identifying devices based on cryptographic schemes or physical unclonable function (PUF), but each of these methods has disadvantages in that key management costs are incurred or additional chips or circuits are required. In this article, we propose a device identification method that overcomes these shortcomings. Based on the fact that MEMS sensors are built-in to most devices due to their various applications and the property that MEMS sensors are sensitive to acoustic signals with resonant frequencies, the proposed method uses MEMS sensor readings to acoustic signals. Many attack methods have been studied using this property to disable the normal function of the sensor or eavesdrop on speech around a device. On the other hand, our method is the first to fingerprint MEMS sensors using raw sensor readings to acoustic signals from a security point of view rather than from this conventional attack point of view. Furthermore, our method is based on a challenge-response structure to be secure from replay attacks; here, a challenge is a random acoustic signal, and a response is the unique MEMS sensor reading to the challenge. In our evaluation, we use commercial MEMS sensors and a low-cost speaker that costs less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula> 1. The results show that under various conditions that may affect MEMS sensor readings, the macro F1-scores when identifying authorized devices is 1.0, and the macro accuracy of detecting attacks using devices other than authorized ones is 0.994. As a result, our method can identify devices well at a low cost.

A novel spectrogram visual security encryption algorithm based on block compressed sensing and five-dimensional chaotic system

Based on block compressed sensing theory, combined with a five-dimensional chaotic system, we propose and analyze a novel spectrogram visual security encryption algorithm. This research is devoted to solving the compression, encryption and steganography problems of spectrograms involving large data volumes and high complexity. First, the discrete wavelet transform is applied to the spectrogram to generate the coefficient matrix. Then, block compressed sensing is applied to compress and preencrypt the spectrogram. Second, we design a new five-dimensional chaotic system. Then, several typical evaluation methods, such as the phase diagram, Lyapunov exponent, bifurcation diagram and sample entropy, are applied to deeply analyze the chaotic behavior and dynamic performance of the system. Moreover, the corresponding Simulink model has been built, which proves the realizability of the chaotic system. Importantly, the measurement matrix required for compressed sensing is constructed by the chaotic sequence. Third, dynamic Josephus scrambling and annular diffusion are performed on the secret image to obtain the cipher image. Finally, an improved least significant bit embedding method and alpha channel synchronous embedding are designed to obtain a steganographic image with visual security properties. To make the initial keys of each image completely different from other images, the required keys are produced using the SHA-256 algorithm. The experimental results confirm that the visual security cryptosystem designed in this study has better compression performance, visual security and reconstruction quality. Furthermore, it is able to effectively defend against a variety of conventional attack methods, such as statistical attacks and entropy attacks.

SQLite Encryption Method for Embedded Databases Based on Chaos Algorithm

With the widespread use of embedded systems, chaos is a nonlinear system with certainty and complexity. It is an important topic in the field of information security at present, and it is an effective way to apply to embedded systems. It has great practical value in theory and in practice. This research mainly focuses on the encryption technology of SQLite embedded database and proposes an improved sparrow algorithm (Logistic Chaos Sparrow Search Algorithm, LCSSA) based on Logistic Chaos Map. It shows that the security level of SQLite in web development is higher than that of conventional Access. The population is initialized by the logistic chaotic mapping method, which improves the quality of the initial solution, increases the diversity of the population, and reduces the risk of premature maturity of the algorithm. The initial value y 0 determines the encryption method of the nonlinear function. Taking the integer variable (int) as an example, the value range is -231~231. It can be seen that the key space is sufficient to prevent various conventional attacks. When the key is the wrong key, decryption will not yield any data. It can be found that encryption and decryption are very sensitive to the key, which is also determined by the sensitivity of chaotic encryption system to the initial value. The benchmark function compares the performance of the improved algorithm with the algorithm before the improvement and compares it with the SSA. The LCSSA has better convergence performance, higher accuracy, and better stability.

Application of Robust Zero-Watermarking Scheme Based on Federated Learning for Securing the Healthcare Data.

The privacy protection and data security problems existing in the healthcare framework based on the Internet of Medical Things (IoMT) have always attracted much attention and need to be solved urgently. In the teledermatology healthcare framework, the smartphone can acquire dermatology medical images for remote diagnosis. The dermatology medical image is vulnerable to attacks during transmission, resulting in malicious tampering or privacy data disclosure. Therefore, there is an urgent need for a watermarking scheme that doesn't tamper with the dermatology medical image and doesn't disclose the dermatology healthcare data. Federated learning is a distributed machine learning framework with privacy protection and secure encryption technology. Therefore, this paper presents a robust zero-watermarking scheme based on federated learning to solve the privacy and security issues of the teledermatology healthcare framework. This scheme trains the sparse autoencoder network by federated learning. The trained sparse autoencoder network is applied to extract image features from the dermatology medical image. Image features are undergone to two-dimensional Discrete Cosine Transform (2D-DCT) in order to select low-frequency transform coefficients for creating zero-watermarking. Experimental results show that the proposed scheme has more robustness to the conventional attack and geometric attack and achieves superior performance when compared with other zero-watermarking schemes. The proposed scheme is suitable for the specific requirements of medical images, which neither changes the important information contained in medical images nor divulges privacy data.

New medications to mitigate attacks of hereditary angioedema: does one size fit all?

Hereditary angioedema has received renewed attention in the past two decades, with the elucidation of the biochemical pathways resulting in bradykinin overproduction in patients with congenital C1esterase inhibitor (C1INH) deficiency, leading to recurrent attacks of tissue swelling due to uncontrolled vascular permeability. 1 Busse PJ Christiansen SC Hereditary angioedema. N Engl J Med. 2020; 382: 1136-1148 Crossref PubMed Scopus (132) Google Scholar Oedema attacks affecting the lingual or laryngeal area can become lethal as a result of asphyxiation. 2 Minafra FG Gonçalves TR Alves TM Pinto JA The mortality from hereditary angioedema worldwide: a review of the real-world data literature. Clin Rev Allergy Immunol. 2022; 62: 232-239 Crossref PubMed Scopus (4) Google Scholar Several new treatments have emerged, 143 years after Quincke's first description of hereditary angioedema and 15 years after the introduction of the first specific bradykinin B2 receptor inhibitor (icatibant). These drugs specifically target crucial steps in the kallikrein–kinin (contact) cascade downstream of bradykinin production. 3 Fijen LM Bork K Cohn DM Current and prospective targets of pharmacologic treatment of hereditary angioedema types 1 and 2. Clin Rev Allergy Immunol. 2021; 61: 66-76 Crossref PubMed Scopus (0) Google Scholar Many of these agents have shown their efficacy and safety in acute (on-demand) treatment and prophylaxis of hereditary angioedema attacks, saving lives and revolutionising patients’ quality of life. 4 Lumry WR Settipane RA Hereditary angioedema: epidemiology and burden of disease. Allergy Asthma Proc. 2020; 41: S08-S Crossref Google Scholar These modalities fit well into the modern paradigm in hereditary angioedema treatment, supporting patients’ autonomy, recommending self-administration, and reducing reliance on hospital emergency rooms, health-care providers, and medical points of care. 5 Maurer M Magerl M Betschel S et al. The international WAO/EAACI guideline for the management of hereditary angioedema—the 2021 revision and update. Allergy. 2022; 77: 1961-1990 Crossref PubMed Scopus (32) Google Scholar An investigational oral plasma kallikrein inhibitor for on-demand treatment of hereditary angioedema: a two-part, randomised, double-blind, placebo-controlled, crossover phase 2 trialOral administration of sebetralstat was well tolerated and led to rapid suppression of plasma kallikrein activity, resulting in increased time to use of conventional attack treatment and faster symptom relief versus placebo. Based on these results, a phase 3 trial to evaluate the efficacy and safety of two dose levels of sebetralstat in adolescent and adult participants with hereditary angioedema has been initiated ( NCT05259917 ). Full-Text PDF

An investigational oral plasma kallikrein inhibitor for on-demand treatment of hereditary angioedema: a two-part, randomised, double-blind, placebo-controlled, crossover phase 2 trial

Guidelines recommend effective on-demand therapy for all individuals with hereditary angioedema. We aimed to assess the novel oral plasma kallikrein inhibitor, sebetralstat, which is in development, for on-demand treatment of hereditary angioedema attacks. In this two-part phase 2 trial, individuals with type 1 or 2 hereditary angioedema aged 18 years or older were recruited from 25 sites, consisting of specialty outpatient centres, across nine countries in Europe and the USA. Individuals were eligible if they had experienced at least three hereditary angioedema attacks in the past 93 days, were not on prophylactic therapy, and had access to and the ability to self-administer conventional attack treatment. In part 1 of the trial, participants were given a single 600 mg open-label oral dose of sebetralstat to assess safety, pharmacokinetics, and pharmacodynamics of the dose. Part 2 was a randomised, double-blind, placebo-controlled, two-sequence, two-period (2 × 2) crossover trial; participants were randomly assigned (1:1) to either sequence 1, in which they were given a single dose of 600 mg of sebetralstat to treat the first eligible attack and a second dose of placebo to treat the second eligible attack, or sequence 2, in which they were given placebo to treat the first eligible attack and then 600 mg of sebetralstat to treat the second eligible attack. Participants and investigators were masked to treatment assignment. The primary endpoint was time to use of conventional attack treatment within 12 h of study drug administration, which was assessed in all participants who were randomly assigned to treatment and who received study drug for two attacks during part 2 of the study. Safety was assessed in all participants who received at least one dose of study drug, starting in part 1. This study is registered with ClinicalTrials.gov, NCT04208412, and is completed. Between July 2, 2019, and Dec 8, 2020, 84 individuals were screened and 68 were enrolled in part 1 and received sebetralstat (mean age 38·3 years [SD 13·2], 37 [54%] were female, 31 [46%] were male, 68 [100%] were White). 42 (62%) of 68 participants completed pharmacokinetic assessments. Sebetralstat was rapidly absorbed, with a geometric mean plasma concentration of 501 ng/mL at 15 min. In a subset of participants (n=6), plasma samples obtained from 15 min to 4 h after study drug administration had near-complete protection from ex vivo stimulated generation of plasma kallikrein and cleavage of high-molecular-weight kininogen. In part 2, all 68 participants were randomly assigned to sequence 1 (n=34) or sequence 2 (n=34). 53 (78%) of 68 participants treated two attacks (25 [74%] in the sequence 1 group and 28 [82%] in the sequence 2 group). Time to use of conventional treatment within 12 h of study drug administration was significantly longer with sebetralstat versus placebo (at quartile 1: >12 h [95% CI 9·6 to >12] vs 8·0 h [3·8 to >12]; p=0·0010). There were no serious adverse events or adverse event-related discontinuations. Oral administration of sebetralstat was well tolerated and led to rapid suppression of plasma kallikrein activity, resulting in increased time to use of conventional attack treatment and faster symptom relief versus placebo. Based on these results, a phase 3 trial to evaluate the efficacy and safety of two dose levels of sebetralstat in adolescent and adult participants with hereditary angioedema has been initiated (NCT05259917). KalVista Pharmaceuticals.