Search Results (933)

Software attack surfaces define the external boundaries—entry points, communication channels, and sensitive data stores through which adversaries may compromise a system. This paper introduces a scoring mechanism that produces a normalized attack-surface metric in the range of 0–1. Building on the established Damage-Potential-to-Effort ratio, our approach further incorporates real-world vulnerability intelligence drawn from MITRE’s CVE and CWE repositories. We compute each application’s score by ingesting preliminary findings from a static-analysis tool and processing them through our unified model. To assess effectiveness, we validate the scoring system across a spectrum of scenarios, from a simple Java application to complex enterprise applications. The resulting metric offers development and security teams a concise, objective measure to monitor an application’s attack surface and hence proactively identify vulnerabilities in their applications. This tool can also be used to benchmark various third-party or dependent applications, enabling both developers and security practitioners to better manage risk. Full article

Smart farming is an agricultural technology integrating advanced technology such as cloud computing, Artificial Intelligence (AI), the Internet of Things (IoT), and robots into traditional farming. Smart farming can help farmers by increasing agricultural production and managing resources efficiently. However, malicious attackers can attempt security attacks because communication in smart farming is conducted via public channels. Therefore, an authentication scheme is necessary to ensure security in smart farming. In 2024, Rahaman et al. proposed a privacy-centric authentication scheme for smart farm monitoring. However, we demonstrated that their scheme is vulnerable to stolen mobile device, impersonation, and ephemeral secret leakage attacks. This paper suggests a secure and privacy-preserving scheme to resolve the security defects of the scheme proposed by Rahaman et al. We also verified the security of our scheme through “the Burrows-Abadi-Needham (BAN) logic”, “Real-or-Random (RoR) model”, and “Automated Validation of Internet Security Protocols and Application (AVISPA) tool”. Furthermore, a performance analysis of the proposed scheme compared with related studies was conducted. The comparison result proves that our scheme was more efficient and secure than related studies in the smart farming environment. Full article

Cyber threat intelligence (CTI) has become critical in enhancing cybersecurity measures across various sectors. This systematic review aims to synthesize the current literature on the effectiveness of CTI strategies in mitigating cyber attacks, identify the most effective tools and methodologies for threat detection and prevention, and highlight the limitations of current approaches. An extensive search of academic databases was conducted following the PRISMA guidelines, including 43 relevant studies. This number reflects a rigorous selection process based on defined inclusion, exclusion, and quality criteria and is consistent with the scope of similar systematic reviews in the field of cyber threat intelligence. This review concludes that while CTI significantly improves the ability to predict and prevent cyber threats, challenges such as data standardization, privacy concerns, and trust between organizations persist. It also underscores the necessity of continuously improving CTI practices by leveraging the integration of advanced technologies and creating enhanced collaboration frameworks. These advancements are essential for developing a robust and adaptive cybersecurity posture capable of responding to an evolving threat landscape, ultimately contributing to a more secure digital environment for all sectors. Overall, the review provides practical reflections on the current state of CTI and suggests future research directions to strengthen and improve CTI’s effectiveness. Full article

Digitalisation can positively impact the efficiency of real-world business processes, but may also introduce new cybersecurity challenges. One area that is particularly vulnerable to cyber-attacks is the business logic embedded in processes in which flaws may exist. This is especially the case when these processes are within web-based applications and services, which is increasingly becoming the norm for many organisations. Business logic vulnerabilities (BLVs) can emerge following the software development process, which may be difficult to detect by vulnerability detection tools. Through a systematic literature review and interviews with industry practitioners, this study identifies key BLV types and the challenges in detecting them. The paper proposes an eight-stage operational framework that leverages Artificial Intelligence (AI) for enhanced BLV detection and mitigation. The research findings contribute to the rapidly evolving theory and practice in this field of study, highlighting the current reliance on manual detection, the contextual nature of BLVs, and the need for a hybrid, multi-layered approach integrating human expertise with AI tools. The study concludes by emphasizing AI’s potential to transform cybersecurity from a reactive to a proactive defense against evolving vulnerabilities and threats. Full article

Phishing attacks continue to pose serious challenges to cybersecurity, with attackers constantly refining their methods to bypass detection systems. One particularly evasive technique is Zero-Font phishing, which involves the insertion of invisible or zero-sized characters into email content to deceive both users and traditional email filters. Because these characters are not visible to human readers but still processed by email systems, they can be used to evade detection by traditional email filters, obscuring malicious intent in ways that bypass basic content inspection. This study introduces a proactive phishing detection tool capable of identifying both traditional and Zero-Font phishing attempts. The proposed tool leverages a multi-layered security framework, combining structural inspection and machine learning-based classification to detect both traditional and Zero-Font phishing attempts. At its core, the system incorporates an advanced machine learning model trained on a well-established dataset comprising both phishing and legitimate emails. The model alone achieves an accuracy rate of up to 98.8%, contributing significantly to the overall effectiveness of the tool. This hybrid approach enhances the system’s robustness and detection accuracy across diverse phishing scenarios. The findings underscore the importance of multi-faceted detection mechanisms and contribute to the development of more resilient defenses in the ever-evolving landscape of cybersecurity threats. Full article

As the world accelerates toward a sustainable future with electric vehicles (EVs), smartphone applications have become an indispensable tool for drivers. These applications, developed by both EV manufacturers and third-party developers, offer functionalities such as remote vehicle control, charging station location, and route planning. However, they also have access to sensitive information, making them potential targets for cyber threats. This paper presents a comprehensive survey of the cybersecurity vulnerabilities, weaknesses, and permissions in these applications. We categorize 20 applications into two groups: those developed by EV manufacturers and those by third parties, and conduct a comparative analysis of their functionalities by performing static and dynamic analysis. Our findings reveal major security flaws such as poor authentication, broken encryption, and insecure communication, among others. The paper also discusses the implications of these vulnerabilities and the risks they pose to users. Furthermore, we analyze 10 permissions and 12 functionalities that are not present in official EV applications and mostly present in third-party apps, leading users to rely on poorly built third-party applications, thereby increasing their attack surface. To address these issues, we propose defensive measures which include 10 CWE AND OWASP top 10 defenses to enhance the security of these applications, ensuring a safe and secure transition to EVs. Full article

Background: Transcranial direct current stimulation (tDCS) is a non-invasive neuromodulation technique for migraine prevention. This study evaluates the efficacy of tDCS compared to sham in preventing episodic migraine in adults. Methods: PubMed and Embase databases were searched until May 2025 to identify randomized controlled trials comparing tDCS with sham for the prevention of episodic migraine in adults. Risk of bias in the included trials was assessed using the Cochrane Risk of Bias Tool version 2. A random effect meta-analysis was conducted to evaluate the effects of cathodal and anodal tDCS on migraine frequency (days per month and attacks per month). Results: The meta-analysis included six trials with 172 participants (mean age 34 years, 82% females). Both cathodal (three studies, over the occipital area) and anodal (three studies, over the occipital or primary motor area) tDCS reduced the mean number of monthly migraine days and migraine attacks compared to sham. After pooling the outcomes and excluding two studies at high risk of bias, anodal tDCS over the occipital or primary motor area (standardized difference in means = −0.7, 95% CI: −1.7, 0.2, p = 0.124) and cathodal tDCS over the occipital area (standardized difference in means = −0.7, 95% CI: −1.1, −0.3, p = 0.000) reduced headache frequency compared to sham. However, the reduction with anodal tDCS was not statistically significant. Summary: tDCS may be effective in preventing episodic migraine. However, the evidence is limited by the small number of heterogeneous trials, with variation in electrode placement and stimulation intervals. Full article

The widespread adoption of web applications has led to a significant increase in the number of automated cyberattacks. Parameter tampering attacks pose a substantial security threat, enabling privilege escalation and unauthorized data exfiltration. Traditional pattern-based detection tools exhibit limited efficacy against such threats, as identical parameters may produce varying response patterns contingent on their processing context, including security filtering mechanisms. This study proposes a machine learning-based detection model to address these limitations by identifying parameter tampering vulnerabilities through a contextual analysis. The training dataset aggregates real-world vulnerability cases collected from web crawls, public vulnerability databases, and penetration testing reports. The Synthetic Minority Over-sampling Technique (SMOTE) was employed to address the data imbalance during training. Recall was adopted as the primary evaluation metric to prioritize the detection of true vulnerabilities. Comparative analysis showed that the XGBoost model demonstrated superior performance and was selected as the detection model. Validation was performed using web URLs with known parameter tampering vulnerabilities, achieving a detection rate of 73.3%, outperforming existing open-source automated tools. The proposed model enhances vulnerability detection by incorporating semantic representations of parameters and their values using BERT embeddings, enabling the system to learn contextual characteristics beyond the capabilities of pattern-based methods. These findings suggest the potential of the proposed method for scalable, efficient, and automated security diagnostics in large-scale web environments. Full article

Background: Acute heart failure (AHF) is a major cause of hospitalizations, posing significant challenges to healthcare systems. Despite advancements in management, the rate of poor outcomes remains high globally, emphasizing the need for timely interventions. This study aimed to identify early admission-based factors predictive of poor outcomes in hospitalized AHF patients, in order to contribute to early risk stratification and optimize patient care. Methods: This retrospective single-center study analyzed routine data of adult patients hospitalized for AHF at a public university teaching hospital in Switzerland. Outcomes included in-hospital death, intensive care (ICU) treatment, and length of hospital stay (LOHS). Potential predictors were limited to routine parameters, readily available at admission. Missing predictor data was imputed and predictors were identified by means of multivariable regression analysis. Results: Data of 638 patients (median age 84 years, range 45–101 years, 50% female) were included in the study. In-hospital mortality was 7.1%, ICU admission rate 3.8%, and median LOHS was 8 days (IQR 5–12). Systolic blood pressure ≤ 100 mmHg (Odds ratio (OR) 3.8, p = 0.009), peripheral oxygen saturation ≤ 90% or oxygen supplementation (OR 5.9, p < 0.001), and peripheral edema (OR 2.7, p = 0.044) at hospital admission were identified as predictors of in-hospital death. Furthermore, a stroke or transient ischemic attack in the patient’s history (OR 3.2, p = 0.023) was associated with in-hospital death. ICU admission was associated with oxygen saturation ≤ 90% or oxygen supplementation (OR 22.9, p < 0.001). Factors linked to longer LOHS included oxygen saturation ≤ 90% or oxygen supplementation (IRR 1.2, p < 0.001), recent weight gain (IRR 1.1, p = 0.028), and concomitant chronic kidney disease (IRR 1.2, p < 0.001). Conclusions: This study validated established predictors of AHF outcomes in a Swiss cohort, highlighting the predictive value of poor perfusion status, fluid overload, and comorbidities such as chronic kidney disease. The identified predictors imply potential for developing tools to improve rapid treatment decisions. Future research should focus on the prospective external validation of the identified predictors and the design and validation of risk scores, incorporating these parameters to optimize early interventions and reduce adverse outcomes in AHF. Full article

In a biological nervous system, neurons are connected to each other via synapses to transmit information. Synaptic crosstalk is the phenomenon of mutual interference or interaction of neighboring synapses between neurons. This phenomenon is prevalent in biological neural networks and has an important impact on the function and information processing of the neural system. In order to simulate and study this phenomenon, this paper proposes a memristor model based on hyperbolic tangent function for simulating the activation function of neurons, and constructs a three-neuron HNN model by coupling two memristors, which brings it close to the real behavior of biological neural networks, and provides a new tool for studying complex neural dynamics. The intricate nonlinear dynamics of the MHNN are examined using techniques like Lyapunov exponent analysis and bifurcation diagrams. The viability of the MHNN is confirmed through both analog circuit simulation and FPGA implementation. Moreover, an image encryption approach based on the chaotic system and a dynamic key generation mechanism are presented, highlighting the potential of the MHNN for real-world applications. The histogram shows that the encryption algorithm is effective in destroying the features of the original image. According to the sensitivity analysis, the bit change rate of the key is close to 50% when small perturbations are applied to each of the three parameters of the system, indicating that the system is highly resistant to differential attacks. The findings indicate that the MHNN displays a wide range of dynamical behaviors and high sensitivity to initial conditions, making it well-suited for applications in neuromorphic computing and information security. Full article

The accelerated uptake of unmanned aerial vehicles (UAVs) has significantly altered communication and data exchange landscapes but has also introduced substantial security challenges, especially in open-access UAV communication environments. To address these, Elliptic curve cryptography (ECC) offers robust security with computational efficiency, ideal for resource-constrained Internet of Drones (IoD) systems. This study proposes a Secure and Efficient Three-Way Key Exchange (SETKE) protocol using ECC, specifically tailored for IoD. The SETKE protocol’s security was rigorously analyzed within an extended Bellare–Pointcheval–Rogaway (BPR) model under the random oracle assumption, demonstrating its resilience. Formal verification using the AVISPA tool confirmed the protocol’s safety against man-in-the-middle (MITM) attacks, and formal proofs establish its Authenticated Key Exchange (AKE) security. In terms of performance, SETKE is highly efficient, requiring only 3 ECC scalar multiplications for the Service Requester drone, 4 for the Service Provider drone, and 3 for the Control Server, which is demonstrably lower than several existing schemes. My approach achieves this robust protection with minimal communication overhead (e.g., a maximum payload of 844 bits per session), ensuring its practicality for resource-limited IoD environments. The significance of this work for the IoD field lies in providing a provably secure, lightweight, and computationally efficient key exchange mechanism vital for addressing critical security challenges in IoD systems. Full article

Malware remains a central tool in cyberattacks, and systematic research into adversarial attack techniques targeting malware is crucial in advancing detection and defense systems that can evolve over time. Although numerous review articles already exist in this area, there is still a lack of comprehensive exploration into emerging artificial intelligence technologies such as reinforcement learning from the attacker’s perspective. To address this gap, we propose a foundational reinforcement learning (RL)-based framework for adversarial malware generation and develop a systematic evaluation methodology to dissect the internal mechanisms of generative models across multiple key dimensions, including action space design, state space representation, and reward function construction. Drawing from a comprehensive review and synthesis of the existing literature, we identify several core findings. (1) The scale of the action space directly affects the model training efficiency. Meanwhile, factors such as the action diversity, operation determinism, execution order, and modification ratio indirectly influence the quality of the generated adversarial samples. (2) Comprehensive and sensitive state feature representations can compensate for the information loss caused by binary feedback from real-world detection engines, thereby enhancing both the effectiveness and stability of attacks. (3) A multi-dimensional reward signal effectively mitigates the policy fragility associated with single-metric rewards, improving the agent’s adaptability in complex environments. (4) While the current RL frameworks applied to malware generation exhibit diverse architectures, they share a common core: the modeling of discrete action spaces and continuous state spaces. In addition, this work explores future research directions in the area of adversarial malware generation and outlines the open challenges and critical issues faced by defenders in responding to such threats. Our goal is to provide both a theoretical foundation and practical guidance for building more robust and adaptive security detection mechanisms. Full article

Digital agriculture has rapidly developed in the last decade in many countries where the share of agricultural production is a significant part of the total volume of gross production. Digital agroecosystems are developed using a variety of IT solutions, software and hardware tools, wired and wireless data transmission technologies, open source code, Open API, etc. A special place in agroecosystems is occupied by electronic payment technologies and blockchain technologies, which allow farmers and other agricultural enterprises to conduct commodity and monetary transactions with suppliers, creditors, and buyers of products. Such ecosystems contribute to the sustainable development of agriculture, agricultural engineering, and management of production and financial operations in the agricultural industry and related industries, as well as in other sectors of the economy of a number of countries. The introduction of crypto solutions in the agricultural sector is designed to create integrated platforms aimed at helping farmers manage supply lines or gain access to financial services. At the same time, there are risks of illegal use of computing power for cryptocurrency mining—cryptojacking. This article offers a thorough risk assessment of cryptojacking attacks on endpoint systems, focusing on identifying critical vulnerabilities within IT infrastructures and outlining practical preventive measures. The analysis examines key attack vectors—including compromised websites, infected applications, and supply chain infiltration—and explores how unauthorized cryptocurrency mining degrades system performance and endangers data security. The research methodology combines an evaluation of current cybersecurity trends, a review of specialized literature, and a controlled experiment simulating cryptojacking attacks. The findings highlight the importance of multi-layered protection mechanisms and ongoing system monitoring to detect malicious activities at an early stage. Full article

Ransomware is a group of malware that aims to make computing resources unavailable, demanding a ransom amount to return control back to users. Ransomware can be classified into two types: crypto-ransomware and locker ransomware. Crypto-ransomware employs strong encryption and prevents users’ access to the system. Locker ransomware makes access unavailable to users either by locking the boot sector or the user’s desktop. The proposed solution is an anomaly-based ransomware detection and prevention system consisting of post- and pre-encryption detection stages. The developed IDS is capable of detecting ransomware attacks by monitoring the usage of resources, triggered by anomalous behavior during an active attack. By analyzing the recorded parameters after recovery and logging any adverse effects, we were able to train the system for better detection patterns. The proposed solution allows for detection and intervention against the crypto and locker types of ransomware attacks. In previous work, the authors introduced a novel anti-ransomware tool for Windows platforms, known as R-Locker, which demonstrates high effectiveness and efficiency in countering ransomware attacks. The R-Locker solution employs “honeyfiles”, which serve as decoy files to attract ransomware activities. Upon the detection of any malicious attempts to access or alter these honeyfiles, R-Locker automatically activates countermeasures to thwart the ransomware infection and mitigate its impact. Building on our prior R-Locker framework this work introduces a multi-stage detection architecture with resource–behavioral hybrid analysis, achieving cross-platform efficacy against evolving ransomware families not addressed previously. Full article

Rice (Oryza sativa) is a globally important crop that plays a central role in maintaining food security. This scientific review examines the critical role of genetic disease resistance in protecting rice yields, dissecting at the molecular level how rice plants detect and respond to pathogen attacks while evaluating modern approaches to developing improved resistant varieties. The analysis covers single-gene-mediated and multi-gene resistance systems, detailing how on one hand specific resistance proteins, defense signaling components, and clustered loci work together to provide comprehensive protection against a wide range of pathogens and yet their production is severely impacted by pathogens such as Xanthomonas oryzae (bacterial blight) and Magnaporthe oryzae (rice blast). The discussion extends to breakthrough breeding technologies currently revolutionizing rice improvement programs, including DNA marker-assisted selection for accelerating traditional breeding, gene conversion methods for introducing new resistance traits, and precision genome editing tools such as CRISPR/Cas9 for enabling targeted genetic modifications. By integrating advances in molecular biology and genomics, these approaches offer sustainable solutions to safeguard rice yields against evolving pathogens. Full article