Search Results (1,201)

Extreme climatic conditions often intensify abiotic stress factors (such as drought, salinity, heat stress, ultraviolet radiation, and soil degradation), and are increasingly limiting crop productivity and threatening global food security. Secondary metabolites (SMs), traditionally viewed as defense compounds, are now recognized as key regulators of plant adaptation to environmental stress. This review synthesizes recent advances in understanding the role of SMs as biochemical targets for improving crop resilience to climate extremes. By integrating evidence from multi-omics studies, artificial-intelligence-driven analyses, and functional genomics, we examine how stress-specific metabolic signatures and regulatory networks can be exploited for crop improvement. We further discuss the application of genome editing, synthetic biology, and metabolomics-assisted breeding to modulate the SM pathways to enhance stress tolerance. Selected case studies highlight the contribution of flavonoids, alkaloids, and terpenoids to stress adaptation in major and underutilized crops grown under salinity, drought, and low-temperature conditions. Despite significant progress, challenges remain, including metabolic trade-offs between stress tolerance and yield, regulatory constraints, and public acceptance of genetically engineered crops. By linking molecular mechanisms with applied strategies, this review provides a conceptual framework for leveraging secondary metabolism in climate-resilient agriculture and identifies key gaps to guide future research and innovation. Full article

Industrial control systems (ICS), which manage critical infrastructure such as power grids and water treatment, are increasingly exposed to cyber threats and operational faults as their connectivity to external networks grows. AI-based anomaly detection has emerged as a key defense, yet three limitations restrict its practical deployment: (i) detected anomalies are treated uniformly without distinguishing between transient faults and intentional attacks, hindering tailored incident response; (ii) the trade-off between detection accuracy and the false-positive rate burdens experts with extensive manual triage and delays prompt action; and (iii) prevailing feature-attribution Explainable AI (XAI) techniques such as SHAP and LIME produce fragmented sensor-level explanations and fail to capture correlations among sensors in time-series data, undermining trust in model decisions. To address these gaps, this paper proposes a graph-based deep learning framework that (a) defines anomaly types in terms of the anomalous-sensor ratio measured before and after smoothing—which operationalizes the correlation-maintenance principle that faults keep coupled sensors jointly anomalous while attacks isolate them—enabling explicit separation of faults, attacks, false positives, and false negatives; (b) identifies ambiguous decisions near the detection threshold as candidate false alarms via dynamic threshold smoothing; and (c) provides correlation-aware graph visualizations for intuitive interpretation. Experiments on the Secure Water Treatment (SWaT) dataset center on this post-detection layer: built on a standard graph-based detector (F1-score 0.787 at Top-K = 10) that serves only as the substrate, the categorization separates faults from attacks, and the subsequent ambiguity analysis identifies false negatives with 83% precision and false positives with 73% precision. By separating attacks from faults and surfacing high-likelihood false alarms together with intuitive sensor-correlation explanations, the proposed approach reduces analyst workload and supports more reliable, prioritized incident response in ICS environments. Full article

Sustainable agricultural technologies are essential to respond to environmental and social pressures, ensuring the maintenance of global food security. Therefore, there is an urgent demand for more sustainable agricultural practices that promote soil quality, as this factor directly impacts the global economy. Agricultural yield is directly associated with soil health and fertility. The use of organic waste serves as a source of essential nutrients for plants, increasing soil organic matter, contributing to the improvement of soil physical and chemical properties, as well as increasing crop yield. Based on this context, this research aimed to evaluate the effects of incorporating organic waste aiming to mitigate the oxidative damage in maize plants grown under different levels of soil fertility (low, average, and high), evaluating soil and plant, more specifically chemical, physiological, biochemical, and morphological responses. In soil, organic waste promoted significant increases in the activities of arylsulfatase and β-glucosidase and improved the chemical parameters, including cation exchange capacity, soil organic matter, base saturation, and sum of bases. The application of organic waste, regardless of fertility level, improved the nutritional status in maize plants, increased concentrations of photosynthetic pigments, maximized the photochemical efficiency and photosynthesis rate. In plant metabolism, the results demonstrated that organic waste promoted significant increases in plant antioxidant defense, including superoxide dismutase, catalase, ascorbate peroxidase, and peroxidase, minimizing the oxidative stress on photosynthetic machinery, especially in plants cultivated on soil with low fertility. Therefore, this research proves that organic waste mitigates the negative impacts associated with nutritional starvation, improves soil health and fertility, favors the maintenance of redox metabolism, and stimulates photosynthesis in maize plants cultivated in low-fertility soil. Full article

This study analyzes cyberattack trends targeting non-profit organizations using longitudinal data collected over a three-year period within the Non-Profit Cybersecurity Incident Repository (NPCIR). Developed through a National Security Agency Center of Academic Excellence in Cyber Defense (NSA CAE-CD) designated center, the NPCIR applies an open-source intelligence (OSINT) methodology to systematically document cybersecurity incidents affecting the global non-profit sector. This study examines attack types, threat actor characteristics, sectoral distribution, and cybersecurity impacts using the Confidentiality–Integrity–Availability (CIA) triad framework. The results indicate that availability-related incidents, particularly ransomware and distributed denial-of-service (DDoS) attacks, constitute the most prevalent threats, while confidentiality breaches remain highly significant due to frequent data exposure incidents. Statistical analyses further demonstrate significant differences between non-profit organizations aligned with DHS CISA critical infrastructure sectors and those operating outside these sectors, especially regarding the prevalence of availability-focused attacks. In addition to its empirical contribution, the NPCIR initiative supports experiential learning opportunities for undergraduate and graduate students in cybersecurity and information technology. The resulting dataset provides actionable cyber threat intelligence for researchers, practitioners, and non-profit leaders seeking to strengthen organizational cybersecurity resilience and awareness. Full article

Research examining the security of network intrusion detection systems is vital for protecting modern digital infrastructure from increasingly sophisticated threats. This study investigates how machine learning network security models, trained with tactical frameworks like MITRE ATT&CK, respond to adversarial examples crafted through black-box optimization techniques. Using three attack algorithms, HopSkipJump, Simultaneous Perturbation Stochastic Approximation Attack and the Square Attack algorithms, we demonstrate that the Random Forest model remains vulnerable despite tactical framework integration. For example, the HopSkipJump attack achieved a 92% success rate in causing malicious traffic to appear benign. Our analysis reveals which network traffic features are most susceptible to manipulation, with model performance metrics declining significantly under adversarial conditions. These findings highlight an important gap between theoretical security frameworks and practical implementation that must be addressed to develop more robust defense systems. By identifying specific vulnerabilities, this research contributes valuable insights that can inform improved adversarial robustness in operational network security environments. Full article

Distributed photovoltaic (PV) power stations are core enablers for dual-carbon goals in modern power systems, with IoT-based monitoring systems serving as their nerve center for real-time data collection and grid dispatch. However, PV monitoring nodes operate in harsh, unattended outdoor environments with severe computational resource constraints, exposing them to critical hardware security risks that can trigger cross-domain cascading hazards. Existing research focuses primarily on communication and software security, lacking systematic hardware security modeling and lightweight defense designs. Generic IoT hardware security solutions are also inapplicable due to excessive overhead. To address these gaps, this paper proposes LHSF, a lightweight hardware security framework tailored for resource-constrained PV edge nodes. It integrates an on-chip OTP-based lightweight hardware root of trust (L-HROT) with an SRAM-PUF-driven non-resident key management protocol, which implements full-lifecycle key management via a “power-on generation, on-demand usage, post-use destruction, zero-residue storage” paradigm. Experiments on ESP32 and Raspberry Pi 4B show that LHSF provides robust resistance to side-channel recovery, physical extraction, malicious firmware boot and rollback attacks, reducing fault injection bypass rate to 6.8%. Compared to standard TPM 2.0, it cuts boot delay by 60.7%, power consumption by 18.6% and memory footprint by 72.7% with negligible performance overhead. This work fills the hardware security gap for PV monitoring systems and provides a reusable technical pathway for distributed energy IoT terminals. Full article

Due to the indispensable use of the internet, malicious actors have exploited URLs as a threat source of network information security and integrity. URL detection based on traditional methods has become inefficient against the uncontrolled increase of URLs, especially when facing dynamic and large-scale threats. To address the limitations of traditional methods and to provide intelligent and scalable detection of malicious URLs, this study proposes the hybrid framework (NetGuard) by integrating probabilistic data structures (PDSs) with machine learning (ML) capabilities. The proposed NetGuard utilizes PDSs to develop a Hybrid Scalable Detection Filter (HSDF), which combines the strengths of counting Bloom filters (CBFs) (deletion capability) and Scalable Bloom filters (SBFs). The proposed HSDF provides efficient membership queries under bounded false-positive rates (approximately 0.01) and ensures efficient data management and low-latency lookups on a scale of 10⁻⁵ s. On the other hand, NetGuard leverages the ML classifier capabilities to train and package a learned classifier for detecting malicious URLs. The proposed framework utilizes Decision Trees (DTs) and Random Forest (RF) classifiers. The proposed classifiers are trained by a novel SupURLsIdDs dataset which includes fifteen distinctive lexical and structural URL features extracted from four URL classes: benign, defacement, malware, and phishing URLs. The experimental results indicated the effectiveness of the HSDF in insertion and deletion operations, with minimal memory consumption (approximately 2.7 MB for 222,000 URLs) while maintaining a controlled false-positive rate (approximately 0.01 on Real-only subset up to 0.12 with synthetic data). The HSDF memory footprint represents a 99.88% enhancement compared to the RF model (which demands 2253.17 MB); thus, the HSDF complements RF as an ultra-lightweight first line of defense. The ML classifiers showed the superiority of RF, which achieved an overall classification accuracy of approximately 96% on large-scale URL data. These experiments are conducted using benchmark datasets constructed from aggregated real and synthetic data to demonstrate the scalability, adaptability, and resource efficiency of the first phase of NetGuard as a practical foundation for real-time web threat detection. The real-time integration and dynamic updates are presented as a deployment architecture and constitute future work. Full article

The biofuel supply chain is a complex value chain spanning from production to consumption. Manipulating information such as geographical origin, raw material type, and quantity at the production stage can disrupt refinery production plans and cause supply–demand imbalances. Therefore, a transparent traceability system is essential. The existing centralized database architecture poses a high risk of supply chain service suspension due to even a temporary fault in the central server, and it lacks resilience. Furthermore, it is vulnerable to data forgery, making it urgent to secure information integrity. To resolve these issues, this study proposes a blockchain-based biofuel supply chain information protection system. This system utilizes Shamir’s Secret Sharing algorithm to distribute data location information across all nodes and introduces the R-snowball consensus algorithm, which combines the reputation score of nodes with the random sampling of Snowball. The system aims to secure resilience in the event of a failure, achieve reputation-based security, and provide preliminary evidence of robustness against internal and external threats under the tested conditions. Experimental results demonstrated that the proposed system achieved an average recovery time of within 0.03 s, regardless of the load volume. Furthermore, preliminary evidence under the tested conditions suggests that the security and robustness of the system were supported through the exclusion of internal malicious nodes via a reputation-based penalty logic, the defense against main chain takeover attempts in external attack scenarios involving multiple fake nodes (Sybil nodes), and the maintenance of consistent consensus times. Full article

Maritime piracy poses an ongoing operational threat to commercial shipping in high-risk regions, where fast-approach attack scenarios leave vessel crews with critically limited reaction time. Automated threat detection technologies—including radar, electro-optical, and thermal imaging sensors—are increasingly integrated into maritime security architectures; however, their operational effectiveness has rarely been evaluated through quantitative engineering frameworks. This study presents a technical analysis of mechatronic detection systems, focusing on detection range, reaction time constraints, and classification reliability under representative piracy conditions. A kinematic time-to-contact model is introduced to quantify how detection distance directly governs the available defensive response window: extending reliable detection from 1 NM to 3 NM expands the reaction margin from approximately 171 s to over 440 s, a difference that may determine whether protective measures can be executed in time. Classification performance is assessed using standard metrics, with recall identified as the operationally critical indicator in asymmetric threat environments. Model-based simulations indicate that, under the assumed scenario parameters, automated detection systems can reduce operational risk by up to 45%, illustrating the sensitivity of survivability outcomes to early detection capability. The findings translate directly into design thresholds for sensor range, algorithmic sensitivity, and processing latency, providing actionable engineering recommendations for practitioners responsible for maritime security system design and vessel protection planning. Full article

The growing sophistication of the internet and web space has spawned highly dynamic, multi-vector cyber threats that cannot be handled by automated detectives and hence the necessity to introduce analyst-oriented, cognitively powerful security analysis apparatus. The character of current visualization-based security frameworks is that they are inclined to deliver data unproactively, fail to engage the dynamic setting, and fail to comprehend the evolving motive of assailants, resulting in subsequent identification and a fractured understanding of coordinated web attacks. The paper introduces a new model of interactive security visualization known as Context-Oriented Visual Exploration of Resilient Threats (COVERT), a hybrid of behavioral context modeling, adaptive visual storytelling, and intent-sensitive interaction. COVERT is dynamically rearranged to the development of threats, patterns of interaction between analysts, and objectives of the possible attacks, which helps in releasing relevant security capabilities gradually. The framework integrates graphical threat flows, attention-directed visual cues, and real-time feedback loops to align system responses to the thinking processes of the analysts. The evaluation of high-scale web traffic and attack simulation dataset indicates that COVERT is much more effective in the multi-stage detection of attacks, false-positive interpretation is minimized, and the investigation period is reduced compared to the visualization infrastructure of the static and semi-interactive infrastructure. According to user studies, there is higher situation awareness, enhanced correlation of distributed events, and enhanced decision-making in complex web intrusion situations, such as advanced persistent threats and web exploitation coordination. Combining contextual intelligence with adaptive interaction and visualization of security, COVERT reveals that intent-based visual analytics may greatly improve internet and web threat detection and analysis systems to support more agile and resilient cyber defense procedures. The proposed COVERT strategy achieved 93% threat-detection rate, the false positives were reduced to 6%, the response time of the analysts was reduced to 140 s, and the situational awareness was increased to 88%. Full article

As digitalization is widely used, Industrial Control Systems (ICSs) face severe cybersecurity challenges, where traditional defenses often lack real-time detection and immutable audit trails. Therefore, we propose a security alert platform that integrates blockchain, smart contracts, and homomorphic encryption. By leveraging the decentralized architecture of blockchain, the platform ensures the integrity and non-repudiation of operational logs. Concurrently, anomaly detection logic is embedded within smart contracts to enable an automated, real-time alerting mechanism. Furthermore, to preserve industrial data privacy, homomorphic encryption is employed, allowing the system to perform anomaly detection directly on encrypted data, thereby maintaining confidentiality throughout the data lifecycle. Preliminary analysis indicates that the proposed platform effectively enhances the resilience of ICS, strengthening both defense against unauthorized operations and post-incident forensic capabilities. Full article

The rise of capture-the-flag (CTF) competitions and offensive security training requires the deployment of systems that are, by design, flawed. This creates a unique architectural paradox: how does one host a system intended to be compromised without compromising the host itself? This paper classifies the security principles of “range engineering”—the discipline of engineering the environment. This research study synthesizes evidence across the cyber-range, honeypot, ICS/OT testbed, and cloud-isolation literature to derive a containment-focused classification of threat planes, security invariants, boundary mechanisms and properties, and operational controls for intentionally vulnerable environments used in education, training, and research. Five security invariants are derived under the assumption of expected compromise and mapped to boundary families and measurable operational objectives. The analysis further identifies under-evidenced areas, particularly control-plane isolation, corrective controls for cross-tenant failures, and systematic validation of externalization defenses. Full article

As the internet and complex network infrastructures continue to expand, so does the threat of sophisticated cyberattacks, compelling organizations to adopt advanced proactive defenses. A cornerstone of these defensive strategies is the honeypot. However, existing dynamic solutions often rely on reactive deployment or centroid-based clustering (e.g., K-Means), which mathematically yields invalid, unrealistic host profiles. Because intelligent threat detection increasingly relies on high-fidelity honeypot data to analyze adversary tactics, deploying easily fingerprinted decoys fundamentally undermines downstream AI-driven defense mechanisms. To overcome this limitation, we propose DYNAMIT, an intelligent honeynet deployment system that resolves the centroid validity problem by utilizing the unsupervised K-Medoids algorithm. By combining K-Medoids with a novel hybrid Manhattan-Jaccard distance metric, DYNAMIT selects valid, existing hosts as templates based on categorical hardware and binary software similarities. The system then leverages containerization and network virtualization to simulate multiple realistic, internet-facing honeypot profiles from a single physical host, ensuring the decoys remain indistinguishable from legitimate targets. Our evaluation demonstrates that DYNAMIT accurately captures the intended number of clusters with a low relative error ($18.75\%$ for 40 hosts and $6.625\%$ for 1000 hosts) while maintaining minimal resource overhead, establishing it as a highly scalable and robust data-generation prerequisite for modern intelligent network security. Full article

The convergence of Software-Defined Networking (SDN) and Blockchain (BC) creates a symbiotic relationship in which SDN’s programmable global visibility complements BC’s decentralized, immutable trust model to address critical cybersecurity vulnerabilities and cyber attacks. Addressing the fragmentation in the current literature, this study rigorously investigates BC and SDN (B-SDN) integration with the primary objectives of: (1) differentiating impacts across varied sectors, including the Internet of Things (IoT), Smart Grids, and Vehicular Ad Hoc Networks (VANETs) and more; (2) analyzing critical performance metrics such as energy efficiency and scalability; (3) classifying mitigation, detection, and prevention schemes for specific threats; (4) examining novel Artificial Intelligence (AI) methods; and (5) identifying open challenges and future research directions. Methodologically, this study conducts a survey of state-of-the-art B-SDN studies to investigate six key areas: Industry-specific applications, security mechanisms, defense strategies, defenses against specific attacks, AI integration, and implementation performance. The findings demonstrate that B-SDN integration shows strong potential in simulated and prototype environments to mitigate specific high-impact threats, such as Distributed Denial of Service (DDoS), Man-in-the-Middle (MiTM), and spoofing, across various domains, including IoT, 5G/6G, VANETS, and Smart Grid. Despite the benefits and advantages promised by B-SDN, several limitations continue to exist, including the latency–security trade-off inherent to consensus protocols and scalability constraints in large-scale deployments. Finally, open research challenges persist in AI-driven automation, particularly in Federated Learning (FL) and in the development of standardized interoperability protocols required to enable the transition from conceptual models to operational systems. Full article

The internal saboteur may be understood as a multidimensional configuration of maladaptive inner processes involving recurrent negative self-evaluation, distressing relational expectations, repetitive negative thinking, and self-undermining inner experiences. Within this framework, the present study aimed to develop and examine the psychometric properties of the Internal Saboteur Scale (ISS), a self-report measure designed to assess this construct. A sample of 328 Italian adults (women 71.6%; Mage = 37.37, SD = 14.88) completed the survey. Confirmatory factor analyses supported both an eight-factor correlational model and a theoretically meaningful higher-order model, in which the lower-order dimensions were grouped into four broader domains: Negative Relational Expectations (Expected Rejection; Expected Judgment), Self-Devaluation (Negative Self-Appraisal; Interpersonal Unworthiness), Rumination (Retrospective Rumination; Anticipatory Rumination), and Internal Destructiveness (Helplessness; Defensive Relational Withdrawal). Measurement invariance across gender was also supported. All dimensions showed satisfactory-to-good internal consistency. Furthermore, ISS scores were negatively associated with secure attachment, self-reassurance, and mentalizing and positively associated with insecure attachment, self-criticism, shame, and anger. Overall, the ISS appears to be a theoretically grounded and psychometrically promising instrument for the assessment of maladaptive inner dialogue and self-sabotaging internal processes. It may represent a useful tool for both research and clinical practice, particularly in supporting transdiagnostic assessment and case formulation. Full article