Robust Defense Research Articles

Dolphin leukocytes exhibit an attenuated cytokine response and increase heme oxygenase activity upon exposure to lipopolysaccharides

Cetaceans exhibit physiological adaptations that allowed the transition to aquatic life, including a robust antioxidant defense system that prevents injury from repeated exposure to ischemia/reperfusion events associated with breath-hold diving. The signaling cascades that characterize ischemic inflammation in humans are well characterized. In contrast, cetaceans' molecular and biochemical mechanisms that confer tolerance to inflammatory events are poorly understood. Heme oxygenase (HO) is a cytoprotective protein with anti-inflammatory properties. HO catalyzes the first step in the oxidative degradation of heme. The inducible HO-1 isoform is regulated by various stimuli, including hypoxia, oxidant stress, and inflammatory cytokines. The objective of this study was to compare the response of HO-1 and cytokines to a proinflammatory challenge in leukocytes isolated from humans and bottlenose dolphins (Tursiops truncatus). We measured changes in HO activity, and abundance and expression of interleukin 1 beta (IL-1β), interleukin 6 (IL-6), tumor necrosis factor-alpha (TNF-α), and heme oxygenase 1 (HMOX1) in leukocytes treated with lipopolysaccharide (LPS) for 24 and 48 h. HO activity increased (p < 0.05) in dolphin (48 h) but not human cells. TNF-α expression increased in human (24 h, 48 h), but not dolphin cells following LPS stimulation. LPS-induced cytokine expression was lower in dolphin than in human leukocytes, suggesting a blunted cytokine response in bottlenose dolphin leukocytes treated with LPS. Results suggest species-specific regulation of inflammatory cytokines in leukocytes treated with LPS, which may lead to differential responses to a pro-inflammatory challenge between marine and terrestrial mammals.

Natural products and their analogues acting against Mycobacterium tuberculosis: A recent update.

Tuberculosis (TB) remains one of the deadliest infectious diseases caused by Mycobacterium tuberculosis (M.tb). It is responsible for significant causes of mortality and morbidity worldwide. M.tb possesses robust defense mechanisms against most antibiotic drugs and host responses due to their complex cell membranes with unique lipid molecules. Thus, the efficacy of existing front-line drugs is diminishing, and new and recurring cases of TB arising from multidrug-resistant M.tb are increasing. TB begs the scientific community to explore novel therapeutic avenues. A precise knowledge of the compounds with their mode of action could aid in developing new anti-TB agents that can kill latent and actively multiplying M.tb. This can help in the shortening of the anti-TB regimen and can improve the outcome of treatment strategies. Natural products have contributed several antibiotics for TB treatment. The sources of anti-TB drugs/inhibitors discussed in this work are target-based identification/cell-based and phenotypic screening from natural products. Some of the recently identified natural products derived leads have reached clinical stages of TB drug development, which include rifapentine, CPZEN-45, spectinamide-1599 and1810. We believe these anti-TB agents could emerge as superior therapeutic compounds to treat TB over known Food and Drug Administration drugs.

A Robust Stackelberg Game for Cyber-Security Investment in Networked Control Systems

We present a resource-planning game for cyber-security of networked control systems (NCSs). The NCS is assumed to be operating in closed loop using a linear state feedback <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {H}_{2}$ </tex-math></inline-formula> -controller. A zero-sum, two-player Stackelberg game (SG) is developed between an attacker and a defender for this NCS. The attacker aims to disable communication of selected nodes and thereby render the feedback gain matrix to be sparse, leading to degradation of closed-loop performance, while the defender aims to prevent this loss by investing in the protection of targeted nodes. Both the players trade their <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {H}_{2}$ </tex-math></inline-formula> -performance objectives for the costs of their actions. The standard backward induction (BI) method is modified to determine a cost-based Stackelberg equilibrium (CBSE) that saves the players’ costs without degrading the control performance. We analyze the dependence of a CBSE on the relative budgets of the players and on the node “importance” order. Moreover, a robust defense (RD) method is developed for the realistic case when the defender is not informed about the attacker’s resources. The proposed algorithms are validated using examples from wide-area control of electric power systems. It is demonstrated that reliable and RD is feasible unless the defender’s resources are severely limited relative to the attacker’s resources. We also show that the proposed methods are robust to time-varying model uncertainties and thus are suitable for long-term security investment in realistic NCSs. Finally, we use computationally efficient genetic algorithms (GAs) to compute the optimal strategies of the attacker and the defender in realistic large power systems.

DFF-SC4N: A Deep Federated Defence Framework for Protecting Supply Chain 4.0 Networks

The management of contemporary communication networks of supply chain (SC) 4.0 is becoming more complex due to the heterogeneity requirements of new devices concerning the integration of the Internet of Things in the legacy industry networks. Hence, it becomes a challenging task to secure networks of SC 4.0 from cyber-attacks and provide a robust and efficient defence framework that can resist sophisticated attacks. Machine learning-based intelligent detection algorithms are often trained at either a centralized or single server, which makes it difficult to train an effective model and also it violates privacy concerns if gathering data from other servers at the edge. Classical machine learning approaches function on the legacy group of data placed on a central or single server, which brands it the least favored choice for supply chain networks, with data privacy issues. To address these problems, this article proposes a federated learning-based efficient detection model named, DFF-SC4N, to proactively identify intrusions from SC 4.0 networks using distributed local data training. DFF-SC4N uses communication rounds in a federated learning manner having gated recurrent units by only sharing the learned parameters and keeps the data intact on local servers. The accuracy of the global model is optimized by an aggregating model, which updates from multiple servers and multiple SC 4.0 networks. Extensive experiments on real industrial network data demonstrate that the DFF-SC4N outperforms both centralized training models and state-of-the-art peer methods in protecting SC 4.0 networks.

A Deep Learning Game Theoretic Model for Defending Against Large Scale Smart Grid Attacks

Power grids that are interdependent with communication networks create more possible modes of failure (e.g., cyberattacks) as well as more complex propagation of failure through the coupled networks. To ensure robust defense of smart grids, it is important to model both attacker and defender as intelligent, a scenario that the framework of game theory provides methods to analyze. However, prior works in applying game theoretic models to smart grid security limit the problem space to a small number targets under threat due to the inability of state-of-the-art methods to scale to large networks. Our method scales to large networks by combining neural networks that use featurized action representations with an approximation of large combinatorial actions to generalize knowledge about the best targets to attack/defend across graphs of various topologies and sizes. Our model’s invariance to the size of the input graph allows us to transfer knowledge from games played on small graphs during training to large graphs during evaluation. Our experiments show that our method can learn Nash equilibrium strategies on small networks, and demonstrate low exploitability when generalized to large networks, especially compared to the common heuristics currently used to simulate attacks on large graphs.

NF-κB activation enhances STING signaling by altering microtubule-mediated STING trafficking

It is widely known that stimulator of interferon genes (STING) can trigger nuclear factor κB (NF-κB) signaling. However, whether and how the NF-κB pathway affects STING signaling remains largely unclear. Here, we report that Toll-like receptor (TLR)-, interleukin-1 receptor (IL-1R)-, tumor necrosis factor receptor (TNFR)-, growth factor receptor (GF-R)-, and protein kinase C (PKC)-mediated NF-κB signaling activation dramatically enhances STING-mediated immune responses. Mechanistically, we find that STING interacts with microtubules, which plays a crucial role in STING intracellular trafficking. We further uncover that activation of the canonical NF-κB pathway induces microtubule depolymerization, which inhibits STING trafficking to lysosomes for degradation. This leads to increased levels of activated STING that persist for a longer period of time. The synergy between NF-κB and STING triggers a cascade-amplified interferon response and robust host antiviral defense. In addition, we observe that several gain-of-function mutations of STING abolish the microtubule-STING interaction and cause abnormal STING trafficking and ligand-independent STING autoactivation. Collectively, our data demonstrate that NF-κB activation enhances STING signaling by regulating microtubule-mediated STING trafficking.

Modified Firefly Optimization Algorithm-Based IDS for Nature-Inspired Cybersecurity

The new paradigm of nature-inspired cybersecurity can establish a robust defense by utilizing well-established nature-inspired computing algorithms to analyze networks and act quickly. The existing research focuses primarily on the efficient selection of features for quick and optimized detection rates using firefly and other nature-inspired optimization techniques. However, selecting the most appropriate features may be specific to the network, and a different set of features may work better than the selected one. Therefore, there is a need for a generalized pre-processing step based on the standard network monitoring parameters for the early detection of suspicious nodes before applying feature-based or any other type of monitoring. This paper proposes a modified version of the firefly optimization algorithm to effectively monitor the network by introducing a novel health function for the early detection of suspicious nodes. We implement event management schemes based on the proposed algorithm and optimize the observation priority list based on a genetic evolution algorithm for real-time events in the network. The obtained simulation results demonstrate the effectiveness of the proposed algorithm under various attack scenarios. In addition, the results indicate that the proposed method reduces approximately 60–80% of the number of suspicious nodes while increasing the turnaround time by only approximately 1–2%. The proposed method also focuses specifically on accurate network health monitoring to protect the network proactively.

Priming Bean Seedlings to Boost Natural Plant Defenses Against Common Bacterial Wilt: Salicylic Acid Responses to Chemical Primers (Part 1)

This greenhouse study evaluated the effects of two chemical inducers for priming kidney bean seedlings against a bacterial wilt disease. This study's central premise was that chlorine dioxide's oxidant properties would mimic the signaling properties of radical oxygen species, thereby initiating a cascade of molecular plant defenses, including the synthesis of salicylic acid (SA). This signaling agent then initiates a cascade of pre-defense activities to provide a more rapid and robust natural defense against pathogen attacks. This factorial study included two levels for a foliar chlorine dioxide treatment and two for a bacterial wilt inoculation treatment. The two plant response variables were free and conjugated salicylic acid levels sampled in leaf tissue over two collection dates. Half of the 96 plants were inoculated with a bacterial culture that causes common bean wilt disease. Leaf tissue was harvested 17 to 32 h and 960 h after the wilt inoculation to determine the temporal dynamics of SA due to chemical treatments. Also, PCR tests were used to verify wilt presence in the inoculated plants. Inoculation of the wilt disease did not affect free SA when leaf tissue was sampled from 17 to 32 h. after wilt inoculation. However, chlorine dioxide applied at 400 mg/l and sampled at 20 h after inoculation resulted in a 15-fold increase in free SA over the control. Also, chlorine dioxide applied at 400 mg/l with leaf tissue sampled at 26 h after inoculation resulted in a 33-fold increase in conjugate SA levels compared to the control plants. Leaf tissue sampled at 960 h after the inoculation showed no free SA differences among the chemical treatments. However, the inoculated plant had a 15.9-fold increase in free SA compared to the non-inoculated plants. The priming effect on kidney bean seedlings using a single chlorine dioxide foliage application temporarily increased free and conjugate SA. The free and conjugate SA levels for the non-inoculated plants returned to baseline levels when sampled at 960 h. These results indicate that primed plants elevate SA up to several weeks with a slow decline back to baseline levels. Stem injection of the bacterial wilt bypassed the immunity mechanisms present in leaves, which significantly increased the wilt injury levels. Stem injection negated much of the foliar defenses, which overshadowed the priming effects of the chemical treatments on plant immunity and foliar defenses. The second leaf sampling on newly formed leaves reveals elevated SA levels in the inoculated plants but not in the non-inoculated plants.

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Making Law Possible

Ectopic expression of disease resistance protein promotes resistance against pathogen infection and drought stress in Arabidopsis

Plant-pathogen interaction is co-evolving and results in developed resistance in both the host and invader. Plants being sessile cannot physically escape from the pathogens and thus are armored with defense strategies resulting in neutralizing the pest effects. Resistance (R) genes in plants elicit a robust defense by synthesizing their protein products against pathogen effectors. Primarily, the expressions of R genes are tightly regulated by multiple surveillance mechanism(s). A less explored R gene of Arabidopsis thaliana, AT5G51630 (disease related protein; DRP), confers resistance against bacterial pathogen. When over-expressed, the DRP localizes in the cytoplasm and the Arabidopsis with ectopic expression of DRP exhibits delayed flowering phenotype aligning with the fact that immunity costs growth. The over-expression of the DRP in Arabidopsis thaliana results in enhanced immunity, primarily by modulating the basal immunity, as evident from the early activation of pathogenesis-related protein 1 (PR1) relative to the wild-type. Here we demonstrate that the engineered transgenic plant not only offers resistance against the Pseudomonas syringae but also favors the plant during drought stress.

Mysterium Esse Christi: Thomas Aquinas &amp; the Supernatural Being of Jesus Christ

AbstractFor over 700 years scholastic theologians of varying degrees of allegiance to the text(s) of Thomas Aquinas have discoursed on the mystery of Christ's being (esse): Did Christ have one or two acts of existence? Yet despite this frequent and recurring quaestio, nevertheless only a handful of scholastic commentators pause to note that this is not simply a debate between rival scholastic ‘schools’ in regard to a theological mystery, but that in fact there is an inconsistency within the Angelic doctor's own texts. And while in more recent scholarship this discrepancy has not only been noticed but explicated in various ways, nevertheless it is the contention of this paper that a satisfactory exposition of the meaning of esse secundarium has not yet been achieved. Consequently, I propose in this paper that esse secundarium is the created, substantial, but absolutely supernatural participation of the human nature of Jesus in the uncreated communication of the divine esse of the Word and provide a robust textual defense of this interpretation.

Detection-based active defense of biased injection attack based on robust adaptive controller

As the promising technology, the cooperative cyber-physical system can enhance the operating efficiency and reliability of smart grids. Meanwhile, the characteristics that deep integration of cyber-physical system can make smart grid face new security problems caused by false data injection attack. To maintain a safe and stable operation of smart grids, timely detection and defense of the emerging false data injection attacks, such as biased injection attack, is crucial. For this reason, this paper aims at developing a detection-based active defense mechanisms against biased injection attacks via robust adaptive controller. Through the established physical dynamic power model, an improved adaptive observer-based detection algorithm is proposed. Through the design of observer parameters, the proposed adaptive observer can enhance the accuracy of estimation state. In contrast to well-known attack detection methods for smart grids, the performance of attack detection under the developed detection algorithm can be effectively improved, such as the accuracy of state estimation and false positive rate. Through the above results provided by the attack detection, a robust adaptive controller-based active defense method is further developed. The proposed method can offset the impact of biased injection attack to maintain the stable running of power system. Simulation studies demonstrate the reliable response of the developed active defense method against biased injection attacks.

Japan and Australia: Strengthening Partnership in the Indo-Pacific Era

After many decades of a relationship defined primarily by commercial ties, Japan and Australia now have a robust security and defence partnership. While making their bilateral relations more all-rounded in the Indo-Pacific era, they also aim to promote a regional order where the United States remains engaged and like-minded nations come together to manage the ever-volatile strategic environment in the wake of China’s rise and US–China strategic rivalry. These tasks are not easy due to strategic complexities; how the two key Indo-Pacific nations respond to each other and to their partners in managing the China challenge while ensuring US commitment to the region will be of deep interest to policymakers and analysts alike.

Enhancing Cybersecurity against Stuxnet in the Future of Cyberwarfare: A Combined Approach Using Firewalls and Intrusion Detection Systems

Stuxnet is a highly customized malware developed to destroy centrifuges used in the Iranian nuclear program through SCADA systems. It infects a computer through a USB drive, making it effective for targeting air-gapped networks. Stuxnet is larger and more complex than an equivalent worm; it is created in several different programming languages, and some components are encrypted. The malware utilizes four unprecedented zero-day vulnerability attacks that exploit application security vulnerabilities before developers become aware of the vulnerability. Additionally, it employs advanced rootkit technology to conceal itself from users and antimalware software on both Windows and the control computer it targets. To strengthen cybersecurity, this study implemented and configured a combination of firewalls and intrusion detection systems (IDS) to enhance security against the Stuxnet malware. By integrating these security measures, the aim was to establish a robust defense against the sophisticated attack methods employed by Stuxnet. It is essential to continuously update and adapt these security measures as the threat landscape evolves. By remaining vigilant and proactive, organizations can effectively safeguard their systems from sophisticated threats like Stuxnet, bolstering their cybersecurity defenses.

A collaborative prediction approach to defend against amplified reflection and exploitation attacks

&lt;abstract&gt;&lt;p&gt;An amplified reflection and exploitation-based distributed denial of service (DDoS) attack allows an attacker to launch a volumetric attack on the target server or network. These attacks exploit network protocols to generate amplified service responses through spoofed requests. Spoofing the source addresses allows attackers to redirect all of the service responses to the victim's device, overwhelming it and rendering it unresponsive to legitimate users. Mitigating amplified reflection and exploitation attacks requires robust defense mechanisms that are capable of promptly identifying and countering the attack traffic while maintaining the availability and integrity of the targeted systems. This paper presents a collaborative prediction approach based on machine learning to mitigate amplified reflection and exploitation attacks. The proposed approach introduces a novel feature selection technique called closeness index of features (CIF) calculation, which filters out less important features and ranks them to identify reduced feature sets. Further, by combining different machine learning classifiers, a voting-based collaborative prediction approach is employed to predict network traffic accurately. To evaluate the proposed technique's effectiveness, experiments were conducted on CICDDoS2019 datasets. The results showed impressive performance, achieving an average accuracy, precision, recall and F1 score of 99.99%, 99.65%, 99.28% and 99.46%, respectively. Furthermore, evaluations were conducted by using AUC-ROC curve analysis and the Matthews correlation coefficient (MCC) statistical rate to analyze the approach's effectiveness on class imbalance datasets. The findings demonstrated that the proposed approach outperforms recent approaches in terms of performance. Overall, the proposed approach presents a robust machine learning-based solution to defend against amplified reflection and exploitation attacks, showcasing significant improvements in prediction accuracy and effectiveness compared to existing approaches.&lt;/p&gt;&lt;/abstract&gt;

Performance optimization for Intrusion Detection by Long Short Term Memory (LSTM)

Concerns about cyber threats have emerged as the expansion of system connectivity and the proliferation of system applications intensified in the industry. This has underscored the necessity for a robust defense mechanism against various cyber threats, including potential intrusions from malicious actors within the network. A specially targeted system is the intrusion detection system (IDS), designed to safeguard the confidentiality, integrity, and availability of network traffic, especially in critical sectors like healthcare. Recent advancements in the area of IDS involve the utilization of artificial intelligence (AI) and deep learning (DL) based IDS to efficiently recognize network issues. Notably, the research at hand adopts a deep learning approach employing Long Short Term Memory (LSTM) models, applied to the CICIDS-2019 dataset that is sourced from New Brunswick University’s website. The focal point of evaluation lies in the precision, recall, F1-score, and accuracy metrics, specifically analyzing its performance in identifying Denial-of-Service (DoS) cyber-attacks. The findings of this study lighten the superior performance of the Long Short Term Memory method in the realm of intrusion detection systems. The LSTM model showcases its proficiency, particularly in discerning Denial of Service attacks by giving a loss of less than 0.03%.

The Possibilities of Anti-Colonial Constitutionalism – Commentary on State of West Bengal v. Anwar Ali Sarkar and others AIR 1952 SC 75

The case of Anwar Ali Sarkar has been a founding precedent for equality jurisprudence in India and elsewhere. The case is most cited for the two-step reasonable classification test. Yet over the years, it has become increasingly clear that there are limitations to the test as a doctrinal framework to adjudicate equality. This article is a comment on Tarunabh Khaitan’s accompanying rewritten judgment on Anwar Ali Sarkar, which seeks to offer a more robust doctrine of equality grounded in the anti-colonial character of the Indian Constitution. In this piece, I locate the original judgment in its historical and socio-political context to understand the possibilities of building an anti-colonial jurisprudence of equality for and of the Indian Constitution, and to evaluate the differences between the original and rewritten judgments. I argue that the rewritten judgment offers a more robust defence of equality, in line with strands of anti-colonial ideas that shaped the framing of the Indian Constitution, as well as offering a restrained method for judicial interpretation that acknowledges historical context while centering the interpretation of law and rules.

Super Alloy Coatings for Enhanced Corrosion and Oxidation Resistance in Extreme Environments

The use of super alloy coatings is an innovative method for enhancing the durability of materials against the detrimental effects of corrosion and oxidation, especially in areas with severe circumstances. This study explores the forefront of super alloy coatings, examining their significant role in minimising the adverse effects of corrosive and oxidative processes on materials working in harsh conditions. This study highlights the transformative potential of super alloy coatings in extending the lifespan and enhancing the reliability of critical components. It achieves this by presenting a comprehensive overview of the fundamental principles, fabrication methodologies, and practical applications associated with these coatings. As many sectors navigate more difficult environments, the effective incorporation of advanced coatings presents a potential direction, advancing the field of materials science and guaranteeing consistent performance in the presence of significant challenges. The emergence of super alloy coatings represents a significant transformation in the field of corrosion and oxidation control, providing a robust defence against the persistent deteriorating factors seen in highly challenging settings. The combination of complex production procedures and superior material qualities empowers these coatings to strengthen materials, therefore assuring their durability.

In Vitro Reconstitutive Base Excision Repair (BER) Assay.

The mammalian cell genome is continuously exposed to endogenous and exogenous insults that modify its DNA. These modifications can be single-base lesions, bulky DNA adducts, base dimers, base alkylation, cytosine deamination, nitrosation, or other types of base alteration which interfere with DNA replication. Mammalian cells have evolved with a robust defense mechanism to repair these base modifications (damages) to preserve genomic stability. Base excision repair (BER) is the major defense mechanism for cells to remove these oxidative or alkylated single-base modifications. The base excision repair process involves replacement of a single-nucleotide residue by two sub-pathways, the single-nucleotide (SN) and the multi-nucleotide or long-patch (LP) base excision repair pathways. These reactions have been reproduced in vitro using cell free extracts or purified recombinant proteins involved in the base excision repair pathway. In the present chapter, we describe the detailed methodology to reconstitute base excision repair assay systems. These reconstitutive BER assay systems use artificially synthesized and modified DNA. These reconstitutive assay system will be a true representation of biologically occurring damages and their repair.

Anomaly-Based Intrusion Detection System To Detect Advanced Persistent Threats: Environmental Sustainability

In an evolving digital world, Advanced Persistent Threats (APTs) pose severe cybersecurity challenges. These extended, stealthy cyber-attacks, often elude conventional Intrusion Detection Systems (IDS). To bridge this gap, our research introduces a novel, environmentally conscious, deep learning-based IDS designed for APT detection. The system encompasses various stages from objective definition, data collection and preprocessing, to model development, integration, validation, and deployment. The system, utilizing deep learning algorithms, scrutinizes network traffic to detect patterns characteristic of APTs. This approach improves IDS accuracy and allows real-time threat detection, enabling prompt response to potential threats. Importantly, our system contributes to environmental protection by minimizing power consumption and electronic waste associated with cyberattacks, promoting sustainable cybersecurity practices. Our research outcomes are expected to enhance APT detection, providing robust defense against sophisticated cyber threats. Our environmentally-conscious perspective adds a unique dimension to the cybersecurity domain, underlining its role in sustainable practices.