Framework for a NetFPGA-based Snort NIDS

By comparing natural immunity to Aspergillus fumigatus (AF) in vivo with the action of human or mouse phagocytes against AF in vitro, we delineated two sequential lines of defense against AF. The first line of defense was formed by macrophages and directed against spores. Macrophages prevented germination and killed spores in vitro and rapidly eradicated conidia in vivo, even in neutropenic and athymic mice. The second was the neutrophilic granulocyte (PMN), which protected against the hyphal form of AF. Human and mouse PMN killed mycelia in vitro. Normal, but not neutropenic mice, stopped hyphal growth, and eradicated mycelia. Either line of defense acting alone protected mice from high challenge doses. Natural immunity collapsed only when both the reticuloendothelial system and PMN were impaired. These findings are in keeping with the clinical observation that high doses of cortisone and neutropenia are the main risk factors for invasive aspergillosis. Cortisone inhibited the conidiacidal activity of mouse macrophages in vivo and of human or mouse mononuclear phagocytes in vitro. Cortisone damaged this first line of defense directly and not through the influence of T lymphocytes or other systems modifying macrophage function as shown in athymic mice and in vitro. In addition, daily high doses of cortisone in mice reduced the mobilization of PMN so that the second line of defense was also impaired. Thus, cortisone can break down natural resistance on its own. Myelosuppression rendered mice susceptible only when the first line of defense was overpowered by high challenge doses, by activated spores that cannot be killed by macrophages, or by cortisone suppression of the conidiacidal activity of macrophages. The host, thus, can call upon two independent phagocytic cell lines that form graded defense systems against aspergillus. These lines of defense function in the absence of a specific immune response, which seems superfluous in the control and elimination of this fungus.

Plants absorb light energy for photosynthesis via photosystem complexes in their chloroplasts. However, excess light can damage the photosystems and decrease photosynthetic output, thereby inhibiting plant growth and development. Plants have developed a series of light acclimation strategies that allow them to withstand high light. In the first line of defense against excess light, leaves and chloroplasts move away from the light and the plant accumulates compounds that filter and reflect the light. In the second line of defense, known as photoprotection, plants dissipate excess light energy through non-photochemical quenching, cyclic electron transport, photorespiration, and scavenging of excess reactive oxygen species. In the third line of defense, which occurs after photodamage, plants initiate a cycle of photosystem (mainly photosystem II) repair. In addition to being the site of photosynthesis, chloroplasts sense stress, especially light stress, and transduce the stress signal to the nucleus, where it modulates the expression of genes involved in the stress response. In this review, we discuss current progress in our understanding of the strategies and mechanisms employed by plants to withstand high light at the whole-plant, cellular, physiological, and molecular levels across the three lines of defense.

Wildfires pose a significant challenge to the natural and the built environments, as well as the safety and economic wellbeing of the communities residing in wildfire-prone areas. The electric power grid is specifically among the built environments most affected by, and contributing to, wildfires. In this paper, we propose a three lines of defense (3LD) framework for wildfire risk management in electric power infrastructure and review the literature from this lens. An overview of the physics and phenomenology of the wildfires as it relates to power grids is presented, and the logic for the proposed 3LD framework is discussed. The reviewed literature based on the 3LD theme includes the most relevant and emerging research work on wildfire prevention as the first line of defense, wildfire mitigation and proactive response as the second line of defense, and wildfire recovery preparedness as the third line of defense. This study reveals that while the state of the art, to a large extent, stands comprehensive in various aspects of power system resilience and wildfire risk management, there is a gap in the literature in addressing this emerging risk in a holistic, interdisciplinary approach.

Network Intrusion Detection Systems (NIDS) have become crucial to securing modern networks. To be effective, a NIDS must be able to counter evasion attempts and operate at or near wire-speed. Failure to do so allows malicious packets to slip through a NIDS undetected. In this paper, we explore NIDS evasion through algorithmic complexity attacks. We present a highly effective attack against the Snort NIDS, and we provide a practical algorithmic solution that successfully thwarts the attack. This attack exploits the behavior of rule matching, yielding inspection times that are up to 1.5 million times slower than that of benign packets. Our analysis shows that this attack is applicable to many rules in Snorts ruleset, rendering vulnerable the thousands of networks protected by it. Our countermeasure confines the inspection time to within one order of magnitude of benign packets. Experimental results using a live system show that an attacker needs only 4.0 kbps of bandwidth to perpetually disable an unmodified NIDS, whereas all intrusions are detected when our countermeasure is used.

The Internet of Things (IoT) represents a new reality, as smart devices spread quickly and a higher number of applications arises. This attracts the attention of not only legitimate users but also attackers aiming to jeopardize the entire IoT infrastructure. Intrusion detection mechanisms are paramount in this networking environment as its first line of defense. Hence, this work proposes a Network Intrusion Detection System (NIDS) that deals with the Distributed Denial of Service (DDoS) attack, one of the most critical attacks that occur through IoT. The proposed NIDS uses stream learning to detect DDoS attacks in the IoT network and is designed to be deployed in a fog infrastructure. The detection model, built on Hoeffding Anytime Tree (HATT) algorithm, achieved a 99% accuracy and a 99% recall.

Wireless Sensor Networks (WSNs) have become a hot research topic in recent years. They have many potential applications for both civil and military tasks. However, the unattended nature of WSNs and the limited computational and energy resources of their nodes make them susceptible to many types of attacks. Intrusion detection is one of the major and efficient defence methods against attacks in a network infrastructure. Intrusion Detection Systems can be seen as the second line of defence and they complement the security primitives that are adopted in order to prevent attacks against the computer network being protected. The peculiar features of a wireless sensor network pose stringent requirements to the design of intrusion detection systems. In this paper, we propose a hybrid, lightweight, distributed Intrusion Detection System (IDS) for wireless sensor networks. This IDS uses both misuse-based and anomaly-based detection techniques. It is composed of a Central Agent, which performs highly accurate intrusion detection by using data mining techniques, and a number of Local Agents running lighter anomaly-based detection techniques on the motes. Decision trees have been adopted as classification algorithm in the detection process of the Central Agent and their behaviour has been analysed in selected attacks scenarios. The accuracy of the proposed IDS has been measured and validated through an extensive experimental campaign. This paper presents the results of these experimental tests.

In view of the current problems of HNIDS (high-speed network intrusion detection system), such as high packet loss rate, slow pace of testing for attacks and unbalanced data for detection. This paper presents a novel approach for HNIDS by taking two-stage strategy with load balancing model. In the on-line phase, the system captures the packets from network and split into small according the type of protocol, then detected intrusion through each sensor. In the off-line, training dataset are used to build model, which can detected intrusion. We discuss different approaches to unbalanced data, empirically evaluate the SMOTE over-sampling approaches, AdaBoost and random forests algorithm. We also discuss the approaches for selecting features. Finally report our experimental results over the KDD'99 datasets. The results show that SMOTE and the AdaBoost algorithm by using random forests as weak learner not only can provides better performance to small class, but also has lower build model time.

The Secure Storage service for the gLite middleware provides users with a set of tools to store in a secure way and in an encrypted format confidential data (e.g. medical or financial data) on the grid storage elements. The data stored though provided tools will be accessible and readable by authorized users only. Moreover, it solves the insider abuse problem preventing also the administrators of the storage elements to access the confidential data in a clear format. The service has been designed and developed for the grid middleware of the EGEE Project, gLite, in the context of the TriGrid VL Project

To improve the data processing speed of intrusion detection system, this paper focused on how to select representative samples from network data sets. Several resampling methods were discussed in this paper. The novel algorithm, Fast Hierarchical Nearest Neighbor (FHNN) outperformed NCL method in experiments with KDD’99 datasets. Taking the two-stage strategy with load balancing model for high-speed network intrusion detection system (HNIDS), we split the training dataset by the protocol and build the patterns for each dataset. Experimental results show that FHNN is faster than other methods and it is very efficient in tacking noise from majority class examples.

Bacterial Retrons Function In Anti-Phage Defense

Enveloped viruses such as SAR-CoV-2 are sensitive to temperature and are destroyed by temperatures tolerable to humans. All mammals use fever to deal with infections and heat has been used throughout human history in the form of hot springs, saunas, hammams, steam-rooms, sweat-lodges, steam inhalations, hot mud and poultices to prevent and treat respiratory infections and enhance health and wellbeing. This paper reviews the evidence for using heat to treat and prevent viral infections and discusses potential cellular, physiological and psychological mechanisms of action. In the initial phase of infection, heat applied to the upper airways can support the immune system’s first line of defence by supporting muco-ciliary clearance and inhibiting or deactivating virions in the place where they first lodge. This may be further enhanced by the inhalation of steam containing essential oils with anti-viral, mucolytic and anxiolytic properties. Heat applied to the whole body can further support the immune system’s second line of defence by mimicking fever and activating innate and acquired immune defences and building physiological resilience. Heat-based treatments also offer psychological benefits by directing focus on positive action, enhancing relaxation and sleep, inducing 'forced-mindfulness', and invoking the power of positive thinking and remembered wellness. Heat is a cheap, convenient and widely accessible therapeutic modality and while no clinical protocols exist for using heat to treat COVID-19, protocols that draw from traditional practices and consider contraindications, adverse effects and infection control measures could be developed and implemented rapidly and inexpensively on a wide scale. While there are significant challenges in implementing heat-based therapies during the current pandemic, these therapies present an opportunity to integrate natural medicine, conventional medicine and traditional wellness practices, and support the wellbeing of both patients and medical staff, while building community resilience and reducing the likelihood and impact of future pandemics.

The facultative intracellular pathogen Aeromonas salmonicida survives and replicates in macrophages, a virulence trait presumed to be associated with its ability to resist reactive radicals. The mechanisms used by A. salmonicida to resist reactive radicals in vitro were shown to have both structural and physiological determinants. The sensitivity of A. salmonicida to exogenous H2O2, superoxide, and nitrogen radicals, as well as endogenous oxygen radicals, differed depending on growth conditions, cell surface structure, and preexposure to sublethal doses of radicals. Whereas sensitivities to exogenous oxygen radicals did not correlate with basal levels of catalase or Fe-superoxide dismutase, under similar culture conditions S-layer positive cells were more resistant to oxygen radicals than S-layer mutants. S-layer mutants recovered resistance when physically reconstituted with S-layer sheets. Hemin-coated S-layers, while protective against nitrogen radicals, sensitized A. salmonicida to H2O2. Sublethal concentrations of H2O2or superoxide induced a highly protective response characterized by de novo synthesis of both catalase and Mn-superoxide dismutase. It is proposed that for A. salmonicida the constitutive S-layer provides a first line of defense and the inducible catalase and Mn-superoxide dismutase provide a powerful second line of defense against macrophage-mediated killing via reactive oxygen species.Key words: Aeromonas salmonicida, oxygen radicals, nitrogen radicals, oxidative stress, S-layers.

Immune responses against human papillomavirus (HPV) infection and evasion of host defense in cervical cancer

Network Intrusion Detection Systems (NIDS) are critical in ensuring the security of connected computer systems by actively detecting and preventing unauthorized activities and malicious attacks. Machine learning (ML) and deep learning (DL) based NIDS models leverage algorithms that learn from historical network traffic data to identify patterns and anomalies to capture complex relationships. The primary objective of this research is to generate tags and descriptions for the packets that are difficult to classify by the NIDS. Most NIDS datasets that are publicly available have focused on flow data, offering aggregated information about network connections, and have played a crucial role in enabling researchers and network security professionals to design and develop flow-based NIDS solutions. While flow records provide valuable information for detecting network-level anomalies and attacks, they do not consider packet-level information and payload contents. In this research, we propose a packet-level approach for NIDS that leverages the flow information with the packet header fields and payload. To facilitate this research, we have curated a comprehensive Packet-level dataset constructed by extracting the Packet Capture (PCAP) files from two widely used flow-level datasets, namely CIC-IDS2017 and UNSW-NB15. Recent advancements in Natural Language Processing (NLP) have demonstrated the effectiveness of Transformer-based models in handling sequence data with tasks such as token classification and text generation. We have adapted this technology to NIDS to extract key features and characteristics of the header and payload in the context of various attacks. Unlike traditional classification methods that assign predefined labels to network packets, this method focuses on generating tags based on the packet signature that explains the packet content and potential risks. The tags and descriptions offer network security professionals a tool to comprehend suspicious packets with an unfamiliar or potentially malicious signature, assess their nature, and help make informed decisions promptly.

Network intrusion detection system (NIDS) monitors network traffic to detect an unauthorized activity in computer networks. The NIDS is classified according to detection technique into signature and anomaly based. Each of them has its own advantage and disadvantage. The signature-based is more effective in detecting known attacks but it is unable to detect new attacks. The anomaly-based is better in detecting new attacks but it may produce many false alarms. NIDS which use both of them try to exploit the strengths of them. In this paper we propose an improvement of the well known Snort NIDS using clonal selection algorithm (CSA). The proposed approach is evaluated using the 1999 DARPA Intrusion Detection Evaluation Data Sets of MIT (Massachusetts Institute of Technology) as a testbed. The conducted experiments compare the recall, precision, and F-score of Snort NIDS on its own, Snort NIDS improved by negative selection algorithm (NSA), and the proposed approach. The obtained results show that the proposed approach is more powerful than the others.