Danger Theory Research Articles

The danger theory of immunity revisited.

The danger theory of immunity, introduced by Polly Matzinger in 1994, posits that tissue stress, damage or infection has a decisive role in determining immune responses. Since then, a growing body of evidence has supported the idea that the capacity to elicit cognate immune responses (immunogenicity) relies on the combination of antigenicity (the ability to be recognized by T cell receptors or antibodies) and adjuvanticity (additional signals arising owing to tissue damage). Here, we discuss the molecular foundations of the danger theory while focusing on immunologically relevant damage-associated molecular patterns, microorganism-associated molecular patterns, andneuroendocrine stress-associated immunomodulatory molecules, as well as on their receptors. We critically evaluate patient-relevant evidence, examining how cancer cells and pathogenic viruses suppress damage-associated molecular patterns to evade immune recognition, how intestinal dysbiosis can reduce immunostimulatory microorganism-associated molecular patterns and compromise immune responses, and which hereditary immune defects support the validity of the danger theory. Furthermore, we incorporate the danger hypothesis into a close-to-fail-safe hierarchy of immunological tolerance mechanisms that also involve the clonal deletion and inactivation of immune cells.

A Review on Immune-Inspired Node Fault Detection in Wireless Sensor Networks with a Focus on the Danger Theory.

The use of fault detection and tolerance measures in wireless sensor networks is inevitable to ensure the reliability of the data sources. In this context, immune-inspired concepts offer suitable characteristics for developing lightweight fault detection systems, and previous works have shown promising results. In this article, we provide a literature review of immune-inspired fault detection approaches in sensor networks proposed in the last two decades. We discuss the unique properties of the human immune system and how the found approaches exploit them. With the information from the literature review extended with the findings of our previous works, we discuss the limitations of current approaches and consequent future research directions. We have found that immune-inspired techniques are well suited for lightweight fault detection, but there are still open questions concerning the effective and efficient use of those in sensor networks.

Dengue Outbreak Detection Model Using Artificial Immune System: A Malaysian Case Study

Dengue is a virus that is spreading quickly and poses a severe threat in Malaysia. It is essential to have an accurate early detection system that can trigger prompt response, reducing deaths and morbidity. Nevertheless, uncertainties in the dengue outbreak dataset reduce the robustness of existing detection models, which require a training phase and thus fail to detect previously unseen outbreak patterns. Consequently, the model fails to detect newly discovered outbreak patterns. This outcome leads to inaccurate decision-making and delays in implementing prevention plans. Anomaly detection and other detection-based problems have already been widely implemented with some success using danger theory (DT), a variation of the artificial immune system and a nature-inspired computer technique. Therefore, this study employed DT to develop a novel outbreak detection model. A Malaysian dengue profile dataset was used for the experiment. The results revealed that the proposed DT model performed better than existing methods and significantly improved dengue outbreak detection. The findings demonstrated that the inclusion of a DT detection mechanism enhanced the dengue outbreak detection model’s accuracy. Even without a training phase, the proposed model consistently demonstrated high sensitivity, high specificity, high accuracy, and lower false alarm rate for distinguishing between outbreak and non-outbreak instances.

A short history of innate immunity

Innate immunity refers to the mechanisms responsible for the first line of defense against pathogens, cancer cells and toxins. The innate immune system is also responsible for the initial activation of the body's specific immune response (adaptive immunity). Innate immunity was studied and further developed in parallel with adaptive immunity beginning in the first half of the 19th century and has been gaining increasing importance to our understanding of health and disease. In the present overview, we describe the main findings and ideas that contributed to the development of innate immunity as a continually expanding branch of modern immunology. We start with the toxicological studies by Von Haller and Magendie, in the late 18th and early 19th centuries, and continue with the discoveries in invertebrate immunity that supported the discovery and characterization of lipopolysaccharide (LPS) and pattern recognition receptors that led to the development of the pattern recognition and danger theory.

Enhanced MANET security using artificial immune system based danger theory to detect selfish nodes

The dynamic distributed topology of a Mobile Ad Hoc Network (MANET) provides many challenges including decentralized infrastructure wherein each node acts as both the host and router for transiting traffic. MANET nodes provide the transmission capability to receive, transmit and route traffic from a sender node to the destination node. In this paper, we design an artificial immune system-based security method for MANET by simulating the mechanism of the human immune system. This paper presents a new bio inspired algorithm namely Artificial Immune System Based Algorithm (AISBA) to select selfish nodes in MANET, which utilizes principles of Artificial Immune Systems (AIS) that in turn mimics the strategy of the Human Immune System. Unlike Combined Immune Theories Algorithm, AISBA does not utilize a learning stage. In AISBA two different Trust models are developed as well to distinguish between a genuine node as well as a selfish node. An average detection rate of 93.41% was achieved with this bio inspired approach. The Simulation results demonstrating the superiority of the proposed scheme compared to the well known secured AODV (SAODV) protocol in terms of packet delivery ratio (about 87% of packet delivery ratio is achieved compared to about 37% in the case of SAODV) and about 94% of detection rate for the proposed scheme compared to 86% for SAODV in the presence of misbehaved nodes. The statistical analysis of the false positive probability is also conducted to assess the efficiency of the obtained results, showing promising results with respect to the effect of weight and trust on threshold.

Spam message detection using Danger theory and Krill herd optimization

Due to proliferation of online posts and rise in the active social media users, fraudulent activities related with spam messages have taken a spike drift. Spam is an activity by which hackers use electronic messaging system to unsolicited messages in mass content to unknown users. It can be also taken as one of the major attraction of attackers in the form of short message service (SMS) messages. Spam messages can be categorized in different categories such as business opportunity spam, trending topic spam, banking services spam etc. These problems can be tackled by confirming to the actions taken by users towards these messages. There is an urgent explicit need of practical medium in order to assist the users against these spam messages. This paper proposes a novel SMS spam filtering model based on Danger theory of Artificial Immune System (AIS). Several feature extraction and selection techniques have been applied for optimizing the algorithm and claiming an admissible accuracy. This paper uses a biologically inspired algorithm named Krill herd Optimization (KHO) for the task of feature selection and various optimization functions like Quing function, Sumsquare function, Levy function etc. are applied for enhancing its performance. The Dendritic Cell Algorithm (DCA) is also incorporated with KHA as an added advantage towards achieving efficiency. Comparative results between Dendritic Cell Algorithm (DCA) with KHA and other spam filtering models have been shown in comparison with several state-of-the-art machine learning classifiers. The algorithms have been experimented by using varied optimization functions illustrated using visualization tools and results have been validated in the paper. The obtained results demonstrate an admissible accuracy of 96% that is calculated using different information retrieval metrics using recall, F-measure and precision.

A novel sophisticated hybrid method for intrusion detection using the artificial immune system

Artificial immune system (AIS) as a good prototype for developing Machine Learning (ML) is a promising candidate to design Intrusion Detection System (IDS). Two of its prevalent paradigms, the negative selection, and the danger theory, inspired by immunity responses of the powerful human immune system (HIS) are being widely used in this case. In this paper, we proposed a novel sophisticated hybrid method including two defensive lines by using two aforesaid mechanisms. In the proposed method, decentralized cooperation of dendritic cells together with mature detectors acts as a stimulator to generate efficient and accurate detectors and retain memory in the long-term that meant security control via ensuring immunity. Simulating such a system was applicable only by establishing artificial life cycles in the MATLAB environment. Experimental consequences demonstrate that the present schema gradually ameliorates its overall performance during the incipient stages of life cycles and also it is more capable than the previous ones found in the literature.

Revisiting the “Danger Theory”: Toll-like Receptor 9 Stimulation Triggers Activation of Conventional CD8α+ and Plasmacytoid Dendritic Cells En Route to Enhancing FVIII Inhibitor Formation

Inhibitor formation is the most serious complication of FVIII replacement therapy for hemophilia A. The long-standing "danger theory" posits that inhibitors may form as part of collateral damage from immune response to a primary challenge such as an infection or vaccination. Innate immune signaling, for example through Toll-like receptors (TLRs), could be one way of triggering or reinforcing unwanted immune responses. However, the danger hypothesis has been contradicted by recent reports showing no increase in inhibitor formation in boys and animal models with hemophilia when FVIII was administered concurrently with vaccines. The aim of this study was to elucidate the influence of TLR9 signaling on FVIII inhibitor formation in hemophilia A mice. Hemophilia A (F8e16-/-) B6/129 mice were co-injected IV with FVIII (1.5 IU) and ODN-1826 (a class B CpG oligodeoxynucleotide, 50 µg), which is a TLR9 agonist. Control mice were naïve or received FVIII only. Blood samples and spleens were collected for Bethesda assay and flow cytometry analysis 3h, 24h, 2, 3 and 7 days or 2, 4, 6 and 8 weeks after a single or repeated once-weekly injections. After four weeks, mice co-injected with FVIII and ODN-1826 (n=4) showed ~15-fold higher inhibitor titers (median 2667 BU/mL) than mice injected with FVIII only (median 181.4 BU/mL; n=15). We also found significantly higher T follicular helper (Tfh; CD4+CXCR5+PD1+Bcl6―) [F (7, 96) = 9.801, p<0.0001] and Germinal Center (GC) B cell numbers (CD19+GL7+CD95+) [F (6, 92) = 11,53, p<0.0001] in the spleen after 4, 6 and 8 weeks of co-injections, with the Tfh numbers being 1.3-, 12.2- and 1.65-fold, while GC B cell numbers being 2.9-, 5.3- and 1.1-fold higher, respectively, compared to mice injected with FVIII only. GC B cell numbers correlated with inhibitor titers [r(31)=0.52, p=0.002]. We also investigated dendritic cell (DC) responses after a single injection of FVIII or co-injection with ODN-1826. We found increased numbers of monocyte-derived DCs (moDCs; CD11chighMHCII+CD11b+ CD64+MAR-1+) in the co-injected group at all time points (3, 24, 48, 72 hours and 1 week after a single injection; n=4-5 per group) with the peak number (~10-fold higher compared to naïve mice; p=0.0002) being reached 72 hours and remaining similarly elevated at 1 week. However, levels of activation markers CD86 and MHCII on moDCs in both injected groups were not significantly elevated. The moDC numbers were also ~2.5-fold higher in the FVIII only group than in naïve animals at one week post-administration, but the difference was not statistically significant. Conversely, exposure to FVIII with or without ODN-1826 did not significantly affect the number of CD8α+CD11b- DCs, but this subset showed >2-fold upregulation of both CD86 and MHCII in the co-injected group 3h and 24h after injection (n=5-11, p<0.01). We further found increased numbers of plasmacytoid DCs (pDCs; CD11c+CD11b―PDCA+) but the difference was significant only in the co-injected group at d1 and d7 after injection (~2-fold higher than the naïve group; n=4-16 per group, p<0.05). This DC subset also showed upregulated CD86 and MHCII levels ~2-fold in the co-injected group, with difference between CD86 levels showing significance (p=0.0024). We next examined an interferon signature in pDCs by intracellular cytokine staining and found that the cells from co-injected mice had ~2-fold higher levels of IFNβ (p=0.0001, n=5 per group). We propose that TLR9 stimulation enhances FVIII inhibitor formation in hemophilia A mice through recruitment and/or activation of conventional CD8α+ and plasmacytoid dendritic cells. The narrowing differences in Tfh and GC B cell numbers between the FVIII only and ODN-1826 co-injected groups at week 8 suggest that TLR9 stimulation accelerates GC formation in response to FVIII, which otherwise may reach a similar magnitude, but it takes longer. Also, our results suggest that it may be premature to put the danger theory to rest. The outcome of an immune challenge may vary depending on which innate immune receptor is concurrently stimulated. Notably, all vaccinations tested in the animal study showing no link to inhibitor formation were against pathogens with genomes composed of single-stranded RNA (measles, mumps, rubella and influenza viruses), which does not activate TLR9. Therefore, the impact of vaccination against single-stranded DNA pathogens that can stimulate TLR9, such as VZV, may be worth further investigation. Disclosures Herzog: Takeda Pharmaceuticals: Patents & Royalties.

An Efficient Snort NIDSaaS based on Danger Theory and Machine Learning,

An Efficient Snort NIDSaaS based on Danger Theory and Machine Learning,

Multi Agent Based Intrusion Detection System using Artificial Immune System for Distributed Network

To enhance the ability of intrusion detection system (IDS) with detection accuracy and low false positive rate, artificial immune system (AIS) based multi agent IDS is proposed that is inspired by human immune system (HIS). Presented IDS is designed four new algorithms these are mobile agent cloning algorithm, attribute selection algorithm based on danger theory which is filtering unnecessary attributes and giving most appropriate attributes for intrusion detection, detection algorithm based on negative selection technique is the prime algorithm which is detecting intrusions and rule algorithm is the simple and initial level algorithm to find normality or abnormality in the captured packet. Proposed IDS is the combination of misuse and anomaly IDS where architecture is the client server cum distributed IDS architecture. Proposed IDS reducing work load of network administrator and overcome network latency through automation and dynamic adaptation of mobile agent which is implement clonal selection concept of AIS. Proposed IDS is increasing efficiency and detection accuracy by using negative selection concept of AIS to design rule agent and informative agent. Another concept of AIS is danger theory is used to design packet capture & extract packet agent which is support to reduce execution time and improve efficiency. Database agent is used to maintain or update database (training, testing) dynamically during novel information identified. Finally alert agent responded to client host after intrusion detected for further action. These entire agents are the primary and most influential performer of the proposed IDS. Presented IDS identify and focused on most appropriate 13 attributes out of 41 of a packet to detect intrusion and proved that intrusion detection accuracy and intrusion detection rate increased with 0.2% and 0.1% respectively and reduce false positive rate with 0.5%.

Hybrid Multi-Level Detection and Mitigation of Clone Attacks in Mobile Wireless Sensor Network (MWSN).

Wireless sensor networks (WSNs) are often deployed in hostile environments, where an adversary can physically capture some of the sensor nodes. The adversary collects all the nodes’ important credentials and subsequently replicate the nodes, which may expose the network to a number of other security attacks, and eventually compromise the entire network. This harmful attack where a single or more nodes illegitimately claims an identity as replicas is known as the node replication attack. The problem of node replication attack can be further aggravated due to the mobile nature in WSN. In this paper, we propose an extended version of multi-level replica detection technique built on Danger Theory (DT), which utilizes a hybrid approach (centralized and distributed) to shield the mobile wireless sensor networks (MWSNs) from clone attacks. The danger theory concept depends on a multi-level of detections; first stage (highlights the danger zone (DZ) by checking the abnormal behavior of mobile nodes), second stage (battery check and random number) and third stage (inform about replica to other networks). The DT method performance is highlighted through security parameters such as false negative, energy, detection time, communication overhead and delay in detection. The proposed approach also demonstrates that the hybrid DT method is capable and successful in detecting and mitigating any malicious activities initiated by the replica. Nowadays, crimes are vastly increasing and it is crucial to modify the systems accordingly. Indeed, it is understood that the communication needs to be secured by keen observation at each level of detection. The simulation results show that the proposed approach overcomes the weaknesses of the previous and existing centralized and distributed approaches and enhances the performance of MWSN in terms of communication and memory overhead.

Tolerance to FVIII: Role of the Immune Metabolic Enzymes Indoleamine 2,3 Dyoxigenase-1 and Heme Oxygenase-1.

The occurrence of neutralizing anti-FVIII antibodies is a major complication in the treatment of patients affected by hemophilia A. The immune response to FVIII is a complex, multi-factorial process that has been extensively studied for the past two decades. The reasons why only a proportion of hemophilic patients treated with FVIII concentrates develop a clinically significant immune response is incompletely understood. The “danger theory” has been proposed as a possible explanation to interpret the findings of some observational clinical studies highlighting the possible detrimental impact of inflammatory stimuli at the time of replacement therapy on inhibitor development. The host immune system is often challenged to react to FVIII under steady state or inflammatory conditions (e.g., bleeding, infections) although fine tuning of mechanisms of immune tolerance can control this reactivity and promote long-term unresponsiveness to the therapeutically administered factor. Recent studies have provided evidence that multiple interactions involving central and peripheral mechanisms of tolerance are integrated by the host immune system with the environmental conditions at the time of FVIII exposure and influence the balance between immunity and tolerance to FVIII. Here we review evidences showing the involvement of two key immunoregulatory oxygenase enzymes (IDO1, HO-1) that have been studied in hemophilia patients and pre-clinical models, showing that the ability of the host immune system to induce such regulatory proteins under inflammatory conditions can play important roles in the balance between immunity and tolerance to exogenous FVIII.

Detekcija napada zasnovana na veštačkom imunom sistemu

Introduction/purpose: The artificial immune system is a computational model inspired by the biological or human immune system. Of particular interest in artificial immune systems is the way the human body reacts to new pathogens and adapts to remain immune for a long period after a disease has been combated, which refers to the recognition of known malicious attacks and the way the immune system identifies self-cells not to be reacted to, which refers to the anomaly detection. Methods: Negative selection, positive selection, clonal selection, immune networks, danger theory, and dendritic cell algorithm are presented. Results: A variety of algorithms and models related to artificial immune systems and two classification principles are presented; one based on the detection of a particular attack and the other based on anomaly detection. Conclusion: Artificial immune systems are often used in intrusion detection since they are accurate and fast. Experiments show that the models can be used in both known attack and anomaly detection. Eager machine learning classifiers show better results in the decision, which is an advantage if runtime is not a significant parameter. Dendritic cell and negative selection algorithms show better results for real-time detection.

Towards a Hybrid Immune Algorithm Based on Danger Theory for Database Security

In Databases, the most prevalent cause of data breaches comes from insiders who misuse their account privileges. Due to the difficulty of discovering such breaches, an adaptive, accurate, and proactive database security strategy is required. Intrusion detection systems are utilized to detect, as fast as possible, user's account privilege misuse when a prevention mechanism has failed to address such breaches. In order to address the foremost deficiencies of intrusion detection systems, artificial immune systems are used to tackle these defects. The dynamic and more complex nature of cybersecurity, as well as the high false positive rate and high false negative percentage in current intrusion detection systems, are examples of such deficiency. In this paper, we propose an adaptable efficient database intrusion detection algorithm based on a combination of the Danger Theory model and the Negative Selection algorithm from artificial immune system mechanisms. Experimental results for the implementation of the proposed algorithm provide a self-learning mechanism for achieving high detection coverage with a low false positive rate by using the signature of previously detected intrusions as detectors for the future detection process. The proposed algorithm can enhance detecting insider threats and eliminate data breaches by protecting confidentiality, ensuring integrity, and maintaining availability. To give an integrated picture, a comprehensive and informative survey for the different research directions that are related to the proposed algorithm is performed.

Problematic Node Identification with Negative Selection, Danger Theory and Clonal Selection using Source Based Immunization with Tag Scaling in Mobile Ad-Hoc Network

Problematic Node Identification with Negative Selection, Danger Theory and Clonal Selection using Source Based Immunization with Tag Scaling in Mobile Ad-Hoc Network

A Danger Theory Inspired Protection Approach for Hierarchical Wireless Sensor Networks

With the application of wireless sensor networks in the fields of ecological observation, defense military, architecture and urban management etc., the security problem is becoming more and more serious. Characteristics and constraint conditions of wireless sensor networks such as computing power, storage space and battery have brought huge challenges to protection research. Inspired by the danger theory in biological immune system, this paper proposes an intrusion detection model for wireless sensor networks. The model abstracts expressions of antigens and antibodies in wireless sensor networks, defines meanings and functions of danger signals and danger areas, and expounds the process of intrusion detection based on the danger theory. The model realizes the distributed deployment, and there is no need to arrange an instance at each sensor node. In addition, sensor nodes trigger danger signals according to their own environmental information, and do not need to communicate with other nodes, which saves resources. When danger is perceived, the model acquires the global knowledge through node cooperation, and can perform more accurate real-time intrusion detection. In this paper, the performance of the model is analyzed including complexity and efficiency, and experimental results show that the model has good detection performance and reduces energy consumption.

Intrusion Detection in Wireless Sensor Networks with an Improved NSA Based on Space Division

Inspired by the biological immune system, many researchers apply artificial immune principles to intrusion detection in wireless sensor networks, such as negative selection algorithms, danger theory, and dendritic cell algorithms. When applying the negative selection algorithm to wireless sensor networks, the characteristics of wireless sensor networks, such as frequent changes in network topology and limited resources, are not considered too much, which makes the detection effect to need improvement. In this paper, a negative selection algorithm based on spatial partition is proposed and applied to hierarchical wireless sensor networks. The algorithm first analyzes the distribution of self-set in the real-valued space then divides the real-valued space, and several subspaces are obtained. Selves are filled into different subspaces. We implement the negative selection algorithm in the subspace. The randomly generated candidate detector only needs to be tolerated with selves in the subspace where the detector is located, not all the selves. This operation reduces the time cost of distance calculation. In the detection process of detectors, the antigen which is to be detected only needs to match the mature detectors in the subspace where the antigen is located, rather than all the detectors. This operation speeds up the antigen detection process. Theoretical analysis and experimental results show that the algorithm has better time efficiency and quality of detectors, saves sensor node resources and reduces the energy consumption, and is an effective algorithm for wireless sensor network intrusion detection.

Danger theory inspired micro-population immune optimization for probabilistic constrained programming

This work solves the problem of a general kind of single-objective probabilistic constrained programming without any a prior stochastic distribution information, after probing into an adaptive sampling-based micro immune optimization approach inspired by the danger theory in immunology. In the whole design of the algorithm, the current population is divided into uninfected, susceptible and infected sub-populations based on the version of individuals’ dominance, relying upon the schemes of sample-dependent constraint handling and objective evaluation. Those uninfected and susceptible sub-populations proliferate their clones and execute adaptive mutation with small variable mutation rates, whereas the infected sub-population directly participates in mutation at a large and variable mutation rate. Two mutation strategies, together with the version of life cycle on individual, are simultaneously designed to evolve those sub-populations along different directions in order to explore those diverse and high-quality solutions. It is shown that the complexity of the algorithm depends mainly on the number of iterations. Experimental results have validated that one such approach is a competitive and potential optimizer because of few parameters, fast convergence and its ability of effective noise suppression.

Modeling an Intrusion Detection System Based on Adaptive Immunology

Network security has always has been an area of priority and extensive research. Recent years have seen a considerable growth in experimenting with biologically inspired techniques. This is a consequence of the authors increased understanding of living systems and the application of that understanding to machines and software. The mounting complexity of telecommunications networks and the need for increasing levels of security have been the driving factors. The human body can act as a great role model for its unique abilities in protecting itself from external entities owing to its diverse complexities. Many abnormalities in the human body are similar to that of the attacks in wireless sensor networks (WSN). This article presents the basic ideas that can help modelling a system to counter the attacks on a WSN by monitoring parameters such as energy, frequency of data transfer, data sent and received. This is implemented by exploiting an immune concept called danger theory, which aggregates the anomalies based on the weights of the anomalous parameters. The objective is to design a cooperative intrusion detection system (IDS) based on danger theory.

Mimicry Detection Method Based on Immune Danger Theory

Considering that the traditional detector has lower detection rate, resource utilization and higher false alarm rate, this paper proposes a mimicry algorithm and a mimicry detector based on Immune Danger Theory (IDTMD). The detector adjusts the types and number of detection organizations and detection cells dynamically, according to the types and number of the detected signal and the detected dangerous signal, as to realize the dynamic, diversity and randomness of mimicry detection. The KDD-CUP99 dataset was used to test the comprehensive detection performance of the IDTMD detector, the detector based on Negative Selection Algorithm (NSA), the detector based on Dendritic Cell Algorithm (DAC). The results show that the detector improves the detection rate and the resource utilization, decreases the false alarm rate, and when testing amount reaches a certain scale, its response time is significantly less than other detectors’.