Shopping Cart System: Load Balancing and Fault Tolerance in the OSGi Service Platform

The main purpose of this paper was to find a simple solution for load balancing and fault tolerance in OSGi. The challenge was to implement a highly available web application such as a shopping cart system with load balancing and fault tolerance, without having to change the core of OSGi.

Thermal Tolerance of the Coffee Berry Borer Hypothenemus hampei: Predictions of Climate Change Impact on a Tropical Insect Pest

Coffee is predicted to be severely affected by climate change. We determined the thermal tolerance of the coffee berry borer, Hypothenemus hampei, the most devastating pest of coffee worldwide, and make inferences on the possible effects of climate change using climatic data from Colombia, Kenya, Tanzania, and Ethiopia. For this, the effect of eight temperature regimes (15, 20, 23, 25, 27, 30, 33 and 35 degrees C) on the bionomics of H. hampei was studied. Successful egg to adult development occurred between 20-30 degrees C. Using linear regression and a modified Logan model, the lower and upper thresholds for development were estimated at 14.9 and 32 degrees C, respectively. In Kenya and Colombia, the number of pest generations per year was considerably and positively correlated with the warming tolerance. Analysing 32 years of climatic data from Jimma (Ethiopia) revealed that before 1984 it was too cold for H. hampei to complete even one generation per year, but thereafter, because of rising temperatures in the area, 1-2 generations per year/coffee season could be completed. Calculated data on warming tolerance and thermal safety margins of H. hampei for the three East African locations showed considerably high variability compared to the Colombian site. The model indicates that for every 1 degrees C rise in thermal optimum (T(opt)), the maximum intrinsic rate of increase (r(max)) will increase by an average of 8.5%. The effects of climate change on the further range of H. hampei distribution and possible adaption strategies are discussed. Abstracts in Spanish and French are provided as supplementary material Abstract S1 and Abstract S2.

'Danger Theory: The Link between AIS and IDS?'

Abstract We present ideas about creating a next generation Intrusion Detection System (IDS) based on the latest immunological theories. The central challenge with computer security is determining the difference between normal and potentially harmful activity. For half a century, developers have protected their systems by coding rules that identify and block specific events. However, the nature of current and future threats in conjunction with ever larger IT systems urgently requires the development of automated and adaptive defensive tools. A promising solution is emerging in the form of Artificial Immune Systems (AIS): The Human Immune System (HIS) can detect and defend against harmful and previously unseen invaders, so can we not build a similar Intrusion Detection System (IDS) for our computers? Presumably, those systems would then have the same beneficial properties as HIS like error tolerance, adaptation and self-monitoring. Current AIS have been successful on test systems, but the algorithms rely on self-nonself discrimination, as stipulated in classical immunology. However, immunologist are increasingly finding fault with traditional self-nonself thinking and a new 'Danger Theory' (DT) is emerging. This new theory suggests that the immune system reacts to threats based on the correlation of various (danger) signals and it provides a method of 'grounding' the immune response, i.e. linking it directly to the attacker. Little is currently understood of the precise nature and correlation of these signals and the theory is a topic of hot debate. It is the aim of this research to investigate this correlation and to translate the DT into the realms of computer security, thereby creating AIS that are no longer limited by self-nonself discrimination. It should be noted that we do not intend to defend this controversial theory per se, although as a deliverable this project will add to the body of knowledge in this area. Rather we are interested in its merits for scaling up AIS applications by overcoming self-nonself discrimination problems.

Detecting Danger: Applying a Novel Immunological Concept to Intrusion Detection Systems'

INTRODUCTION In recent years computer systems have become increasingly complex and consequently the challenge of protecting these systems has become increasingly difficult. Various techniques have been implemented to counteract the misuse of computer systems in the form of firewalls, antivirus software and intrusion detection systems. The complexity of networks and dynamic nature of computer systems leaves current methods with significant room for improvement. Computer scientists have recently drawn inspiration from mechanisms found in biological systems and, in the context of computer security, have focused on the human immune system (HIS). The human immune system provides an example of a robust, distributed system that provides a high level of protection from constant attacks. By examining the precise mechanisms of the human immune system, it is hoped the paradigm will improve the performance of real intrusion detection systems. This paper presents an introduction to recent developments in the field of immunology. It discusses the incorporation of a novel immunological paradigm, Danger Theory, and how this concept is inspiring artificial immune systems (AIS). Applications within the context of computer security are outlined drawing direct reference to the underlying principles of Danger Theory and finally, the current state of intrusion detection systems is discussed and improvements suggested.

'Introducing Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomaly Detection'

Abstract. Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system. Research into this family of cells has revealed that they perform the role of coordinating T-cell based immune responses, both reactive and for generating tolerance. We have derived an algorithm based on the functionality of these cells, and have used the signals and differentiation pathways to build a control mechanism for an artificial immune system. We present our algorithmic details in addition to some preliminary results, where the algorithm was applied for the purpose of anomaly detection. We hope that this algorithm will eventually become the key component within a large, distributed immune system, based on sound immunological concepts.

'Cooperative Automated worm Response and Detection ImmuNe ALgorithm (CARDINAL) inspired by T-cell Immunity and Tolerance'

The role of T-cells within the immune system is to confirm and assess anomalous situations and then either respond to or tolerate the source of the effect. To illustrate how these mechanisms can be harnessed to solve real-world problems, we present the blueprint of a T-cell inspired algorithm for computer security worm detection. We show how the three central T-cell processes, namely T-cell maturation, differentiation and proliferation, naturally map into this domain and further illustrate how such an algorithm fits into a complete immune inspired computer security system and framework.

The tolerance of steers (Bos taurus) to sorghum ergot (Claviceps africana) in a feedlot during the cooler months in sub-tropical Queensland

Two experiments tested the tolerance of steers (Bos taurus) to sorghum ergot (Claviceps africana) during cooler months in south-east Queensland. Sorghum grain containing 2.8% ergot and 28 mg/kg ergot alkaloids (84% dihydroergosine, 10% dihydroelymoclavine, 6% festuclavine) was incorporated into feedlot rations. In a previous study in summer–autumn, ergot (1.1–4.4 mg alkaloids/kg ration) severely reduced performance in steers when the temperature–humidity index (THI; dry bulb temperature °C + 0.36 dew-point temperature °C + 41.2) was ~70, whereas a THI of ~79 was tolerated by steers fed ergot-free rations. Experiment 1 was conducted in winter–spring, with rations containing 0, 2.8, 5.6, 8.2 or 11.2 mg ergot alkaloids/kg ration. All ergot inclusions depressed feed intake (14% average reduction) and growth rate (34% average reduction), even when the weekly average daily THI was less than 65. Rectal temperatures were occasionally elevated in ergot-fed steers (P < 0.05), primarily when the THI exceeded ~65. All ergot inclusions depressed plasma prolactin concentrations in steers. Experiment 2 was predominantly carried out in winter, with weekly average daily THI <65 throughout the experiment. Rations containing 0, 0.28, 0.55 or 1.1 mg ergot alkaloids/kg were fed for 4 weeks but produced no significant effect on feed intakes and growth rates of steers. Alkaloid concentrations were then changed to 0, 2.1, 4.3 and 1.1 mg/kg, respectively. Subsequently, feed intakes declined by 17.5% (P < 0.05), and growth rates by 28% (P > 0.05) in the group receiving 4.3 mg/kg alkaloid, compared with Controls. Plasma prolactin concentrations were depressed, relative to the Controls, by dietary alkaloid inclusion greater than 1.1 mg/kg, with alkaloid intake of 4.3 mg/kg causing the greatest reduction (P < 0.05). Cattle performance in these studies shows steers can tolerate up to ~2 mg ergot alkaloid/kg (0.2% ergot) in feedlot rations under low THI conditions (< ~60–65), but previous findings indicate a much lower threshold will apply at higher THI (>65).

The tolerance of steers (Bos taurus) to sorghum ergot (Claviceps africana) in a feedlot during the cooler months in sub-tropical Queensland

Two experiments tested the tolerance of steers (Bos taurus) to sorghum ergot (Claviceps africana) during cooler months in south-east Queensland. Sorghum grain containing 2.8% ergot and 28 mg/kg ergot alkaloids (84% dihydroergosine, 10% dihydroelymoclavine, 6% festuclavine) was incorporated into feedlot rations. In a previous study in summer–autumn, ergot (1.1–4.4 mg alkaloids/kg ration) severely reduced performance in steers when the temperature–humidity index (THI; dry bulb temperature °C + 0.36 dew-point temperature °C + 41.2) was ~70, whereas a THI of ~79 was tolerated by steers fed ergot-free rations. Experiment 1 was conducted in winter–spring, with rations containing 0, 2.8, 5.6, 8.2 or 11.2 mg ergot alkaloids/kg ration. All ergot inclusions depressed feed intake (14% average reduction) and growth rate (34% average reduction), even when the weekly average daily THI was less than 65. Rectal temperatures were occasionally elevated in ergot-fed steers (P < 0.05), primarily when the THI exceeded ~65. All ergot inclusions depressed plasma prolactin concentrations in steers. Experiment 2 was predominantly carried out in winter, with weekly average daily THI <65 throughout the experiment. Rations containing 0, 0.28, 0.55 or 1.1 mg ergot alkaloids/kg were fed for 4 weeks but produced no significant effect on feed intakes and growth rates of steers. Alkaloid concentrations were then changed to 0, 2.1, 4.3 and 1.1 mg/kg, respectively. Subsequently, feed intakes declined by 17.5% (P < 0.05), and growth rates by 28% (P > 0.05) in the group receiving 4.3 mg/kg alkaloid, compared with Controls. Plasma prolactin concentrations were depressed, relative to the Controls, by dietary alkaloid inclusion greater than 1.1 mg/kg, with alkaloid intake of 4.3 mg/kg causing the greatest reduction (P < 0.05). Cattle performance in these studies shows steers can tolerate up to ~2 mg ergot alkaloid/kg (0.2% ergot) in feedlot rations under low THI conditions (< ~60–65), but previous findings indicate a much lower threshold will apply at higher THI (>65).

Modelling Immunological Memory

Accurate immunological models offer the possibility of performing highthroughput experiments in silico that can predict, or at least suggest, in vivo phenomena. In this chapter, we compare various models of immunological memory. We first validate an experimental immunological simulator, developed by the authors, by simulating several theories of immunological memory with known results. We then use the same system to evaluate the predicted effects of a theory of immunological memory. The resulting model has not been explored before in artificial immune systems research, and we compare the simulated in silico output with in vivo measurements. Although the theory appears valid, we suggest that there are a common set of reasons why immunological memory models are a useful support tool; not conclusive in themselves.

'Cooperative Automated worm Response and Detection ImmuNe ALgorithm (CARDINAL) inspired by T-cell Immunity and Tolerance'

The role of T-cells within the immune system is to confirm and assess anomalous situations and then either respond to or tolerate the source of the effect. To illustrate how these mechanisms can be harnessed to solve real-world problems, we present the blueprint of a T-cell inspired algorithm for computer security worm detection. We show how the three central T-cell processes, namely T-cell maturation, differentiation and proliferation, naturally map into this domain and further illustrate how such an algorithm fits into a complete immune inspired computer security system and framework.

Detecting Danger: Applying a Novel Immunological Concept to Intrusion Detection Systems'

INTRODUCTION In recent years computer systems have become increasingly complex and consequently the challenge of protecting these systems has become increasingly difficult. Various techniques have been implemented to counteract the misuse of computer systems in the form of firewalls, antivirus software and intrusion detection systems. The complexity of networks and dynamic nature of computer systems leaves current methods with significant room for improvement. Computer scientists have recently drawn inspiration from mechanisms found in biological systems and, in the context of computer security, have focused on the human immune system (HIS). The human immune system provides an example of a robust, distributed system that provides a high level of protection from constant attacks. By examining the precise mechanisms of the human immune system, it is hoped the paradigm will improve the performance of real intrusion detection systems. This paper presents an introduction to recent developments in the field of immunology. It discusses the incorporation of a novel immunological paradigm, Danger Theory, and how this concept is inspiring artificial immune systems (AIS). Applications within the context of computer security are outlined drawing direct reference to the underlying principles of Danger Theory and finally, the current state of intrusion detection systems is discussed and improvements suggested.

'Introducing Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomaly Detection'

Abstract. Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system. Research into this family of cells has revealed that they perform the role of coordinating T-cell based immune responses, both reactive and for generating tolerance. We have derived an algorithm based on the functionality of these cells, and have used the signals and differentiation pathways to build a control mechanism for an artificial immune system. We present our algorithmic details in addition to some preliminary results, where the algorithm was applied for the purpose of anomaly detection. We hope that this algorithm will eventually become the key component within a large, distributed immune system, based on sound immunological concepts.

'Danger Theory: The Link between AIS and IDS?'

Abstract We present ideas about creating a next generation Intrusion Detection System (IDS) based on the latest immunological theories. The central challenge with computer security is determining the difference between normal and potentially harmful activity. For half a century, developers have protected their systems by coding rules that identify and block specific events. However, the nature of current and future threats in conjunction with ever larger IT systems urgently requires the development of automated and adaptive defensive tools. A promising solution is emerging in the form of Artificial Immune Systems (AIS): The Human Immune System (HIS) can detect and defend against harmful and previously unseen invaders, so can we not build a similar Intrusion Detection System (IDS) for our computers? Presumably, those systems would then have the same beneficial properties as HIS like error tolerance, adaptation and self-monitoring. Current AIS have been successful on test systems, but the algorithms rely on self-nonself discrimination, as stipulated in classical immunology. However, immunologist are increasingly finding fault with traditional self-nonself thinking and a new 'Danger Theory' (DT) is emerging. This new theory suggests that the immune system reacts to threats based on the correlation of various (danger) signals and it provides a method of 'grounding' the immune response, i.e. linking it directly to the attacker. Little is currently understood of the precise nature and correlation of these signals and the theory is a topic of hot debate. It is the aim of this research to investigate this correlation and to translate the DT into the realms of computer security, thereby creating AIS that are no longer limited by self-nonself discrimination. It should be noted that we do not intend to defend this controversial theory per se, although as a deliverable this project will add to the body of knowledge in this area. Rather we are interested in its merits for scaling up AIS applications by overcoming self-nonself discrimination problems.

Pathophysiological and immunological studies of bovine trypanosomiasis

No abstract available. General introduction on p. 1-10.