Dynamism: The HIS

The dynamism of the immune system and its cells is governed by its utility measurement process – affinity. Each cell and cell type within the HIS has been trained to provide cover for a sufficient portion of the self-space, expressing affinity towards different structures of foreign elements.

When stimulation occurs due to the presence of a foreign element, a cell attempts to bind to the foreign element by applying an approximation approach, referred to as “affinity”

(Samanta, 2010; Silva, Caminhas & Palhares, 2017). During the affinity approach, an approximate binding takes place between a cell and a foreign element, enabling a cell to detect and analyse a foreign element without the need to know the exact nature of the foreign element (Palm & Medzhitov, 2009; Silva et al., 2017; Sompayrac, 2015). This process also enables a smaller set of cells to be maintained, whereby different cells can express different affinities to foreign elements, enabling minimum overlap between detection and recognition capabilities within the HIS (Dasgupta & Dasgupta, 2017; Sompayrac, 2015).

Figure 4.3 illustrates two different cells binding to a foreign element. The affinity utility measurement calculates the ability of a portion of the cells’ endpoints (called epitopes), that can bind to the endpoints of the foreign elements. If a sufficient number of endpoints

cannot bind to the foreign element, the cell is not able to analyse the foreign element (de Alwis, Smith, Olivarez, Messer, Huynh, Wahala & de Silva, 2012; Dasgupta & Nino, 2008;

Parham, 2014).

One potential caveat of this approach (which is addressed in the CESIMAS model), is the fact that mutations of foreign elements can easily occur, which might make it difficult for cells to bind to and recognise these elements (Leung, Tarasenko, Biesova, Kole, Walsh & Bolland, 2012; Poulsen, Jensen, Haurum & Andersen, 2011; Sompayrac, 2015). This is analogous to a zero-day exploit, which was discussed in sections 1.2 and 2.4.2.3.

For an immune system to continually evolve, remain effective, and adapt to the ever-changing environment, Affinity Maturation is introduced to ensure that cell proliferation occurs (Dasgupta, Yu & Nino, 2011). Affinity Maturation enables cells that are stimulated (either by other cells or by the presence of foreign elements) to proliferate and remain active for longer periods of time (Adhikary, Yu, Oda, Walker, Chen, Stanfield, Wilson, Zimmerman & Romesberg, 2015; Sompayrac, 2015; Tas, Mesin, Pasqual, Targ, Jacobsen, Mano, Chen, Weill, Reynaud, Browne & Meyer-Hermann, 2016). This enables these cells to produce more “memory-driven” cells to effectively “save” the knowledge obtained by the immune system itself (Neu & Wilson, 2016; Sompayrac, 2015). Figure 4.3 shows two cells with different affinities binding to a foreign element.

55 | P a g e Figure 4.3: Two cells with different affinities bind to a foreign element (Dasgupta & Nino,

2008)

The dynamism of the immune system accounts for the destructions of cells that are not stimulated through a process called apoptosis, or cell suicide (Doulatov, Notta, Laurenti &

Dick, 2012; Sompayrac, 2015). Cells that aren’t stimulated after a threshold of time are effectively not needed. These cells are replaced by naïve cells, mentioned at the start of section 4.1, and the maturation process commences once again (Dasgupta & Nino, 2008;

Parham, 2014; Sompayrac, 2015). Conversely, the cells that remain in circulation due to continual stimulation also undergo a transformation to prevent the stagnation of the population of cells with regard to the HIS detection capabilities.

Somatic hypermutation introduces mutations into the population of stimulated cells to avoid the stagnation of the affinity maturation process. These variations might be subtle in effect, but over time they ensure the continual proliferation of the HIS as a whole (Reed, Jackson, Christ & Goodnow, 2016; Stern, O’Connor, Hafler, Laserson, Vigneault & Kleinstein, 2015; Wang, Sen, Zhang, Ahmad, Zhu, Wilson & Schultz, 2013).

Similar to the dynamic threats of CIIP, as mentioned previously, the HIS must account for dynamic threats. This is made possible via the slight variations introduced at random intervals.

The immune system is a large, distributed system that is very effective and efficient. Each cell can have a different affinity towards foreign elements, making it efficient regarding memory and time, as depicted in Figure 4.4. To improve the response time of the HIS over time, the three immune layers improve over time as the cells proliferate and mature

(Dasgupta & Dasgupta, 2017; Dasgupta & Nino, 2008; Parham, 2014; Sompayrac, 2015). This continual feedback loop ensures that knowledge is maintained, that there is no stagnation and that responses to foreign elements improve over time.

56 | P a g e Different cell types can also undergo proliferation through various processes. This is

discussed later in this section. Section 4.3 addresses the communication that occurs within the immune system. Figure 4.4 shows the immune response efficiency over time.

Figure 4.4: Immune Response Efficiency Over Time (Dasgupta & Nino, 2008) 4.3 Effective and Efficient Communication within the HIS

Similar to CIIP, the HIS operates in a dynamic environment, where the only constant element is change. The immune system can be exposed daily to a range of foreign and malicious elements, and as such, it requires the ability to communicate and issue immune responses efficiently. In Chapter 3, it was mentioned that agents operate in a dynamic environment where they have to deliberate and make decisions with incomplete and inconsistent data. The same holds true for the HIS, but it has adapted to this requirement over millenniums.

Section 4.1 discussed the way cells bind to foreign elements to analyse them, with the intention of executing an immune response. To conveythe information captured by a cell when it is stimulated, one of two approaches can be taken within the HIS (Dasgupta & Nino, 2008; Folcik, Broderick, Mohan, Block, Ekbote, Doolittle & Marsh, 2011; Goeree & Yariv, 2011; Hancock, 2017; Parham, 2014):

• Immune diffusion – a one-way communication pathway where a cell can transmit information to another cell; and

• Immune Dialogue – a two-way communication pathway whereby cells can exchange information.

The ability to use signalling plays an important role in inter-cell communications. Signalling enables a cell to communicate or flag changes in the environment post analysis of a foreign

57 | P a g e element. A cell can initiate a response from another layer in the immune system as a result of cell-signaling. Communications between cells pre, during and post response to a foreign element is made possible, and it promotes and assists differentiation, proliferation and knowledge retention of cells throughout the system (Dasgupta & Nino, 2008; Hancock, 2017; Hart, 2016; Hasson, 1997; Parham, 2014; Sompayrac, 2015).

Within the CIIP solution that is presented from Chapter 6 onwards, inter-agent

communication and signalling plays a pivotal role in the way in which the system responds to the presence of foreign elements. Any solution that is distributed in nature requires the ability to perform effective and efficient communication with other elements in the solution. The HIS establishes the ideal analogies to realise this, within a distributed environment.

Section 4.4 discusses some of the theoretical models of the immune system that have interesting analogies for CIIP.

4.4 Immune Inspired Models, Processes and Algorithms

Within the dynamism that constitutes the HIS, various processes, components, and elements all work in a complementary manner to achieve the desired state of utility.

Numerous processes occur ubiquitously within the immune system that consist of some ideal characteristics that can be applied to the problem domain of CIIP. The rest of section 4.4 highlights some of these ideal processes, models, and algorithms, elaborating on their dynamics and applicability to CIIP. The list is by no means exhaustive but is intended to convey some of the ideal analogies which can be drawn between the HIS and CIIP.

4.4.1 Clonal Selection Theory (CST)

During the stimulation process, cell proliferation occurs when a cell binds to a foreign

element, as depicted in Figure 4.3. Once a cell is stimulated, the cell produces copies of itself that are dispersed throughout the rest of the immune system, effectively improving the affinity of the system towards the foreign element (Atlan & Cohen, 2012; Hancock, 2017;

Purbasari, Iping, Santoso & Mandala, 2013; Sompayrac, 2015; Steele, 2017). CST is depicted in Figure 4.5.

The purpose of cell proliferation in this case is twofold. Firstly, it enables copies of cells to migrate throughout the rest of the immune system to remove elements which might be similar. Secondly, it allows cells that are not stimulated within a specified period to be removed from the system and replaced with a naïve cell (Gálvez, Iglesias, Avila, Otero, Arias

& Manchado, 2015; Sompayrac, 2015; Steele, 2017).

CST creates an ideal analogy for a CIIP solution that operates within a dynamic and complex environment. Applying CST to the detection process can potentially enable the solution to retain the “most stimulated” elements to ensure that the same attempted exploitation does not occur within a small time-frame. It also enables knowledge about the attempted

58 | P a g e exploitation to be retained and instilled in new elements being deployed into the CIIP environment. Figure 4.5 depicts the clonal selection theory.

Figure 4.5: Clonal Selection Theory (Dasgupta & Nino, 2008) 4.4.2 Immune Network Theory (INT)

In contrast to CST, which relies on foreign elements to stimulate cells, IN establishes a model that realises those elements within a system that can interact without the need for foreign elements to be present (Dasgupta & Nino, 2008; Rucco, Castiglione, Merelli &

Pettini, 2016).

Cells under the INT can communicate, stimulate, and influence one another with or without antigens in the immune system. Initial INT processes focus purely on cell interactions, without considering the absence or presence of foreign elements or the type of cell that is causing the influence (Hoffmann, 2008; Perelson, 1989; Rucco et al., 2016; Silva & Dasgupta, 2016). This model was elaborated upon by considering each cell’s autonomous behaviour in its natural state and environment. Cell-to-cell level influence should not be excessive or few and far between. Obtaining a careful balance ensures that cells do not meet an early death, as this would impact the overall utility of the immune system (Hoffmann, 2008; Perelson, 1989; Silva & Dasgupta, 2016).

INT analogies indicate that some level of interaction and knowledge sharing should occur within a CIIP solution. Specifically, the influence should be well balanced to ensure that continual global proliferation occurs.

INT creates an ideal analogy for a CIIP solution that operates within a dynamic and complex environment. Applying CST to the detection process can potentially enable the solution to

59 | P a g e retain the “most stimulated” elements to ensure that the same attempted exploitation does not occur within a small time-frame. It also enables knowledge about the attempted

exploitation to be retained and instilled in new elements being deployed into the CIIP environment.

4.4.3 Danger Theory (DT)

The HIS relies on the concept of “self” (as discussed in Chapter 1) to discriminate between elements which should be present within the immune system and those elements which should not. Figure 4.6 depicts a visualisation of the Self/Non-self (SNS) space. SNS enables the HIS to learn and evolve, preventing self-destruction and auto-immune diseases

(Greensmith, Whitbrook & Aickelin, 2010; Silva & Dasgupta, 2016; Wing & Sakaguchi, 2009).

Danger Theory (DT) elaborates on SNS by taking into considerations that different types of stimuli can occur at various stages within the HIS (Aickelin & Cayzer, 2008; Dasgupta & Nino, 2008; Silva & Dasgupta, 2016; Tan, 2016). Within DT the notion representing the self is slightly relaxed to accommodate non-self-elements that are of a non-malicious nature (Dasgupta & Nino, 2008; Sompayrac, 2015). SNS discrimination only occurs between non-harmful elements and non-harmful elements. This also contributes to the continual proliferation of the HIS holistically, as over time the HIS might adapt to include elements not originally considered to be part of the self (Degeler, French & Jones, 2015; Mohsin, Bakar & Hamdan, 2014; Pradeu & Cooper, 2012; Silva & Dasgupta, 2016; Tan, 2016).

DT consists of some ideal characteristics which can be applied to CIIP. CIIP occurs in a dynamic environment where elements such as BYOT environments can introduce into the environment new elements which might not be of a malicious nature, yet there might be a business requirement to keep these “non-self” elements active within the CII. DT is ideal in this scenario, as it utilises a relaxed notion of SNS and enables danger signals to be sent whenever any element tries to affect or infect another element within the system (Aickelin

& Cayzer, 2008; Dasgupta & Nino, 2008; Tan, 2016; Zhang & Tan, 2015). This also promotes continual proliferation of the HIS (and a potential CIIP solution), as any system that offers protection is never fully tolerant to all threats and vulnerabilities. Figure 4.6 illustrates a visualisation of the SNS space.

60 | P a g e Figure 4.6: Visualisation of the SNS space (Dasgupta & Nino, 2008)

4.4.4 T-cell Inspired Algorithms

T-cell inspired algorithms are utilised during the production process of naïve cells in the HIS.

Each naïve cell that is produced undergoes rigorous testing to ensure that the cell can detect foreign elements and to make sure that the cell is not self-reactive. Each naïve cell is instilled with “random” knowledge and know-how about the HIS, before being put through two processes to enforce “quality assurance” prior to its release into the HIS.

Firstly, the cell is put through a process called Positive Selection (PS), where it is tested against its detection capabilities of non-self elements. Secondly, the cell is put through Negative Selection (NS), to ensure that the cell is not self-reactive (Dasgupta et al., 2011;

Tan, 2016). It is crucial to prevent cells from attacking other cells to prevent an

“auto-immune disease” analogy (Davidson & Diamond, 2001; Grant, Liberal, Mieli-Vergani, Vergani

& Longhi, 2015; Perez-Alvarez, Pérez-de-Lis, Ramos-Casals & BIOGEAS Study Group, 2013).

Section 4.5 discusses why the immune system has some ideal characteristics from a computational point of view.

4.5 Computational Effectiveness and Efficiency

The immune system and CIIP share a common set of requirements. Both require

computational efficiency and effectiveness in real-time. The HIS contains various desirable characteristics that can be useful if they are to be implemented within a CIIP solution, as it consistently attempts to perform regulation, healing, improvement, and self-monitoring. Figure 4.4 depicted a glimpse of the efficiency that is instilled within the HIS.

The following list shows beneficial characteristics within the context of a CIIP solution (Aickelin et al., 2014; Chi, Pedrielli, Kister, Ng & Bressan, 2015; Dasgupta & Nino, 2008;

Dasgupta et al., 2011; De Castro & Timmis, 2002; Sompayrac, 2015; Tan, 2016):

61 | P a g e

• Diversity – the continual naïve cell generation process, assisted by Clonal Selection and a mutation process, prevent the system from stagnating with regard to the utility. Cells that are unstimulated are removed and replaced with naïve cells which are instilled with the “latest” knowledge that the system has;

• Self-Regulation – cells are continually performing introspection to ensure that they remain effective and efficient. Should a cell not be stimulated for a specific amount of time, the cell emits a signal that terminates it. The cell is then be replaced with a new naïve cell;

• Distributed Control and Processing – cells are found throughout the system (body).

Each cell strives to reach its local maximum utility, while the immune system as a whole strives towards a global maximum utility. There is no centre of control for the cells, enabling distributed problem-solving capabilities to become a reality;

• Protection – cells operate collaboratively to share knowledge and communicate about any potential threats. The distributed approach associated with the HIS also prevents all the cells from being infected at the same time;

• Knowledge Retention – cells that are stimulated retain their knowledge in the form of other cells or copies of themselves. This ensures that naïve cells are instilled with a continually adapting and evolving set of knowledge of the self-space; and

• Approximate Matching – cell binding occurs by applying an approximate binding technique whereby a limited number of connections should be made to a foreign element before the cell can analyse it. This affinity threshold enables multiple cells to potentially bind to and analyse foreign elements.

Chapter 4 has discussed AIS, analogies and desirable characteristics. Section 4.6 concludes the chapter by revisiting the research questions of the chapter and summarising the discussed elements.

4.6 Conclusion

Chapter 4 was dedicated to discussing the AIS and the analogies that can be created from the HIS. To contextualise the operations that occur within the HIS, details were provided on components, processes and elements that form part of the typical immune system.

The dynamism of the HIS was discussed at length, providing details on the effectiveness and efficiency of the HIS. Due to the continual self-regulation processes that occur within the HIS, it can improve continually and maintain a level of utility that increases and improves over time. This also enables the immune system to respond to malicious elements more effectively, as cells that are stimulated retain their knowledge. These characteristics are suitable and desirable in the field of CIIP.

Some of the models, theories and algorithms pertaining to AISs or HISs were discussed and analogised to the problem domain of CIIP. The approaches listed provide sufficient context to the potential dynamics that can be utilised within a CIIP solution.

62 | P a g e Chapter 4 outlined three research questions which served as the general outline:

Research Questions

RQ 4.1 What are the functional components and processes of an AIS?

RQ 4.2 How does an AIS process large volumes of information? How does this assist communication in a distributed environment?

RQ 4.3 How can the dynamism of AISs assist in the process of ensuring effective and efficient CIIP within an organisation, and how will this contribute to the CESIMAS model?

RQ 4.1:What are the functional components and processes of an AIS and how does it work?

Section 4.1 discussed the functional components of the immune system. At a fundamental level, the immune system is a dynamic system that generates naïve cells. Prior to being tested, these naïve cells are trained with sub-sets of data representing the self. During the testing process, the cells need to pass two tests. Firstly, the cell needs to be able to bind to foreign elements through PS. Secondly, NS determines if the cell is self-reactive or not. Once both tests are passed, the cell is sent into the active immune system to perform its duties.

There are various types of cells within the immune system, each performing a complementary role which is geared towards maintaining overall health. All the cells operate within the three immune system layers, which were mentioned in section 4.1.

These protection layers each have a specific role and each one acts as another line of defence.

Section 4.2 discussed the dynamism that occurs within an immune system at a cell level.

When a foreign element is detected, a cell attempts to perform an approximate binding to the foreign element. This approximation enables various cells to express different affinities towards multiple foreign elements, minimising the requirement for the number of cells. The stimulated cells proliferate and make copies of themselves to prevent similar foreign

elements from affecting other parts of the immune system.

The immune system is an autonomous, self-sufficient, self-regulating, self-monitoring and self-healing system that creates the ideal analogy for CIIP.

RQ 4.2: How does an AIS process large volumes of information? How does this assist communication in a distributed environment?

The immune system utilises approximate binding techniques that enable cells to bind to a multitude of foreign elements known and unknown. As discussed in sections 4.2 and 4.3, once a cell is stimulated through it binding to a foreign element, the cell proliferates. During this stimulation process, the cell can communicate the information obtained to other cells

63 | P a g e by utilising signalling, in one of two approaches. Either the cell wishes to communicate its findings, or the cell wants to engage in a two-way communication to obtain assistance.

63 | P a g e by utilising signalling, in one of two approaches. Either the cell wishes to communicate its findings, or the cell wants to engage in a two-way communication to obtain assistance.