Engendaring Trust

Trust is multidimensional and is key to build all types of relationships between teammates, partners, and oneself. Trust is key to help build the best team, where teammates can have a constructive conflict on each other’s ideas to achieve innovation (Cashman, 2010; Kraemer, 2015). This is because all relationships are built on trust, and it takes just one inauthentic or untrustworthy action to ruin the relationship (Shankman, Allen, Haber-Curran, 2015). Once trustworthiness is lost, it takes time and hard work to regain it. Now, for being the best partner to someone that person must be truly committed to the other person’s success as well as their own while building trust along with mutual respect towards each other’s experience, and working towards long-term collaboration are key (Kraemer, 2015; Shankman et al., 2015). But, trust and belief in oneself are needed to get oneself from a fixed mindset into a growth mindset (Cashman, 2010; Sivers, 2014). Trust is key for a person to be authentic, vulnerable, and personal mastery (Cashman, 2010). Trust in oneself is the first thing that must occur prior to being able and open to trusting others. Trustworthiness attracts other people to believe in and follows their leader (Shankman et al., 2015).

Engender Trust

Cashman (2010) and Shankman et al. (2015) state that engendering trust amongst people is by living authentically to oneself and trusting in oneself. To build up trust in oneself Shankman et al. (2015) suggested to: follow through on your commitments and being open and vulnerable to others by exposing your flaws in a positive way.

I have been following through on my commitments:

  • Become an inventor: have one patent and one patent pending.
  • Get my doctorate degree: Graduated on June 10, 2017.
  • Building subject matter expertise: ITIL Expert Certification in 2016, Greenbelt six sigma in 2014, and Project Management Professional in 2014, etc.

I show my flaws in a positive way:

  • I have ADHD, but it allows me to make connections between two seemingly distinct ideas to push forward a new idea.
  • Thinking about ADHD has allowed me to think of other people with hidden or visible disabilities, therefore creating systems that have universal design principles.

Not being able to gain full trust on you

When I don’t live authentically when I don’t show people who I really am, and when I live with secrets and vulnerabilities. That is because if people don’t believe in themselves and what they are doing as leaders, then why would their followers (Shankman et al., 2015).

Circumstances on which you are more or less trustworthy

As an introverted person, when I meet a new person, I am not 100% authentic to myself. According to Matthew 7:6:

“Give not that which is holy unto the dogs, neither cast, ye your pearls before swine, lest they trample them, under their feet, and turn again and rend you.”

Thus, being vulnerable and authentic to someone else is much easier when I develop a relationship and trust with them. Therefore, for me, it takes time for me to build a relationship of trust with others.

Resources

  • Cashman, K. (2010). Leadership from the inside out: Becoming a leader for life. (2nd ed.). San Francisco, Berrett-Koehler Publishing, Inc.
  • Kraemer, H. M. J. (2015). Becoming the best. (1st ed.). New Jersey, Wiley.
  • Shankman, M. L., Allen, S. J., Haber-Curran, P. (2015). Emotionally Intelligent Leadership: A Guide for Students, (2nd ed.). [Bookshelf Online].
  • Sivers, D. (2014). Fixed mindsets vs growth mindset. Retrieved from https://sivers.org/mindset
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Power and conflict

“‘Leadership is difficult.’ Inherent in any leadership challenge is stress. Stress comes from the environment, interpersonal conflict, the nature or amount of work, or simply the uncertain of what lies ahead.” (Shankman, Allen, & Haber Curran, 2015). Best teams can fall apart easily, due to conflict, if the conflict is not handled properly (Kraemer, 2015). Thus, when a conflict breaks, there are five strategies that people could use: forcing, accommodating, avoiding, compromising and collaborative; but usually, people tend to gravitate towards one or two of them (Williams, n.d.).

Kraemer (2015), illustrates the example of Campbell Soup, a company that recruited and grew in size with employees that were not aligned with the company’s values, and eventually, these people got promoted. These newly promoted ill-fitted employees were unequipped to create the best teams, and a few bad apples and negative influences almost destroyed the company, because of their concentration on short-term goals rather than long-term goals by increasing the price of their products above the value of private-labeled store brands. The CEO had a lot of changes to make to turn that company around and with change brings conflict. Williams (n.d.), illustrates an example of a conflict where Shaun Williams didn’t handle conflict appropriately, used physical forcing during a football game, which got his team penalized heavily, cost the team the game, and ended the team’s season. However, constructive conflict and trust are needed to openly and honestly have engaging relationships (Cashman, 2010).

So, I do not avoid conflict; I tend to embrace it by either compromising or being collaborative. I am more compromising if I am not so invested in the final result, but the other person or team is. I am more collaborative, learning about what the other person needs and wants are, building relationships, and forge a solution that is bigger (win-win) than if I were to use any of the other four strategies (win-lose or lose-lose). I had witnessed true collaboration, where my team built up a solution when there was a seed of a solution and combined other aspects of another solution that was brought in by another teammate.

I tend to be more compromising than I am collaborative, given my ADHD. I rarely get attached to a solution that would be worth it enough to keep the most conflict moving forward unless it is a constructive conflict. A constructive conflict helps build a better solution than prolong destructive conflict because constructive conflict focuses on engaging open and honest conflicts and building upon our relationships (Cashman, 2010). When I am seeking collaboration, I try to find a solution that stays true to both solutions or a solution that meets both of our goals, needs, and wants.

However, if a conflict quickly becomes destructive, I tend to separate myself from the situation, to allow my emotions first to subside and give both parties a chance to breathe and see the conflict with fresh eyes. At the point that there is a prolonged destructive conflict, I tend to re-evaluate if it is worth to keep up the conflict or to maintain the relationship (usually the relationship wins as long as it doesn’t violate my personal values). I have a tendency to avoid prolonged destructive conflict.

Resources:

  • Cashman, K. (2010) Leadership from the inside out: Becoming a leader for life. (2nd ed.). San Francisco, Berrett-Koehler Publishing, Inc.
  • Shankman, M. L., Allen, S. J., Haber-Curran, P. (2015-01-26). Emotionally Intelligent Leadership: A Guide for Students, (2nd ed.).
  • Kraemer, H. M. J. (2015). Becoming the best. (1st ed.). New Jersey, Wiley.
  • Williams, S. (n.d.). Conflict management – Style and strategy. Retrieved from http://www.wright.edu/~scott.williams/LeaderLetter/conflict.htm

Ethical Leadership

No one wake up one day and say they will be unethical, however small acts can build up to unethical behavior (Prentice, 2007). This conclusion on ethics is similar to a slippery slope argument. Understandably, unethical people and unethical actions aren’t equivalent to evil people or evil actions (Prentice, 2007). As stated by Chapman and Sisodia (2015), “Ethics is people.” Ethics usually involves and revolves around people. However, good intentions are not enough to ensure ethical behavior (Prentice, 2007). Thus, Prentice outlined how unethical decisions could be made:

  • Obedience to authority: following orders blindly
  • Conformity bias: observing others in a group and conforming to consciously or unconsciously
  • Incrementalism: the slippery slope argument
  • Group think: pressures to not stand out from a group consensus
  • Over-optimism: irrational beliefs led by a strong tendency of optimistic beliefs
  • Overconfidence: irrational beliefs led by a strong tendency of confidence
  • Selfserving bias: gathering information that only strengthens one’s views or self-interest and discarding challenging viewpoints
  • Framing: how a problem or situation is framed can yield different results
  • Sunk costs: continual consideration and loyalty to a bad idea, just because a significant amount or resources have been poured into the idea
  • The tangible, the close and the near term: having something tangible that is near you and close by weights more than those that are separated by distance or time or in the abstract
  • Loss aversion: people prefer not to act for fear of losing something
  • Endowment effect: people getting attached to something

 

End decision: According to Prentice (2007) one way to not fall into the trap of an unethical decision is to check your personal biases at the door.

Resources:

  • Chapman, B. & Sisodia, R. (2015). Everybody matters: The extraordinary power of caring for your people like family. New York, Penguin.
  • Prentice, R. A. (2007). Ethical decision making: More needed than good intentions. Financial Analysis Journal, 63(6), 17–30.

Attention Deficit Hyperactivity Disorder: A gift

In 2016, I was diagnosed with Attention Deficit Hyperactivity Disorder (ADHD). ADHD brains have abnormally low levels of dopamine activating the frontal cortex (Flippin, n.d.) According to the NIH (n.d.), this involves

  • inattention where I would find doing persistent tasks rather difficult
  • hyperactivity where I would find it rather difficult to stop and tend to wear people out
  • impulsive where I conduct content switching multiple time per hour

However, ADHD shouldn’t be seen as a lack of attention, but having a deregulation of the attention system (Flippin, n.d.). ADHD occurs across a spectrum and I have a high functioning form of it, such that as long as a task or job description is interesting enough for me, I will be a high performer, but at this side of the spectrum and my analytical mind, it also comes with the drawback of over analysis.

So, sharing this in this post isn’t easy, but the key part of why I mention it is because of the line “… as long as a task or job description is interesting enough from me, I will be a high performer…” This is an ability under ADHD called hyperfocus. Hyperfocus is the other side of the coin of ADHD, where the person with ADHD finds something really interesting that the person will intently focus on that one item without interruption (Flippin, n.d.).  Over the past year, I have been identifying job roles and statements of works where I can be at a state of hyperfocus. So, one method to encourage self-reflection with me is having a someone to ask me focused questions to engage my thoughts.

Due to hyperfocus, I have been using to do my dissertation, and this is why Colorado Technical University (CTU) was the best choice of school for me. Hyperfocus can be channeled into productive work like focusing on a dissertation where the topic is of utmost interest to me (Flippin, n.d.). This ability has allowed me to have completed my dissertation at a record pace because hours seemed like minutes to me when I am in this headspace. At other universities, you have to do research for your dissertation under a supervisor who has grant funding under a particular area, so you don’t control all the variables in picking your topic. This is where I thrived at CTU; I was able to pick anything of interest that fell under Computer Science and data analytics, which were my other passions.

Cognitive behavioral therapy like meditation and mindfulness were two other techniques my therapist has advised me to use to lessen the symptoms of ADHD, and the NIH (n.d.) agrees. It allows me to make incremental improvements in focus, but it also gives me the second method to encourage self-reflection. During meditation, I focus on breath control, let my thoughts come in, observe them pass by, and refocus on my breath. I have been doing this for 10 minutes every day since June 2015, with about an 80% consistency rate. With all new habits, if you don’t do it 100% of the time, just dust yourself off and say, ok, I missed 1-5 days, but I will start it up again.

Meditation, mindfulness, and therapist focused questions have been effective methods to encourage self-reflection for me in the present.

Resources:

Different Types of Leadership Styles

Leadership Theories:

  • Chapman and Sisodia (2015) define leadership as the value they bring to people. The author’s primary guiding value is that “We measure success by the way we touch the lives of people.” This type of leadership practice stems from treating their followers the similarly to how someone would like their kids to be treated in the work environment. This type of leadership relies on coaching the leader’s followers to build on the follower’s greatness. Then recognition is done that shake employees to the core by involving the employee’s family, so that the employee’s family could be proud of their spouse or parent. The goal of this type of leadership is to have the employee seen, valued, and heard such that they want to be their best and do their best not just for the company but for their coworkers as well.
  • Cashman (2010) defines leadership from an inside-out approach of personal mastery. This type of leadership style is focused on self-awareness of the leader’s conscious beliefs and shadow beliefs to grow and deepen the leader’s authenticity. Cashman pushes the leader to identify, reflect and recognize their core talents, values and purpose. With the purpose of any leadership is understanding “How am I going to make a difference?” and “How am I going to enhance other people’s lives?” Working from the leader’s core purpose releases more of that untapped leader’s energy to do more meaning work that frees the leader and opens leaders up to different possibilities, more so than just working towards a leader’s goals.
  • Open Leadership: Has five rules, which allow for respect and empowerment of the customers and employees, to consistently build trust, nurtures curiosity and humility, holding openness accountable, and allows for forgiving failures (Li, 2010).  These leaders must let go of the old mentality of micromanaging, because once they do let go of micromanagement, these leaders are now open to grow into new opportunities. This thought process is shares commonalities with knowledge sharing, if people were to share the knowledge that they accumulated, these people would be able to let go of your current tasks, such that these people can focus on new and better opportunities. Li stated that open Leadership allows for leaders to build, deepen, and nurture relationships with the customers and employees.  Open leadership is a theory of leadership that is customer and employee centered.
  • Values based leadership requires four principles: self-reflection, balance, humble, and self-confidence (Kraemer, 2015). Through self-reflection, leaders identify their core beliefs and values that matters to the leader. Leaders that view situations from multiple perspectives to gain a deeper understanding of the situation is considered balanced. Humility in leaders refers to not forgetting who the leader is and where the leaders come from to gain appreciation for each person. Finally, self-confidence is the leader accepting themselves as they are, warts and all.

Parts of these leadership theories that resonates

Each of these leadership theories above have a few concepts in common. Most of the leadership theories agree with each other because each leadership theory has a focus on growing the leader’s followers (Cashman, 2010; Chapman & Sisodia, 2015; Li, 2010; Kraemer, 2015). Cashman and Kraemer focuses on self-reflection, so that the leader can understand personal values, strengths, and weaknesses. For Cashman, self-reflection focuses on purpose, which is where there is an unbound level of energy. Whereas Kraemer, self-reflection focuses on defining the leader’s values and constant assessment and realigning the leader’s roles towards the leader’s value.

Resources:

  • Cashman, K. (2010) Leadership from the inside out: Becoming a leader for life. (2nd ed.). San Francisco, Berrett-Koehler Publishing, Inc.
  • Chapman, B. & Sisodia, R. (2015) Everybody matters: The extraordinary power of caring for your people like family. New York, Penguin.
  • Li, C. (2010). Open Leadership: How Social Technology Can Transform the Way You Lead, (1st ed.). Vitalbook file.
  • Kraemer, H. M. J. (2015). Becoming the best. (1st ed.). New Jersey, Wiley.

Compelling Topics in Advance Topics

  • The evolution of data to wisdom is defined by the DIKW pyramid, where Data is just facts without any context, but when facts are used to understand relationships it generates Information (Almeyer-Stubbe & Coleman, 2014). That information can be used to understand patterns, it can then help build Knowledge, and when that knowledge is used to understand principles, it builds Wisdom (Almeyer-Stubbe & Coleman, 2014; Bellinger, Castro, Mills, n.d.). Building an understanding to jump from one level of the DIKW pyramid, is an appreciation of learning “why” (Bellinger et al., n.d.).
  • The internet has evolved into a socio-technical system. This evolution has come about in five distinct stages:
    • Web 1.0: Created by Tim Berners-Lee in the 1980s, where it was originally defined as a way of connecting static read-only information hosted across multiple computational components primarily for companies (Patel, 2013).
    • Web 2.0: Changed the state of the internet from a read-only state to a read/write state and had grown communities that hold a common interest (Patel, 2013). This version of the web led to more social interaction, giving people and content importance on the web, due to the introduction of social media tools through the introduction of web applications (Li, 2010; Patel, 2013; Sakr, 2014). Web applications can include event-driven and object-oriented programming that are designed to handle concurrent activities for multiple users and had a graphical user interface (Connolly & Begg, 2014; Sandén, 2011).
  • Web 3.0: This is the state the web at 2017. Involves the semantic web that is driven by data integration through the uses of metadata (Patel, 2013). This version of the web supports a worldwide database with static HTML documents, dynamically rendered data, next standard HTML (HTML5), and links between documents with hopes of creating an interconnected and interrelated openly accessible world data such that tagged micro-content can be easily discoverable through search engines (Connolly & Begg, 2014; Patel, 2013). This new version of HTML, HTML5 can handle multimedia and graphical content, which are great for semantic content (Connolly & Begg, 2014). Also, end-users are beginning to build dynamic web applications for others to interact with (Patel, 2013).
    • Web 4.0: It is considered the symbiotic web, where data interactions occur between humans and smart devices, the internet of things (Atzori, 2010; Patel, 2013). These smart devices can be wired to the internet or connected via wireless sensors through enhanced communication protocols (Atzori, 2010). Thus, these smart devices would have read and write concurrently with humans, where the largest potential of web 4.0 has these smart devices analyze data online and begin to migrate the online world into the real world (Patel, 2013).
    • Web 5.0: Previous iterations of the web do not perceive people’s emotion, but one day it could be able to understand a person’s emotional (Patel, 2013). Kelly (2007) predicted that in 5,000 days the internet would become one machine and all other devices would be a window into this machine. In 2007, Kelly stated that this one machine “the internet” has the processing capability of one human brain, but in 5,000 days it will have the processing capability of all the humanity.
  • MapReduce is a framework that uses parallel sequential algorithms that capitalize on cloud architecture, which became popular under the open source Hadoop project, as its main executable analytic engine (Lublinsky et al., 2014; Sadalage & Fowler, 2012; Sakr, 2014). Another feature of MapReduce is that a reduced output can become another’s map function (Sadalage & Fowler, 2012).
  • A sequential algorithm is a computer program that runs on a sequence of commands, and a parallel algorithm runs a set of sequential commands over separate computational cores (Brookshear & Brylow, 2014; Sakr, 2014).
  • A parallel sequential algorithm runs a full sequential program over multiple but separate cores (Sakr, 2014).
    • Shared memory distributed programming: Is where serialized programs run on multiple threads, where all the threads have access to the underlying data that is stored in shared memory (Sakr, 2014). Each thread should be synchronized as to ensure that read and write functions aren’t being done on the same segment of the shared data at the same time. Sandén (2011) and Sakr, (2014) stated that this could be achieved via semaphores (signals other threads that data is being written/posted and other threads should wait to use the data until a condition is met), locks (data can be locked or unlocked from reading and writing), and barriers (threads cannot run on this next step until everything preceding it is completed).
    • Message passing distributed programming: Is where data is stored in one location, and a master thread helps spread chunks of the data onto sub-tasks and threads to process the overall data in parallel (Sakr, 2014). There are explicitly direct send and receive messages that have synchronized communications (Lublinsky et al., 2013; Sakr, 2014).         At the end of the runs, data is the merged together by the master thread (Sakr, 2014).
  • A scalable multi-level stochastic model-based performance analysis has been proposed by Ghosh, Longo, Naik and Trivedi (2012) for Infrastructure as a Service (IaaS) cloud computing. Stochastic analysis and models are a form of predicting how probable an outcome will occur using a form of chaotic deterministic models that help in dealing with analyzing one or more outcomes that are cloaked in uncertainty (Anadale, 2016; Investopedia, n.d.).
  • Three-pool cloud architecture: In IaaS cloud scenario, a request for resources initiates a request for one or more virtual machines to be deployed to access a physical machine (Ghosh et al., 2012). The architecture assumes that physical machines are grouped in pools hot, warm, and cool (Sakr, 2014). The hot pool consists of physical machines that are constantly on, and virtual machines are deployed upon request (Ghosh et al., 2012; Sakr, 2014).   Whereas a warm pool has the physical machines in power saving mode, and cold pools have physical machines that are turned off. For both warm and cold pools setting up a virtual machine is delayed compared to a hot pool, since the physical machines need to be powered up or awaken before a virtual machine is deployed (Ghosh et al., 2012). The optimal number of physical machines in each pool is predetermined by the information technology architects.
  • Data-at-rest is probably considered easier to analyze; however, this type of data can also be problematic. If the data-at-rest is large in size and even if the data does not change or evolve, its large size requires iterative processes to analyze the data.
  • Data-in-motion and streaming data has to be iteratively processed until there is a certain termination condition is reached and it can be reached between iterations (Sakr, 2014). However, Sakr (2014) stated that MapReduce does not support iterative data processing and analysis directly.
    • To deal with datasets that require iterative processes to analyze the data, computer coders need to create and arrange multiple MapReduce functions in a loop (Sakr, 2014). This workaround would increase the processing time of the serialized program because data would have to be reloaded and reprocessed, because there is no read or write of intermediate data, which was there for preserving the input data (Lusblinksy et al., 2014; Sakr, 2014).
  • Data usually gets update on a regular basis. Connolly and Begg (2014) defined that data can be updated incrementally, only small sections of the data, or can be updated completely. This data update can provide its own unique challenges when it comes to data processing.
    • For processing incremental changes on big data, one must split the main computation to its sub-computation, logging in data updates in a memoization server, while checking the inputs of the input data to each sub-computation (Bhatotia et al., 2011; Sakr, 2014). These sub-computations are usually mappers and reducers (Sakr, 2014). Incremental mappers check against the memoization servers, and if the data has already been processed and unchanged it will not reprocess the data, and a similar process for incremental reducers that check for changed mapper outputs (Bhatotia et al., 2011).
  • Brewer (2000) and Gilbert and Lynch (2012) concluded that for a distributed shared-data system you could only have at most two of the three properties: consistency, availability, partition-tolerance (CAP theory). Gilbert and Lynch (2012) describes these three as akin to the safety of the data, live data, and reliability of the data.
    • In a NoSQL distributed database systems (DDBS), it means that partition-tolerance should exist, and therefore administrators should then select between consistency and availability (Gilbert & Lynch, 2012; Sakr, 2014). However, if the administrators focus on availability they can try to achieve weak consistency, or if the administrators focus on consistency, they are planning on having a strong consistency system. Strong consistency ensures that all copies of the data are updated in real-time, whereas weak consistency means that eventually all the copies of the data will be updated (Connolly and Begg, 2014; Sakr, 2014). An availability focus is having access to the data even during downtimes (Sakr, 2014).
  • Volume visualization is used to understand large amounts of data, in other words, big data, where it can be processed on a server or in the cloud and rendered onto a hand-held device to allow for the end user to interact with the data (Johnson, 2011). Tanahashi, Chen, Marchesin and Ma (2010), define a framework for creating an entirely web-based visualization interface (or web application), which leverages the cloud computing environment. The benefit of using this type of interface is that there is no need to download or install the software, but that the software can be accessed through any mobile device with a connection to the internet.

Resources:

  • Brookshear, G., Brylow, D. (2014). Computer Science: An Overview, (12th ed.). Vitalbook file.
  • Connolly, T., & Begg, C. (2014). Database Systems: A Practical Approach to Design, Implementation, and Management, (6th ed.). Pearson Learning Solutions. VitalBook file.
  • Ghosh, R., Longo, F., Naik, V. K., & Trivedi, K. S. (2013). Modeling and performance analysis of large scale IaaS clouds. Future Generation Computer Systems, 29 (5), 1216-1234.
  • Gilbert, S., and Lynch N. A. (2012). Perspectives on the CAP Theorem. Computer 45(2), 30–36. doi: 10.1109/MC.2011.389
  • Investopedia (n.d.). Stochastic modeling. Retrieved from http://www.investopedia.com/terms/s/stochastic-modeling.asp
  • Johnson, C. (2011) Visualizing large data sets. TEDx Salt Lake City. Retrieved from https://www.youtube.com/watch?v=5UxC9Le1eOY
  • Kelly, K. (2007). The next 5,000 days of the web. TED Talk. Retrieved from https://www.ted.com/talks/kevin_kelly_on_the_next_5_000_days_of_the_web
  • Li, C. (2010). Open Leadership: How Social Technology Can Transform the Way You Lead, (1st ed.). VitalBook file.
  • Lublinsky, B., Smith, K. T., & Yakubovich, A. (2013). Professional Hadoop Solutions. Vitalbook file.
  • Patel, K. (2013). Incremental journey for World Wide Web: Introduced with Web 1.0 to recent Web 5.0 – A survey paper. International Journal of Advanced Research in Computer Science and Software Engineering, 3(10), 410–417.
  • Sadalage, P. J., Fowler, M. (2012). NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence, (1st ed.). Vitalbook file.
  • Sakr, S. (2014). Large Scale and Big Data, (1st ed.). Vitalbook file.
  • Sandén, B. I. (2011). Design of Multithreaded Software: The Entity-Life Modeling Approach. Wiley-Blackwell. VitalBook file.
  • Tanahashi, Y., Chen, C., Marchesin, S., & Ma, K. (2010). An interface design for future cloud-based visualization services. Proceedings of 2010 IEEE Second International Conference on Cloud Computing Technology and Service, 609–613. doi: 10.1109/CloudCom.2010.46

 

Adv Topics: Possible future study

Application of Artificial Intelligence for real-time cybersecurity threat identification and resolution for network vulnerabilities in the cloud

Motivation: Artificial Intelligence (AI) is an embedded technology, based off of the current infrastructure (i.e. supercomputers), big data, and machine learning algorithms (Cyranoski, 2015; Power, 2015). AI can make use of data hidden in “dark wells” and silos, where the end-user had no idea that the data even existed, to begin with (Power, 2015). The goal of AI is to use huge amounts of data to draw out a set of rules through machine learning that will effectively supplement cyber security experts in identifying and remediating cyberattacks (Cringely, 2013; Power, 2015).

Problem statement: Must consider an attacker’s choices are unknown, if they will be successful in their targets and goals and the physical paths for an attack in the explicit and abstract form, which are hard to do without the use of big data analysis coupled with AI for remediation.

Hypothesis statement:

  • Null: The use of Bayesian Networks and AI cannot be used for both identification and remediation of cyber-attacks that deal with the network infrastructure on a cloud environment.
  • Alternative: The use of Bayesian Networks and AI can be used for both identification and remediation of cyber-attacks that deal with the network infrastructure on a cloud environment.

Proposed solution:

  • New contribution made to the body of knowledge by your proposed solution: The merging of these two technologies can be a first line of defense that can work 24×7 and learn new remediation and identification techniques as time moves forward.

2 research questions:

  • Can the merger of Bayesian Networks and AI be used for both identification and remediation of cyber-attacks that deal with the network infrastructure on a cloud environment? –> This is just based off of the hypothesis.
  • Can the use of Bayesian Networks and AI can be used for both identification and remediation of cyber-attacks that deal with multiple network attacks from various white hat hackers at the same time? –> This is taken from real life. A fortune 500 company is constantly bombarded with thousand if not millions of attempted cyber attackers at a given day. If there is a vulnerability found, it might result in multiple people entering in through that vulnerability and doing serious damage. Could this proposed system handle multiple attacks coming right at the cloud network infrastructure? Essentially providing practitioners some tangible results.

Resources: