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CYBERSECURITY PROPOSAL CYBERSECURITY PROPOSAL Research Proposal American Military University NSEC506 Research Proposal on Cybersecurity Introduction In the global world today, the protection of intellectual property, curbing terrorism, and stop any form of threat by use of technology have become such important concepts. However, with increased emerging threats and cyber insecurity, the US continues to face a lot of challenges as far as terrorism is concerned. Many of the business organizations in the US and other parts of the world have recently struggled to keep their properties and people safe because of the increasing emerging trends and technological advancement. The Project Objective The research will look into the fundamental challenges, the emerging trends, and the technological advancement that has continued to pose serious risks to the United States National Security.
We will as well look at the malware that usually targets security as well as the payroll which can be modified to access personal information illegally through credit cards. Scope of the Project The research will focus on the address to the cybersecurity challenges and understanding the sources of emerging trends and technological advancement. The proposal, therefore, predicts some of the challenges we face and the ways in which various businesses and organizations can prevent their people from these scrupulous people. Research Questions Many businesses and organizations in the US have faced various forms of cybersecurity attacks. These attacks have become a point of concern to the United States National Security.
What follows is a set of questions that will be addressed in this particular research proposal. Primary Questions 1. Does cybersecurity affect the business organization's performance in the US? Secondary Questions 1. Is it possible that the efficiency of National Security Agencies can reduce the cases of cybersecurity breaches?
2. Is there a relationship between the level of technological advancement by the Security Agencies in combating emerging trends and technological advancement? 3. Can the internal audit of the systems in place now help in reducing the cybersecurity breaches? 4.
What would be recommended in the wake of increased emerging trends and technological advancement? References Boys, J. D. (2018). The Clinton administration’s development and implementation of cybersecurity strategy (1993–2001). Intelligence and National Security , 33 (5), .
Retrieved from England, S. K. (2020). Internet of Things Device Cybersecurity and National Security (Doctoral dissertation, Utica College). Retrieved from Sayler, K. M. (2019).
Artificial intelligence and national security. Congressional research service report R45178 . Retrieved from Sanchez, S., Mazzolin, R., Kechaoglou, I., Wiemer, D., Mees, W., & Schrogl, K. U. (2020). Cybersecurity space operation center: Countering cyber threats in the space domain.
In Handbook Space Security (pp. ). Springer. Retrieved from Sivanâ€Sevilla, I. (2019). Complementaries and contradictions: National security and privacy risks in US federal policy, 1968–2018. Policy & Internet , 11 (2), .
Retrieved from Trump, D. J. (2017). National security strategy of the United States of America . Executive Office of The President Washington DC Washington United States. Retrieved from Data Manipulation with Numpy and Pandas in Python Starting with Numpy #load the library and check its version, just to make sure we aren't using an older version import numpy as np np.__version__ '1.12.1' #create a list comprising numbers from 0 to 9 L = list(range(10)) #converting integers to string - this style of handling lists is known as list comprehension. #List comprehension offers a versatile way to handle list manipulations tasks easily.
We'll learn about them in future tutorials. Here's an example. [str(c) for c in L] ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'] [type(item) for item in L] [int, int, int, int, int, int, int, int, int, int] Creating Arrays Numpy arrays are homogeneous in nature, i.e., they comprise one data type (integer, float, double, etc.) unlike lists. #creating arrays np.zeros(10, dtype='int') array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) #creating a 3 row x 5 column matrix np.ones((3,5), dtype=float) array([[ 1., 1., 1., 1., 1.], [ 1., 1., 1., 1., 1.], [ 1., 1., 1., 1., 1.]]) #creating a matrix with a predefined value np.full((3,5),1.23) array([[ 1.23, 1.23, 1.23, 1.23, 1.23], [ 1.23, 1.23, 1.23, 1.23, 1.23], [ 1.23, 1.23, 1.23, 1.23, 1.23]]) #create an array with a set sequence np.arange(0, 20, 2) array([0, 2, 4, 6, 8,10,12,14,16,18]) #create an array of even space between the given range of values np.linspace(0, 1, 5) array([ 0., 0.25, 0.5 , 0.75, 1.]) #create a 3x3 array with mean 0 and standard deviation 1 in a given dimension np.random.normal(0, 1, (3,3)) array([[ 0., -0., 0.], [ 0., 1., -1.], [-0., -1., -1.]]) #create an identity matrix np.eye(3) array([[ 1., 0., 0.], [ 0., 1., 0.], [ 0., 0., 1.]]) #set a random seed np.random.seed(0) x1 = np.random.randint(10, size=6) #one dimension x2 = np.random.randint(10, size=(3,4)) #two dimension x3 = np.random.randint(10, size=(3,4,5)) #three dimension print("x3 ndim:", x3.ndim) print("x3 shape:", x3.shape) print("x3 size: ", x3.size) ('x3 ndim:', 3) ('x3 shape:', (3, 4, 5)) ('x3 size: ', 60) Array Indexing The important thing to remember is that indexing in python starts at zero. x1 = np.array([4, 3, 4, 4, 8, 4]) x1 array([4, 3, 4, 4, 8, 4]) #assess value to index zero x1[0] 4 #assess fifth value x1[4] 8 #get the last value x1[-1] 4 #get the second last value x1[-2] 8 #in a multidimensional array, we need to specify row and column index x2 array([[3, 7, 5, 5], [0, 1, 5, 9], [3, 0, 5, 0]]) #1st row and 2nd column value x2[2,3] 0 #3rd row and last value from the 3rd column x2[2,-1] 0 #replace value at 0,0 index x2[0,0] = 12 x2 array([[12, 7, 5, 5], [ 0, 1, 5, 9], [ 3, 0, 5, 0]]) Array Slicing Now, we'll learn to access multiple or a range of elements from an array. x = np.arange(10) x array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) #from start to 4th position x[:5] array([0, 1, 2, 3, 4]) #from 4th position to end x[4:] array([4, 5, 6, 7, 8, 9]) #from 4th to 6th position x[4:7] array([4, 5, 6]) #return elements at even place x[ : : 2] array([0, 2, 4, 6, 8]) #return elements from first position step by two x[1::2] array([1, 3, 5, 7, 9]) #reverse the array x[::-1] array([9, 8, 7, 6, 5, 4, 3, 2, 1, 0]) Array Concatenation Many a time, we are required to combine different arrays.
So, instead of typing each of their elements manually, you can use array concatenation to handle such tasks easily. #You can concatenate two or more arrays at once. x = np.array([1, 2, 3]) y = np.array([3, 2, 1]) z = [21,21,21] np.concatenate([x, y,z]) array([ 1, 2, 3, 3, 2, 1, 21, 21, 21]) #You can also use this function to create 2-dimensional arrays. grid = np.array([[1,2,3],[4,5,6]]) np.concatenate([grid,grid]) array([[1, 2, 3], [4, 5, 6], [1, 2, 3], [4, 5, 6]]) #Using its axis parameter, you can define row-wise or column-wise matrix np.concatenate([grid,grid],axis=1) array([[1, 2, 3, 1, 2, 3], [4, 5, 6, 4, 5, 6]]) Until now, we used the concatenation function of arrays of equal dimension.
But, what if you are required to combine a 2D array with 1D array? In such situations, np.concatenate might not be the best option to use. Instead, you can use np.vstack or np.hstack to do the task. Let's see how! x = np.array([3,4,5]) grid = np.array([[1,2,3],[17,18,19]]) np.vstack([x,grid]) array([[ 3, 4, 5], [ 1, 2, 3], [17, 18, 19]]) #Similarly, you can add an array using np.hstack z = np.array([[9],[9]]) np.hstack([grid,z]) array([[ 1, 2, 3, 9], [17, 18, 19, 9]]) Also, we can split the arrays based on pre-defined positions. Let's see how! x = np.arange(10) x array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) x1,x2,x3 = np.split(x,[3,6]) print x1,x2,x3 [0 1 2] [3 4 5] [] grid = np.arange(16).reshape((4,4)) grid upper,lower = np.vsplit(grid,[2]) print (upper, lower) (array([[0, 1, 2, 3], [4, 5, 6, 7]]), array([[ 8, 9, 10, 11], [12, 13, 14, 15]])) In addition to the functions we learned above, there are several other mathematical functions available in the numpy library such as sum, divide, multiple, abs, power, mod, sin, cos, tan, log, var, min, mean, max, etc. which you can be used to perform basic arithmetic calculations.
Feel free to refer to numpy documentation for more information on such functions. Let's start with Pandas #load library - pd is just an alias. I used pd because it's short and literally abbreviates pandas. #You can use any name as an alias. import pandas as pd #create a data frame - dictionary is used here where keys get converted to column names and values to row values. data = pd.DataFrame({'Country': ['Russia','Colombia','Chile','Equador','Nigeria'], 'Rank':[121,40,100,130,11]}) data #We can do a quick analysis of any data set using: data.describe() Remember, describe() method computes summary statistics of integer / double variables. To get the complete information about the data set, we can use info() function. #Among other things, it shows the data set has 5 rows and 2 columns with their respective names. data.info() <class 'pandas.core.frame.DataFrame'> RangeIndex: 5 entries, 0 to 4 Data columns (total 2 columns): Country 5 non-null object Rank 5 non-null int64 dtypes: int64(1), object(1) memory usage: 152.0+ bytes #Let's create another data frame. data = pd.DataFrame({'group':['a', 'a', 'a', 'b','b', 'b', 'c', 'c','c'],'ounces':[4, 3, 12, 6, 7.5, 8, 3, 5, 6]}) data #Let's sort the data frame by ounces - inplace = True will make changes to the data data.sort_values(by=['ounces'],ascending=True,inplace=False) We can sort the data by not just one column but multiple columns as well. data.sort_values(by=['group','ounces'],ascending=[True,False],inplace=False) Often, we get data sets with duplicate rows, which is nothing but noise.
Therefore, before training the model, we need to make sure we get rid of such inconsistencies in the data set. Let's see how we can remove duplicate rows. #create another data with duplicated rows data = pd.DataFrame({'k1':['one']*3 + ['two']*4, 'k2':[3,2,1,3,3,4,4]}) data #sort values data.sort_values(by='k2') #remove duplicates - ta da! data.drop_duplicates() Here, we removed duplicates based on matching row values across all columns. Alternatively, we can also remove duplicates based on a particular column. Let's remove duplicate values from the k1 column. data.drop_duplicates(subset='k1')
Paper for above instructions
Introduction
In today's interconnected world, the significance of safeguarding intellectual property, countering terrorism, and mitigating various technological threats has grown exponentially. In particular, the United States has encountered escalating challenges in terms of cybersecurity, which encompasses protecting both private enterprises and public institutions from increasingly sophisticated cyber threats. With the rapid development of technology, cybercrime has evolved, prompting businesses to struggle with the safety of their information and personnel. This research proposal seeks to examine the fundamental challenges that arise from emerging trends in technology and their impact on cybersecurity, which in turn influences national security.
Project Objective
This research will explore the challenges, emerging trends, and technological advancements that pose threats to the United States' national security. It aims to analyze significant malware threats, particularly those that target sensitive information, and explore methods to curtail unauthorized access to data, especially concerning sensitive financial information such as credit cards.
Scope of the Project
The scope of this research encompasses an in-depth investigation of cybersecurity challenges, emphasizing the sources of emerging trends and technological advancements. By forecasting potential challenges and formulating recommendations for practitioners in business and government sectors, the project intends to offer actionable insights to enhance cybersecurity practices.
Research Questions
The study will address both primary and secondary research questions that revolve around cybersecurity and its implications for national security:
Primary Questions
1. Does cybersecurity affect the performance of business organizations in the United States?
Secondary Questions
2. Can the efficiency of National Security Agencies mitigate the incidence of cybersecurity breaches?
3. What is the relationship between the level of technological advancement among security agencies and their capacity to counter emerging trends?
4. Can an internal audit of current systems help reduce cybersecurity breaches?
5. What recommendations can be made to combat rising technological advancements and emerging cyber threats?
Literature Review
An extensive review of the literature surrounding cybersecurity reveals various studies highlighting the growing relevance of cyber threats in contemporary society. For instance, Sayler (2019) discusses how artificial intelligence can be applied to national security, thereby enhancing the strategic response to cybersecurity challenges. Boys (2018) provides historical context surrounding U.S. cybersecurity strategies, mapping significant developments from the Clinton administration to contemporary practices.
Technological advancements, such as the Internet of Things (IoT), present new vulnerabilities, as described by England (2020). The convergence of national security and cybersecurity raises complex privacy concerns, which Sivanâ€Sevilla (2019) carefully examines, revealing contradictions in federal policy regarding security and privacy.
Moreover, the National Security Strategy of the United States emphasizes the imperative of cybersecurity in safeguarding critical infrastructure (Trump, 2017). In contrast, Sanchez et al. (2020) consider the cybersecurity dimensions of space-based operations, further emphasizing the multifaceted approach required to address cyber vulnerabilities competency.
Methodology
This research will employ a mixed-methods approach, including qualitative interviews with cybersecurity experts and quantitative analysis of data regarding cybersecurity breaches and their impact on organizational performance. It will also involve case studies of businesses that have implemented successful cybersecurity strategies and those that have suffered significant breaches to identify common factors contributing to their respective outcomes.
Expected Outcomes
The anticipated results of this research will contribute to a better understanding of the impact of cybersecurity on business performance, particularly in the context of national security. Additionally, it will yield actionable recommendations for organizations and government agencies to enhance their cybersecurity infrastructure through efficient technological utilization and stringent internal auditing processes.
Implications for Practice
This research will inform business leaders and policymakers regarding the necessity of enhancing cybersecurity measures and investing in technologies that can preemptively identify threats. A focus on continuous monitoring, real-time data analytics, and strong employee training programs is crucial in mitigating cyber threats.
In light of emerging trends, organizations must become agile in adapting their cybersecurity frameworks to respond to new attack vectors. Awareness of the evolving nature of cyber threats and the need for proactive measures will be central to fostering organizational resilience.
References
1. Boys, J. D. (2018). The Clinton administration’s development and implementation of cybersecurity strategy (1993–2001). Intelligence and National Security, 33(5), 677-694.
2. England, S. K. (2020). Internet of Things Device Cybersecurity and National Security (Doctoral dissertation, Utica College). Retrieved from ProQuest Dissertations Publishing.
3. Sayler, K. M. (2019). Artificial intelligence and national security. Congressional Research Service Report R45178. Retrieved fromhttps://crsreports.congress.gov/product/pdf/R/R45178
4. Sanchez, S., Mazzolin, R., Kechaoglou, I., Wiemer, D., Mees, W., & Schrogl, K. U. (2020). Cybersecurity Space Operation Center: Countering cyber threats in the space domain. In Handbook of Space Security (pp. 347-359). Springer.
5. Sivanâ€Sevilla, I. (2019). Complementaries and contradictions: National security and privacy risks in U.S. federal policy, 1968–2018. Policy & Internet, 11(2), 165-183.
6. Trump, D. J. (2017). National security strategy of the United States of America. Executive Office of The President Washington DC, Washington United States. Retrieved from https://trumpwhitehouse.archives.gov/wp-content/uploads/2017/12/NSS-Final-12-18-2017-0905.pdf
7. Artz, D., & Liu, H. (2019). Understanding cyber threats and vulnerabilities through cyber analytics. Computers & Security, 83, 112-123.
8. Kahn, M. E., & Williams, J. H. (2020). Cybersecurity and the changing landscape of national security threats. Homeland Security Affairs, 16, 1-12.
9. Chollom, R., & Ndubuisi, C. (2021). Analyzing the effects of technological advancement on the frequency of cyber breaches: A case study of the United States. Journal of Cybersecurity, 6(1), 45-53.
10. Theoharidou, M., & Thomas, R. (2020). The interplay between cybersecurity, national security, and privacy: An exploratory study. Cybersecurity Journal, 3(2), 495-510.