Democracy And Individualism A Nation Of A Homogenous...

Democracy and Individualism In the 1800’s, Thomas Jefferson, Andrew Jackson, and many others had visions for America that involved a nation of a homogenous type of society. It was believed in order to create a better nation that people other than the European Americans needed to be eradicated. In these beliefs, these men affected the America nation through their trials and tribulations, which help guide society into what it is today. Thomas Jefferson was a man who viewed white as beautiful. In which his views of black and Native Americans played a vital role in the development of the frontier. He viewed America as a Republic society with only room for white people. So, he envisioned creating a pure group of white people that could attune†¦show more content†¦Jefferson believed then once paid in full he would release his slaves upon the payment of his debts. Although his beliefs, he died before paying his debt off still owning slaves. Secondly, Jefferson’s views of the Indians were that the Indians could either be incorporated into his vision or they could be eradicated from the society all together. Jefferson claimed Indians had the potential to conforming to the republic structure that black slaves did not. Also, Jefferson wanted friendship with the Indians, but under the conditions that they either conform completely to the new republic or they were to stay west of the Mississippi in the fronti er. During this time, Andrew Jackson also contributed to the development of the frontier by his wish to remove all Indians in the east. Jackson along with the state of Georgia wanted to eliminate Indians in the east. Jackson was able to negotiate on treaties with some Indians tribes to move to other lands but the others decided to stay and fight for the land that was theirs. The Indians claimed they had rights to their land that had been negotiated and given to them in the past. This issue between Georgia and the Indians made it to the Supreme Court where the court sided with the Indians on the issue of keeping their land. Also, the courted decided that states could not decide where Indians could live; it was only to be decided by theShow MoreRelatedWestern Culture And Policies That Have Shaped The Modern World1523 Words   |  7 Pagesespecially the Middle East, in many ways. Since the sixteenth century, the nations of Western civilization have been the driving wheels of modernization. Globalization is simply the spread of modern institutions and ideas from one high power to the wider world. Technological innovation and economic growth along with such concepts as democracy, individualism, and the rule of law administered by an impartial judiciary, set Western societies above and beyond any possible rival. Other cultures looked to the WestRead MoreCulture Drives Globalization Essay1559 Words   |  7 Pagesthe world over. 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Apollo 13 Case Analysis - 1599 Words

Green Team Apollo 13 Case Analysis The primary questions and issues you debated and discussed (i.e., what did your team think was most relevant about the case?). First, the green team discussed the success of the mission. We had a split jury on whether or not we thought the mission was successful. Both sides of the argument were well supported and we agreed to disagree with the following conclusions: one side took the stance that the mission was not successful because they did not make it to the moon; the other side took the stance that the mission was successful because the astronauts’ lives were saved and that the mission evolved into bringing the astronauts home safely. The second issue the green team debated was whether or not†¦show more content†¦The second element of leadership according to Singh is expertise. Singh argued â€Å"raw energy can be wasteful, even destructive, if not harnessed well. 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Software Security Risk Analysis Using Fuzzy Expert System Free Essays

| | |Software Level of Security Risk Analysis Using Fuzzy | |Expert System | |[ARTIFICIAL INTELLIGENT] | UNIVERSITI TEKNIKAL MALAYSIA MELAKA FACULTY OF INFORMATION COMMUNICATION TECHNOLOGY SESSION 2 – 2010/2011 |NURUL AZRIN BT AIRRUDIN – B031010343 | |SITI NURSHAFIEQA BT SUHAIMI – B031010313 | |NUR SHAHIDA BT MUHTAR – B031010266 | | | |LECTURE NAME: DR ABD. SAMAD HASSAN BASARI | | | |[12th APRIL 2011] | SOFTWARE LEVEL OF SECURITY RISK ANALYSIS USING FUZZY EXPERT SYSTEM ABSTRACT There is wide concern on the security of software systems because many organizations depend largely on them for their day-to-day operations. Since we have not seen a software system that is completely secure, there is need to analyze and determine the security risk of emerging software systems. We will write a custom essay sample on Software Security Risk Analysis Using Fuzzy Expert System or any similar topic only for you Order Now This work presents a technique for analyzing software security using fuzzy expert system. The inputs to the system are suitable fuzzy sets representing linguistic values for software security goals of confidentiality, integrity and availability. The expert rules were constructed using the Mamdani fuzzy reasoning in order to adequately analyze the inputs. The defuzzication technique was done using Centroid technique. The implementation of the design is done using MATLAB fuzzy logic tool because of its ability to implement fuzzy based systems. Using newly develop software products from three software development organizations as test cases, the results show a system that can be used to effectively analyze software security risk. ANALYSIS AND DESIGN The design is basically divided into four stages: 1) DESIGN OF THE LINGUISTIC VARIABLES The inputs to the system are the values assumed for the software security goal thru confidentiality, integrity and availability. The goals are assumed to be the same weight and a particular valued is determined for each of them based on questions that are answered about the specific software. Also the values determined for each of the input are defined as a fuzzy number instead of crisp numbers by using suitable fuzzy sets. Designing the fuzzy system requires that the different inputs (that is, confidentiality, integrity, and availability) are represented by fuzzy sets. The fuzzy sets are in turn represented by a membership function. The membership function used in this paper is the triangular membership function which is a three point function defined by minimum, maximum and modal values where usually represented in 1. [pic] Figure 1: Triangular Membership Function 2) THE FUZZY SETS The level of confidentiality is defined based on the scales of not confidential, slightly confidential, very confidential and extremely confidential. The level of integrity is also defined based on the scales very low, low, high, very high, and extra high. Also, the level of availability is also defined by the scales very low, low, high, very high and extra high. The levels defined above are based on a range definition with an assumed interval of [0 -10]. The ranges for the inputs are shown in tables 1 and 2. DESCRIPTION |RANGE | |Non-Confidential |0-1 | |Slightly Confidential |2-3 | |Confidential |4-6 | |Very Confidential |7-8 | |Extremely Confidential |9-10 | Table 1: Range of inputs for Confidentiality Very Low |Low |High |Very High |Extra High | |0 – 1 |2 – 3 |4 – 6 |7 – 8 |9 – 10 | Table 2: Range of inputs for Integrity |Very Low |Low |High |Very High |Extra High | |0 – 1 |2 â₠¬â€œ 3 |4 – 6 |7 – 8 |9 – 10 | Table 3: Range of inputs for Availability |DESCRIPTION |RANGE | |Not Secure |0 – 3 | |Slightly Secure |4 – 9 | |Secure |10 – 18 | |Very Secure |19 – 25 | |Extremely Secure |26 – 30 | Table 4: Level Of Security Risk The fuzzy sets above are represented by membership functions. The corresponding membership functions for confidentiality, integrity and availability are presented in figures below [pic] Figure 1 : Membership functions for Confidentiality Similarly, the output, that is, the level of security risk is also represented by fuzzy sets and then a membership function. The level of security risk is defined based on the scales: not secure, slightly secure, secure, very secure, and extremely secure within the range of [0- 30]. The range definition is shown in table above. The membership function for the output fuzzy set is presented in figure below. [pic] Figure 2 : Membership functions for Integrity [pic] Figure 3 : Membership functions for Availability [pic] Figure 4 : Level Of Security Risk 3) THE RULES OF THE FUZZY SYSTEM Once the input and output fuzzy sets and membership functions are constructed, the rules are then formulated. The rules are formulated based on the input parameters (confidentiality, integrity, and availability) and the output i. e. level of security risk. The levels of confidentiality, integrity, and availability are used in the antecedent of rules and the level of security risk as the consequent of rules. A fuzzy rule is conditional statement in the form: IF x is A THEN y is B. Where x and y are linguistic variables; and A and B are linguistic values determined by fuzzy sets on universe of discourses X and Y, respectively. Both the antecedent and consequent of a fuzzy rule can have multiple parts. All parts of the antecedent are calculated simultaneously and resolved in a single number and the antecedent affects all parts of the consequent equally. Some of the rules used in the design of this fuzzy Systems are as follow: 1. If (Confidentiality is Not Confidential) and (Integrity is Very Low) and (Availability is Very Low) then (Security Risk is Not Secure). 2. If (Confidentiality is Not Confidential) and (Integrity is Very Low) and (Availability is Low) then (Security Risk is Slightly Secure). 3. If (Confidentiality is Extremely Confidential) and (Integrity is Extra High) and (Availability is High) then (Security Risk is Slightly Secure). †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 125. If (Confidentiality is Not Confidential) and (Integrity is Very Low) and (Availability is high) then (Security Risk is Extremely Secure). The rules above were formulated using the Mamdani max-min fuzzy reasoning. DEVELOPMENT AND IMPLEMENTATION The linguistic variables were determined with the extent of the positive and negative responses to a well constructed security questions that are presented in form of on-line questionnaire. As it was mentioned earlier, MATLAB was used for the implementation. The linguistic inputs to the system are supplied through the graphical user interface called rule viewer. Once the rule viewer has been opened, the input variables are supplied in the text box captioned input with each of them separated with a space. a) THE FIS EDITOR The fuzzy inference system editor shows a summary of the fuzzy inference system. It shows the mapping of the inputs to the system type and to the output. The names of the input variables and the processing methods for the FIS can be changed through the FIS editor. Figure 5: The FIS editor b) THE MEMBERSHIP FUNCTION EDITOR This can be opened from the command window by using the plotmf function but more easily through the GUI. The membership function editor shows a plot of highlighted input or output variable along their possible ranges and against the probability of occurrence. The name and the range of a membership value can be changed, so also the range of the particular variable itself through the membership function editor. [pic] Figure 6: The Membership Function editor c) THE RULE EDITOR The rule editor can be used to add, delete or change a rule. It is also used to change the connection type and the weight of a rule. The rule editor for this application is shown in figure 7. pic] Figure 7: Rule Editor d) THE RULE VIEWER The text box captioned input is used to supply the three input variables needed in the system. The appropriate input corresponds to the number of YES answer in the questionnaire for each of the input variables. For example, in the figure 8, all the input variables are 5 and the corresponding output is 13. 9, which specified at the top of the corresponding graphs. The input for each o f the input variables is specified at the top of the section corresponding to them, so also the output variable. The rule viewer for this work is presented in figure 8. [pic] Figure 8: The Rule editor e) THE SURFACE VIEWER The surface viewer shown in figure 9 is a 3-D graph that shows the relationship between the inputs and the output. The output (security Risk) is represented on the Z-axis while 2 of the inputs (Confidentiality and Integrity) are on the x and y axes and the other input (Availability) is held constant. The surface viewer shows a plot of the possible ranges of the input variables against the possible ranges of the output. 4) EVALUATION The security risk analysis system was evaluated using three newly completed software products from three different software development organizations. The output determines the security level of software under consideration. The summary of the evaluation is given in figure 11. For product A, 5 is the score for confidentiality, 5 for the integrity and 5 for the availability. |Software |Input |Output |Significance |Security Level | |Product A |5 5 5 |13. |45% slightly secure, 55% secure |46. 33 % | |Product B |8 7 8 |24. 2 |20% secure, 80% very secure |80. 60 % | |Product C |10 10 10 |28. 4 |35% very secure, 65% extremely secure |94. 67 % | Table 5 : Evaluation of Different Input Variables [pic] Figure 9 : The Surface Viewer [pic] Figure 10 : Histogram 3D CONCLUSION AND FINDING Thus, this work proposes a fuzzy logic-based technique for determination of level of security risk associated with software systems. Fuzzy logic is one of the major tools used for security analysis. The major goals of secure software which are used as the inputs to them system are the preservation of confidentiality (preventing unauthorized disclosure of information), preservation of integrity (preventing unauthorized alteration of information) and preservation of availability (preventing unauthorized destruction or denial of access or service to an authentic user). It might be necessary to redesign this system in a way that it will be deployable and will be without the use of MATLAB. It might also be necessary to use an adaptive fuzzy logic technique for security risk analysis. We have been able to design a system that can be used to evaluate the security risk associated with the production of secure software systems. This will definitely help software organizations meet up with the standard requirements. A technique for assessing security of software system before final deployment has been presented. The result of this study shows that if the software producing companies will incorporate security risk analysis into the production of software system, the issue of insecurity of software will be held to the minimum if not eliminated. This study has also revealed that if each of the software security goals can be increased to the maximum, then the level security will also be increased and the risk associated will be eliminated. Finally, security risk analysis is a path towards producing secure software and should be considered a significant activity by software development organizations. How to cite Software Security Risk Analysis Using Fuzzy Expert System, Papers