Machine Learning in Malware Detection

Machine Learning in Malware Detection 1.0 Background Research Malware was first created in 1949 by John von Neumann. Ever since then, more and more malwares are created. Antivirus company are constantly looking for a method that is the most effective in detecting malware. One of the most famous method used by antivirus company in detecting malware is the signature based detection. But over the years, the growth of malware is increasing uncontrollably. Until recent year, the signature based detection have been proven ineffective against the growth of malware. In this research, I have chosen another method for malware detection which is implementing machine learning method on to malware detection. Using the dataset that I get from Microsoft Malware Classification Challenge (BIG 2015), I will find an algorithm that will be able to detect malware effectively with low false positive error. 1.1 Problem Statement With the growth of technology, the number of malware are also increasing day by day. Malware now are designed with mutation characteristic which causes an enormous growth in number of the variation of malware (Ahmadi, M. et al., 2016). Not only that, with the help of automated malware generated tools, novice malware author is now able to easily generate a new variation of malware (Lanzi, A. et al., 2010). With these growths in new malware, traditional signature based malware detection are proven to be ineffective against the vast variation of malware (Feng, Z. et al., 2015). On the other hand, machine learning methods for malware detection are proved effective against new malwares. At the same time, machine learning methods for malware detection have a high false positive rate for detecting malware (Feng, Z. et al., 2015). 1.2 Objective To investigate on how to implement machine learning to malware detection in order to detection unknown malware. To develop a malware detection software that implement machine learning to detect unknown malware. To validate that malware detection that implement machine learning will be able to achieve a high accuracy rate with low false positive rate. 1.3 Theoretical / Conceptual Framework 1.4 Significance With Machine Learning in Malware detection that have a high accuracy and low false positive rate, it will help end user to be free from fear malware damaging their computer. As for organization, they will have their system and file to be more secure. 2.0 Literature Review 2.1 Overview Traditional security product uses virus scanner to detect malicious code, these scanner uses signature which created by reverse engineering a malware. But with malware that became polymorphic or metamorphic the traditional signature based detection method used by anti-virus is no long effective against the current issue of malware (Willems, G., Holz, T. Freiling, F., 2007). In current anti-malware products, there are two main task to be carried out from the malware analysis process, which are malware detection and malware classification. In this paper, I am focusing on malware detection. The main objective of malware detection is to be able to detect malware in the system. There are two type of analysis for malware detection which are dynamic analysis and static analysis. For effective and efficient detection, the uses of feature extraction are recommended for malware detection (Ahmadi, M. et al., 2016). There are various type of detection method, the method that we are using will b e detecting through hex and assembly file of the malware. Feature will be extracted from both hex view and assembly view of malware files. After extracting feature to its category, all category is to be combine into one feature vector for the classifier to run on them (Ahmadi, M. et al., 2016). For feature selection, separating binary file into blocks to be compare the similarities of malware binaries. This will reduce the analysis overhead which cause the process to be faster (Kim, T.G., Kang, B. Im, E.G., 2013). To build a learning algorithm, feature that are extracted with the label will be undergo classification with using any classification method for example Random Forest, Neural Network, N-gram, KNN and many others, but Support Vector Machine (VCM) is recommended for the presence of noise in the extracted feature and the label (Stewin, P. Bystrov, I., 2016). As to generate result, the learning model is to test with dataset with label to generate a graph which indicate detec tion rate and false positive rate. To find the best result, repeat the process using many other classification and create learning model to test on the same dataset. The best result will the one graph that has the highest detection rate and lowest false positive rates (Lanzi, A. et al., 2010). 2.2 Dynamic and Static Analysis Dynamic Analysis runs the malware in a simulated environment which usually will be a sandbox, then within the sandbox the malware is executed and being observe its behavior. Two approaches for dynamic analysis that is comparing image of the system before and after the malware execution, and monitors the malware action during the execution with the help of a debugger. The first approach usually give a report which will be able to obtain similar report via binary observation while the other approach is more difficult to implement but it gives a more detailed report about the behavior of the malware (Willems, G., Holz, T. Freiling, F., 2007). Static Analysis will be studying the malware without executing it which causing this method to be more safe comparing to dynamic analysis. With this method, we will dissemble the malware executable into binary file and hex file. Then study the opcode within both file to compare with a pre-generated opcode profile in order to search for malicious code that exist within the malware executable (Santos, I. et al., 2013). All malware detection will be needed either Static Analysis or Dynamic Analysis. In this paper, we will be focusing on Static Analysis (Ahmadi, M. et al., 2016). This is because, Dynamic analysis has a drawback, it can only run analysis on 1 malware at a time, making the whole analysis process to take a long time, as we have many malware that needed to be analysis (Willems, G., Holz, T. Freiling, F., 2007). As for Static Analysis, it mainly uses to analyze hex code file and assembly code file, and compare to Dynamic Analysis, Static Analysis take much short time and it is more convenient to analyze malware file as it can schedule to scan all the file at once even in offline (Tabish, S.M., Shafiq, M.Z. Farooq, M., 2009). 2.3 Features Extraction For an effective and efficient classification, it will be wise to extract feature from both hex view file and assembly view file in order to retrieve a complementary date from both hex and assembly view file (Ahmadi, M. et al., 2016). Few types of feature that are extracted from the hex view file and assembly view file, which is N-gram, Entropy, Image Representative, String Length, Symbol, Operation Code, Register, Application Programming Interface, Section, Data Define, Miscellaneous (Ahmadi, M. et al., 2016). For N-gram feature, it usually used to classify a sequence of action in different areas. The sequence of malware execution could be capture by N-gram during feature extraction (Ahmadi, M. et al., 2016).   For Entropy feature, it extracts the probability of uncertainty in a series of byte in the malware executable file, these probability of uncertainty is depending on the amount of information on the executable file (Lyda, R.,Hamrock, J,. 2007). For Image Representative feature, the malware binary file is being read into 8-bit vector file, then organize into a 2D array file. The 2D array file can be visualize as a black and gray image whereas grey are the bit and byte of the file, this feature look fo r common in bit arrangement in the malware binary file (Nataraj, L. et al., 2011). For String Length feature, we open each malware executable file and view it in hex view file and extract out all ASCII string from the malware executable, but because it is difficult to only extract the actual string without extract other non-useful element, it is required to choose important string among the extracted (Ahmadi, M. et al., 2016). For Operation Code features, Operation code also known as Opcode are a type of instruction syllable in the machine language. In malware detection, different Opcode and their frequency is extracted and to compare with non-malicious software, different set of Opcodes are identifiable for either malware or non-malware (Bilar, D., n.d.). For Register feature, the number of register usage are able to assist in malware classification as register renaming are used to make malware analysis more difficult to detect it (Christodorescu, M., Song, D. Bryant, R.E., 2005). For Application Programming Interface feature, API calling are code that call the function of other software in our case it will be Windows API. There are large number of type of API calls in malicious and non-malicious software, is hard to differentiate them, because of this we will be focusing on top frequent used API calls in malware binaries in order to bring the result closer (Top maliciously used apis, 2017). For Data Define feature, because not all of malware contains API calls, and these malware that does not have any API calls they are mainly contain of operation code which usually are db, dw, dd, there are sets of features (DP) that are able to define malware (Ahmadi, M. et al., 2016). For Miscellaneous feature, we choose a few word that most malware have in common from the malware dissemble file (Ahmadi, M. et al., 2016). Among so many feature, the most appropriate feature for our research will be N-gram, and Opcode. This is because it is proven that there two feature have the highest accuracy with low logloss. This two feature appears frequently in malware file and it already have sets of well-known features for malware. But the drawback using N-gram and Opcode are they require a lot of resource to process and take a lot of time (Ahmadi, M. et al., 2016). We will also try other feature to compare with N-gram and Opcode to verified the result. 2.4 Classification In this section, we will not review about the algorithm or mathematical formula of a classifier but rather their nature to able to have advantage over certain condition in classifying malware feature. The type of classifier that we will review will be Nearest Neighbor, NaÃÆ' ¯ve Bayes, Decision tree, Support Vector Machine and XGBOOST [21] (Kotsiantis, S.B., 2007) (Ahmadi, M. et al., 2016). As we need a classifier to train our data with the malware feature, we will need to review the classifier to choose the most appropriate classifier that are able to have the best result. The Nearest Neighbor classifier are one of the simplest method for classifying and it is normally implement in case-based reasoning [21]. As for NaÃÆ' ¯ve Bayes, it usually generates simply and constraint model and not suitable for irregular data input, which make it not suitable for malware classification because that the data in malware classification are not regular (Kotsiantis, S.B., 2007). For Decision Tree, it classify feature by sorting them into tree node base on their feature values and each branch represent the node value. Decision Tree will determine either try or false based on node value, which make it difficult to dealt with unknown feature that are not stored in tree node (Kotsiantis, S.B., 2007). For Support Vector Machine, it has a complexity model which enable it to deal with lar ge amount of feature and still be able to obtain good result from it, which make it suitable for malware classification as malware contains large number of feature (Kotsiantis, S.B., 2007). For XGBOOST, it is a scalable tree boosting system which win many machine learning competition by achieving state of art result. The advantage for XGBOOST, it is suitable for most of any scenario and it run faster than most of other classification technique (Chen, T., n.d.). To choose a Classification for our malware analysis, we will be choosing XGBOOST, as it is suitable for malware classification, it also recommended by winner from Microsoft Malware Classification Challenge (Ahmadi, M. et al., 2016). But we will also use Support Vector Machine, as it too is suitable for malware classification and we will use it to compare the result with XGBOOST to get a more accurate result. References   Ahmadi, M. et al., 2016. Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification. ACM Conference on Data and Application Security and Privacy, pp.183-194. Available at: http://doi.acm.org/10.1145/2857705.2857713. Amin, M. Maitri, 2016. A Survey of Financial Losses Due to Malware. Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies ICTCS 16, pp.1-4. Available at: http://dl.acm.org/citation.cfm?doid=2905055.2905362. Berlin, K., Slater, D. Saxe, J., 2015. Malicious Behavior Detection Using Windows Audit Logs. Proceedings of the 8th ACM Workshop on Artificial Intelligence and Security, pp.35-44. Available at: http://doi.acm.org/10.1145/2808769.2808773. Feng, Z. et al., 2015. HRSà ¢Ã¢â€š ¬Ã‚ ¯: A Hybrid Framework for Malware Detection. , (10), pp.19-26. Han, K., Lim, J.H. Im, E.G., 2013. Malware analysis method using visualization of binary files. Proceedings of the 2013 Research in Adaptive and Convergent Systems, pp.317-321. Kim, T.G., Kang, B. Im, E.G., 2013. Malware classification method via binary content comparison. Information (Japan), 16(8 A), pp.5773-5788. Kà ¼Ãƒ §Ãƒ ¼ksille, E.U., Yalà §Ãƒâ€žÃ‚ ±nkaya, M.A. Uà §ar, O., 2014. Physical Dangers in the Cyber Security and Precautions to be Taken. Proceedings of the 7th International Conference on Security of Information and Networks SIN 14, pp.310-317. Available at: http://dl.acm.org.proxy1.athensams.net/citation.cfm?id=2659651.2659731. Lanzi, A. et al., 2010. AccessMiner: Using System-Centric Models for Malware Protection. Proceedings of the 17th ACM Conference on Computer and Communications Security CCS10, pp.399-412. Available at: http://dl.acm.org/citation.cfm?id=1866353%5Cnhttp://portal.acm.org/citation.cfm?doid=1866307.1866353. Nicholas, C. Brandon, R., 2015. Document Engineering Issues in Document Analysis. Proceedings of the 2015 ACM Symposium on Document Engineering, pp.229-230. Available at: http://doi.acm.org/10.1145/2682571.2801033. Patanaik, C.K., Barbhuiya, F.A. Nandi, S., 2012. Obfuscated malware detection using API call dependency. Proceedings of the First International Conference on Security of Internet of Things SecurIT 12, pp.185-193. Available at: http://www.scopus.com/inward/record.url?eid=2-s2.0-84879830981partnerID=tZOtx3y1. Pluskal, O., 2015. Behavioural Malware Detection Using Efficient SVM Implementation. RACS Proceedings of the 2015 Conference on research in adaptive and convergent systems, pp.296-301. Santos, I. et al., 2013. Opcode sequences as representation of executables for data-mining-based unknown malware detection. Information Sciences, 231, pp.64-82. Stewin, P. Bystrov, I., 2016. Detection of Intrusions and Malware, and Vulnerability Assessment, Available at: http://dblp.uni-trier.de/db/conf/dimva/dimva2012.html#StewinB12. Willems, G., Holz, T. Freiling, F., 2007. Toward automated dynamic malware analysis using CWSandbox. IEEE Security and Privacy, 5(2), pp.32-39. Tabish, S.M., Shafiq, M.Z. Farooq, M., 2009. Malware detection using statistical analysis of byte-level file content. Proceedings of the ACM SIGKDD Workshop on CyberSecurity and Intelligence Informatics CSI-KDD 09, pp.23-31. Available at: http://portal.acm.org/citation.cfm?doid=1599272.1599278. Lyda, R.,Hamrock, J,. 2007.Using Entropy Analysis to Find Encrypted and Packed Malware. Nataraj, L. et al., 2011. Malware Imagesà ¢Ã¢â€š ¬Ã‚ ¯: Visualization and Automatic Classification. Bilar, D., Statistical Structuresà ¢Ã¢â€š ¬Ã‚ ¯: Fingerprinting Malware for Classification and Analysis Why Structural Fingerprintingà ¢Ã¢â€š ¬Ã‚ ¯? Christodorescu, M., Song, D. Bryant, R.E., 2005. Semantics-Aware Malware Detection. Top maliciously used apis. https: //www.bnxnet.com/top-maliciously-used-apis/, 2017. Weiss, S.M. Kapouleas, I., 1989. An Empirical Comparison of Pattern Recognition , Neural Nets , and Machine Learning Classification Methods. , pp.781-787. Kotsiantis, S.B., 2007. Supervised Machine Learningà ¢Ã¢â€š ¬Ã‚ ¯: A Review of Classification Techniques. , 31, pp.249-268. Chen, T., XGBoostà ¢Ã¢â€š ¬Ã‚ ¯: A Scalable Tree Boosting System.

Is Copy Protection Cracking Ethical? Essay -- Technology Hackers Copyr

Is Copy Protection Cracking Ethical? Introduction There are many types of hackers in the world. One group of hackers focuses on breaking copy protection schemes. Copy protection schemes prevent the illegal copying of software, music, etc. By cracking these schemes, people are then able to use and copy the item without the copy protection preventing or hindering their use. This process is commonly known as cracking and the hackers involved in the process are known as crackers. Cracking is commonly viewed as an illegal act in many countries as it is typically used to steal copies of the copyrighted item. However, there are benefits to cracking. It can remove the inconveniences the copy protection scheme imposes on users and in some cases allow users to work around incompatibilities with the copy protection. So the question comes down to, is it ethical for someone to crack copy protection schemes? What is Copy Protection? Copy protection comes in many forms. Its primary purpose is to prevent a person from making a perfect copy of the original. Since the creation of CD burners this technology allows copyright owners to prevent someone from making copies of their intellectual property and either giving them away for free or selling them at a cheaper price without paying royalties to the copyright owner. One type of protection is used on software products. This protection comes in two pieces. The first piece prevents a user from copying the CD. This is usually done by having a portion of the CD unreadable by CD drives. The second portion of the protection requires that the user insert the CD in the drive in order to use the application. This check involves validating the CD as an original CD which would include look... ...f the property then it is unethical. References Stanford University, Stanford Copyright & Fair Use, 2003 http://fairuse.stanford.edu/ US Copyright Office, Digital Millennium Copyright Act of 1998 Summary, December 1998 http://www.copyright.gov/legislation/dmca.pdf World Intellectual Property Organization, The Collection of Laws for Electronic Access, http://clea.wipo.int/ Business Software Alliance, Global Piracy Study, 2002 http://global.bsa.org/globalstudy/ Game Copy World, 2004 http://www.gamecopyworld.com Business Software Alliance, BSA Logo, 2004 SecuROM, 2004 http://www.securom.com SafeDisc, 2004 http://www.safedisc.com Harvard Law School, DeCSS FAQ, 2000 http://cyber.law.harvard.edu/openlaw/DVD/dvd-discuss-faq.html Wired News, DVD-Cracking Teen Acquitted, January 2003 http://www.wired.com/news/politics/0,1283,57107,00.html

Employment and Volkswagen

Explain what is novel and distinctive about the VW model The Volkswagen model is novel and distinctive by 3 concepts which I'll define below. First, the concept of the  «Ã‚  28,8 hour working week  » which basically replaced the former concept  «Ã‚   To save labor costs layoffs are inevitable  » Indeed, by keep the same wages while cutting the working hours of their employees innovates. Therefore, they are able to save 20% of the annual costs with this method. That allows the board of volkswagen to meet the requirements of the works council which were among other things to remain with the same wages. Besides, concerning the job ecurity, as far as some workers are concerned by the temporary works, that would be able in that case to transfer from one plant to another. Obviously, the suitability regarding the new plant would be carefully reviewed case by case according to their competencies and experience. As a matter of fact, the workers will not be able to decline an offer for an other job if it fits them. This has been also agreed by the collective bargaining agreement. Moreover, Volkswagen distributed all annual special payments to its workers. The change to the  «Ã‚  28,8 hour working week  » also shifted the numbers of the working days.Indeed, most employees switched to four day working week while regarding the needs of departments some switched from three to five day working week. The second new element is the  Ã‚ «Ã‚  contract  Ã‚ » between the newcomers in the market, that is to say the young people and the old ones. Indeed while being still at school in apprenticeship, the young generation of future workers would be offered the possibitlity to get into the world of labour step by step until reaching the final stage which would be a full-employment within volkswagen. Nonetheless, they will have the conditions of the part time work.Whereas, the situation is the reverse for the old generation, hence, as soon they reach 56 they can already re duce their working time and even more when they will get to their 59. So, their schedule can be reduced to 24 hours at 56 and 20 at 59. Even if Volkswagen still got to improve some points to make this measure more attractive, this aims to soflty replace the older generation and give full-employment to the young generation. This also supposed to rise the productivity. That leads us to our third element, the qualification element. Volkswagen wants their workers to e as competitive as possible and meet 4 main characteristics, To be multiskilled and mutli- functional; mobile;creative and human. To reach their goal their They target single employees from 18 to 30 and they train trough programmes to make them fulfill to the The needs of the company which are the flexibility and a  «Ã‚  highly-skilled workforce  Ã‚ » which result to a win win situation for both as during this time Volkswagen is receiving governmental subsidy while train the future  «Ã‚  high manpower standard  Ã‚ » . secure the job for the employee and gives Volkswagen a rising productivity

Bronx Tale Questions

From A Bronx Tale You can be anything you want to be. Remember the saddest thing in life is wasted talent. You can have all the talent, but if you don’t do the right thing, nothing happens. But when you do the right thing, guess what, good things happen. ***** Mickey Mantle don’t care about you, so why should you care about him? Nobody cares. ***** Trouble is like a cancer and you got to get it early. Nobody cares. Worry about yourself, your family, the people who are important. **** The people in the neighborhood who see me every day who are on my side, they feel safe because they know I’m close. That gives them more reason to love me. But the people who want to do otherwise, they think twice and that gives them more reason to fear me. It is better to loved or feared? It’s nice to be both, but it’s difficult. But if I had my choice, I’d rather be feared. Fear lasts longer than love. The trick is not to be hated. *****I tell your son to stay in school and go to college. You don’t understand – it’s the clothes, the cars, it’s the money, it’s everything – I treat him like he’s my son. He’s not your son. He’s my son. ***** Sonny’s right. The working man is a sucker. He’s wrong. It don’t take much strength to pull a trigger but try to get up every morning day after day and work for a living. Let’s see him do that. And then we’ll see who’s the real tough guy. The working man is.Your father is the tough guy. ***** They don’t respect him; they fear him. There’s a big difference. You want to be somebody? Be somebody who works for a living and takes care of his family. The saddest thing in life is wasted talent. ***** Don’t blow it, son. These people will hurt you, son. Don’t waste your talent, son. Be careful, C. Don’t lose it, C. Keep your head, C. ***** Don’t you trust anybody? Tha t’s a terrible way to live. For me, it’s the only way. Not for me, not for me.

Free Essays on Tabloids

When standing in the checkout line at the grocery store we have all caught ourselves reading tabloid headlines such as â€Å"Elvis is Alive†, â€Å"Batboy Born†, or â€Å"Abdominal Snowman Rapes Girl†. We sometimes even find ourselves picking one up and thumbing through the pages as we wait. Since their creation long, long ago society has stereotyped tabloids, their readers, and their writers. They all have held a very negative image. Picture what comes to mind when you think of someone who reads the tabloids on a regular basis, or how about the writers. Well, I am sure you probably get the same mental picture as most of America. I would like to go over these stereotypes of tabloid readers and writers, and dismiss some of them. Society has stereotyped tabloid readers to be unintelligent, low-income, silly people. According to S. Bird †For Enquirering Minds†, â€Å"An element in the stereotype of tabloid readers is that they are lonely misfits, shut-ins, or other people who lead sad lives.† (141) People don’t think that â€Å"normal people† read or believe in the tabloids, but that must not be the case. According to statistics compiled by Bird the combined weekly circulation of the six major supermarket tabloids during their peak in the 80’s rounded around ten million issues, with an estimated readership of fifty million. Fifty million Americans who were reading the tabloids cannot all be misfits or shut-ins. Obviously, tabloids are offering readers something they want. Despite this tabloids are still defined as the epitome of trash reading. Studies were done by two groups of men, Salwen and Anderson (1984) and Lehnert and Perpich (1982) to try to uncover who tabloid readers actually are and why they enjoy reading the tabloids. Salwen and Anderson found that the â€Å"typical buyer† of the tabloids were mostly white, predominately female, middle-aged to older people. The majority reported a career in skilled or cleri... Free Essays on Tabloids Free Essays on Tabloids When standing in the checkout line at the grocery store we have all caught ourselves reading tabloid headlines such as â€Å"Elvis is Alive†, â€Å"Batboy Born†, or â€Å"Abdominal Snowman Rapes Girl†. We sometimes even find ourselves picking one up and thumbing through the pages as we wait. Since their creation long, long ago society has stereotyped tabloids, their readers, and their writers. They all have held a very negative image. Picture what comes to mind when you think of someone who reads the tabloids on a regular basis, or how about the writers. Well, I am sure you probably get the same mental picture as most of America. I would like to go over these stereotypes of tabloid readers and writers, and dismiss some of them. Society has stereotyped tabloid readers to be unintelligent, low-income, silly people. According to S. Bird †For Enquirering Minds†, â€Å"An element in the stereotype of tabloid readers is that they are lonely misfits, shut-ins, or other people who lead sad lives.† (141) People don’t think that â€Å"normal people† read or believe in the tabloids, but that must not be the case. According to statistics compiled by Bird the combined weekly circulation of the six major supermarket tabloids during their peak in the 80’s rounded around ten million issues, with an estimated readership of fifty million. Fifty million Americans who were reading the tabloids cannot all be misfits or shut-ins. Obviously, tabloids are offering readers something they want. Despite this tabloids are still defined as the epitome of trash reading. Studies were done by two groups of men, Salwen and Anderson (1984) and Lehnert and Perpich (1982) to try to uncover who tabloid readers actually are and why they enjoy reading the tabloids. Salwen and Anderson found that the â€Å"typical buyer† of the tabloids were mostly white, predominately female, middle-aged to older people. The majority reported a career in skilled or cleri...

Communitarian and Cosmopolitan VIews of Citizenship Essay

Communitarian and Cosmopolitan VIews of Citizenship - Essay Example Nationality is therefore defined in terms of place of birth while citizenship is defined in terms of the rights that have been acquired that enables one to participated in the decision making process of the nation. Citizenship can be acquired and at the same time it can be lost. Therefore it can be defined as when a citizen is legally allowed to live in a country. (Pike 2000, p. 82) It has been shown the principle of citizenship arose from the concept of creation of social orders. The concept of citizenship can be traced to have been developing from the Greek civilization when there were efforts to define what could be considered as national citizenship and global citizenship. If we take a close look at the Marshall's work on successive emergence of civil, political and society right in citizenship, there is a clear evidence that citizenship arose from the need to have social order. In this definition we will find the citizenship has been used as a market of membership in the polities raging from the pre-modern city states and empires to the current status of global citizenship that has been well applied all over the world. (Heater 2004, p. 42) The concept of citizenship can be traced to many years ago when there were efforts by ... Though it was by then limited to only the state, it has been expanding to include the view of global citizenship. But there were also efforts by the Greek philosophers to try and define the concept of global citizenship. For example Greek Cynic philosopher Diogenes replied many people that he was a citizen of the world. There have been many philosophers since then who have expressed that their citizenship belongs to the world. Implication and application of the citizenship Citizenships has bee applied well in the modern societies. It has been used to define membership to a nation. In this case it has been successfully used to define citizens and outsiders or foreigners. Citizenship has been used to define who belongs to nation and those who doesn't belong. Those who are considered as citizens are given the rights to vote in that particular nation while those who are not citizens though living in a nation are not allowed to vote. Therefore it has been used to define political rights that are given to individuals. Citizenship has become one of the most controversial issues in the modern world. There have been conflicts of foreigners living in a nation and the issue of immigrants and their status has not escaped the world either. Citizenship has implied to mean the actual number of those who legally allowed participation in the affairs of a country. Cosmopolitan and communitarian view In the view of cosmopolitan's paradigm, cosmopolitan citizenship is defined in terms of freedom. In this case it is seen as way in which the capacity of individual to look into the possibility of personal choice within the social system is considered as the ultimate object that

Neurology Clerkship Thesis Chapter 4 Example | Topics and Well Written Essays - 1000 words

Neurology Clerkship Chapter 4 - Thesis Example In addition, the problems and sub-problems that directed the researcher were defined in detail. This research explained the effectiveness of instructional materials used by medical lecturers when preparing medical students for future tasks. From this chapter, it will be evident that the principles of effective teaching of the past decade have come of age. Nowadays, technology has evolved, and there is a need for effective teaching techniques for doctors. The research has demonstrated the existence of a technology-driven era of medicine. From statistics, it is clear that there have been increases in the use of modern technology in the field of medical education. The first chapter enlisted modern technology used in medical schools. This includes interactive whiteboards, and Apple’s iPad and the iBook that are used to access the eBook. More recently, as evidenced in the first chapter of this research, many medical colleges have incorporated E-learning in their educational strategies, especially the use of eBooks. Educational experts argue that eBooks are often portable and facilitate learning ‘on the go.’ Moreover, medical institutions prefer eBooks because it encourages self-directed learning, which is very essential to every doctor in the practice. The chapter concludes by listing the main assumptions used while conducting this research. There was a total of six assumptions, and all this concentrated on the issue of familiarization with eBook applications. Furthermore, there was the definition of all necessary terms to be used in the research, and the main limitations encountered during the research process. The second chapter examined the body of language that exist in the developing and designing of an instructional eBook for medical students within a clinical setting. There was a systematic review of the literature and the chapter started off by discussing the best practices for clinical instruction. It is important to