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Russia is not the only alleged country that has cybercrime rings running in their borders. Cybercrime has been traced back to China and a well known hacker organization called Honker Union of China. For example, it recently published the following on its Website, which has been converted from Chinese to English as shown in Figure 7. Figure 7. Recently, tension has been built up between China and Japan, some of the patriotic hackers and honkers also are ready to make a move, boldly publicizing to launch network attacks on Japan.

The real war on the networks has no smoke and fire.

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Publicizing to launching cyber attacks against certain country can only give excuses for other country to establish network army and network forces. Why does the United States claim Chinese hackers a threat? The reason is to give excuses for themselves to build up a strong network army. When have you ever heard the American hackers organizing publicly to launch cyber attack against certain country? But in fact, they meet the objective of stealing sensitive information by infiltrating other countries' network systems. Therefore, the organization or the person who boldly publicized to launch network attacks against Japan is only doing a publicity stunt for themselves.

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What benefit can hacking a web page bring our country and the people? It is only a form of emotional catharsis, please do not launch any pointless attacks, the real attack is to fatally damage their network or gain access to their sensitive information. Any attack will be executed silently, rather than vigorously promoting it. And also everyone please work hard on learning technologies, as Chinese, you have no right to escape the responsibility at any time. On the issue of Japan illegally arrested our fishermen, it is not that China is easy to be bullied, but any country that starts a war will become the enemy of the international anti-war alliance, which will give certain country new excuses to send troops to maintain peace in the world, and also will bring disaster to the people.

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Please take a look at the situation China is facing today, China on the map is already being surrounded by a c-shaped ring. Every world war always broke out from where the world economy shifted to, and today, unfortunately, the world economy center is shifting to China, can China avoid a war? I want to tell the vast number of passionate young people in China, if China is in war in the next 20 years, what can you do? Are you ready???. This type of messaging goes against your typically organized crime modus operandi, as most crime syndicates would not post a manifesto and call to action.

However, it is estimated that the Honker Union of China has 80, members that can carry out nefarious activities. It is best known for its attack on the White House Website. Another group in China called Black Hawk was shut down by Chinese authorities from profiting in selling exploit tools and teaching the trade craft associated with hacking. It has been reported that Black Hawk made over 1 million USD during their time of operation with over 12, paying members.

In the following section, we discuss other tools that are used in cybercrime activity and tools such as a MSR A common tool that is used by cybercriminals is the magnetic stripe reader or writer MSR shown in Figure 7.

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  4. This allows the cybercriminal to populate and read data from credit cards and other mediums that use magnetic stripe readers. Another tool that is commonly used but requires physical interaction with the target is ATM skimming. At first glance, you might find it somewhat challenging in being able to identify the skimmer. The skimmer mounts directly over the slit where you insert you credit card, as shown in Figure 7. Additionally, skimmers often have pinhole cameras that provide the cybercriminal a visual when you enter your PIN on the ATM.

    As we mentioned, the majority of cybercrimes are conducted in a logical manner with the exception of ATM skimming, which requires you to physically deploy and harvest once the cyber actors have conducted their operation. Cybercrime is a big, lucrative business that is fueled by the almighty dollar, and the ability to cash in on the lowest common denominator in terms of attack vector. In Mobile Malware Attacks and Defense , Machine learning involves building computer applications that can learn and improve from experience. However, unlike predicting spam, only a few studies have used machine learning techniques to predict phishing.

    A distributed client-server architecture can be applied to conceal the overhead caused by machine learning techniques, albeit take advantage of their high predictive accuracy. The distributed client-server framework exploits the competitive predictive accuracy of machine learning approaches and feeds it to other classifiers running on resource-constrained devices.

    In the literature, there exist several machine learning techniques for binary classification— that is, classifiers that assign instances into two groups of data. For example, spam or phishing prediction is a binary classification problem since e-mails are either classified as legitimate or phishing based according to certain characteristics. Such techniques include logistic regression, neural networks NNet , binary trees and their derivatives, discriminate analysis DA , Bayesian networks BN , nearest neighbor NN , support vector machines SVM , boosting, bagging, and others.

    In what follows, we briefly provide an overview of some of these classifiers and illustrate how they can be used to detect phishing e-mails. Most of the machine learning algorithms discussed here are categorized as supervised machine learning, where an algorithm classifier is used to map inputs to desired outputs using a specific function. In classification problems, a classifier tries to learn several features variables or inputs to predict an output response.

    In the case of phishing classification, a classifier will try to classify an e-mail to phishing or legitimate response by learning certain characteristics features in the e-mail.

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    Applying any supervised machine learning algorithm to phishing detection consists of two steps: training and classification. During the training step, a set of compiled phishing and non-phishing messages with known status is provided as a training dataset to the classifier.

    E-mails are first transformed into a representation that is understood by the algorithms. Specifically, raw e-mails are converted to vectors using the vector space model VSM , where the vector represents a set of features that each phishing and non-phishing e-mail carries. Then the learning algorithm is run over the training data to create a classifier. The classification step follows the training learning phase. During classification, the classifier is applied to the vector representation of real data that is, the test dataset to produce a prediction based on learned experience.

    The original model of BART was not designed for classification problems; hence, a modified version, hereafter referred to as CBART, was used to render the current model applicable to classification problems in general and phishing or spam classification in particular. Note that BART is a learner set up to predict quantitative outcomes from observations via regression. There is a distinction between regression and classification problems. Regression is the process of predicting quantitative outputs. However, when predicting qualitative categorical outputs, this is called a classification problem.

    Motivated by ensemble methods in general, and boosting algorithms in particular, the basic idea of BART is to model or at least approximate f x using a sum of regression trees,. Figure 6. Note that the BART contains multiple binary trees since it is an additive model.

    Each node in the tree represents a feature in the dataset, while the terminal nodes represent the probability that a specific e-mail is phishing, given that it contains certain features. For example, according to Figure 6. These features are discussed in more detail in the following sections. An Example of a Binary Tree. The model can be considered a classification tree if the response y is discrete, or a regression tree if y is continuous.