In this article, the algorithm models used most often in machine learning are explained. This should be used as a resource to achieve a greater understanding of the fundamentals of machine learning algorithms. To help you acquire greater knowledge of the topic, some practical examples are provided.
K-Nearest Neighbors Algorithm
If you Google a question or product, suggested products will be presented on the left side. To show similar items to people, k-nearest neighbor algorithms are used by search engines or websites, such as YouTube. This algorithm is a very popular classification algorithm used in machine learning. Though it employs a simple principle, its use in various fields is widespread.
Consider an instance when we have a labeled dataset and the category to which every sample in the dataset is part is known to us. An unlabeled data sample is present at this point, and our goal is to determine the category to which the data sample belongs. The k-nearest neighbor algorithm works on the principle of determining how far the data sample that is to be marked is from every sample in the data set, and to obtain the closest k-samples. The closest k samples choose the category of which the labeled data sample is a part.
When we presume that X refers to the data sample that is to be labeled and x refers to the labeled data set, the algorithm functions as discussed below:
Arrange the distance array; obtain the closest k points and use X to denote them. Count how many categories are present in x, i.e. there are various samples of class0 in x, various samples of class1 in x, and so forth.
The popularity of the k-nearest neighbor algorithm is quite high, and it is a lazy algorithm on the whole. Because it has a non-linear nature, it included several examples of sin practical lives.
At times, movies may be recommended to you by streaming websites like Netflix and Hotstar based on the movies that have been watched by people in your demographics. You may not always like those movies; however, you may come across it on your newsfeed, being the closest sample in the dataset. Because of the non-parametric and non-feasible character of this machine learning algorithm, it has various disadvantages.
The k-nearest neighbor algorithm is used by websites of various banks and credit card companies to predict or determine if a customer requesting a loan is viable for their company or not.
Benefits: High precision and high tolerance to noise and outliers.
Drawbacks: Requires a significant amount of computing. It also has extensive memory requirements. The principle of the algorithm shows that every time an unlabeled sample is categorized, the distance has to be computed again.
Decision Tree
Classification Machine Learning algorithms that are used commonly are decision trees and random forest. The decision tree is quite popular, and its logic of judgment is similar to the human thought process in various ways. A decision tree can be created by simply using your decision logic.
One of the most well-established learning models is the decision tree. It is easy to comprehend the results it predicts, which can be elaborated without any problems in the business department. In addition, its prediction speed is high, and it is able to deal with type data as well as continuous data. In machine learning data mining job interviews, the decision tree is one of the most popular questions asked by the interviewer.
A decision tree is a fundamental and widespread supervised learning model and is used frequently to group problems and regression problems, particularly in the domains of marketing and biomedicine, chiefly because the tree structure and sales, diagnosis, and other situations under the decision-making process are quite identical. When the concept of ensemble learning is applied to the decision tree, the models of stochastic forest and gradient lifting decision tree can be obtained.
The objective of a decision tree is to create a tree-like classification structure using a group of sample data, in accordance with various features and characteristics. It is expected that it fits the training data and attains appropriate classification outcomes. However, we also expect to regulate its complexity so that generalization capability is present in the model to some extent. There may be several options for the decision tree for a specific problem. For instance, in a scenario description, if a girl considers the ability to write code at the root node, then the classification may be completed using a simplified tree structure.
In comparison to others, the decision tree is an extraordinary machine learning algorithm in comparison to others because though it is slow and exhibits a lower degree of predictability, it has several uses. The reason for this is that it is capable of effectively filtering a large amount of nonlinear data in a short span of time.
For instance, the decision tree is used by the Hubble telescope, which is a popular instrument and a lifesaver from astronomical scientists, offering a large number of images on a daily basis, to eliminate noise from its pictures. A decision tree may be used to forecast medical diagnosis following a technique of divide and conquer of differential diagnosis using extensive cumulative data that is accessible to it.
Visual Dx, a popular differential diagnosis tool for physicians, is an application that uses a decision tree to determine a diagnosis on its own when it is provided the patient data. It is simple to read decision trees and generate hypotheses, different from other machine learning algorithms that appear to be confusing when used.
Decision trees that have a significant amount of historical data, such as for sales and credit score, may be used to determine the strategy to be used subsequently to get in touch with people with the product. It is possible to use decision trees as an excellent marketing tool if a large amount of data is available that may be used to explain the customer attributes and their hopes for the product.
Random Forest
There is a lot of similarity between the random forest algorithm and decision tree strategy. However, the former handle a significantly greater number of questions, decisions, and predictive analysis.
An example can be used to provide a better explanation of this complex machine learning algorithm. Assume you are trying to plan a vacation with your newly wedded wife. Because of your overwhelming attitude, you are not able to decide which place to visit, so you ask for recommendations from your married friend. To decide a suitable place that both you and your wife may like, your friend asks you several questions, such as your preferences, habits, and likes, as well as those of your wife. He suggests a place that he believes will be the best for you and your wife for your first trip together.
However, you are not fully convinced, so you take help from other friends. These friends also ask you questions, before suggesting an appropriate spot for your vacation. After receiving different options, you decide to visit the place that receives the highest votes. This is a detailed example of the way a random forest algorithm functions in machine learning. There is extensive use of random forest machine learning algorithms in banking applications, such as PayPal, the objective being to determine fraudulent customers that are possibly spamming the entire system. Medical applications can also use this machine-learning algorithm to study an over-abused drug.
Naïve Bayes Algorithm
One of the most popular machine learning algorithms is possibly the Naïve Bayes algorithm, and a well-known fact about this algorithm is that it was used in early spam algorithms. Scientists presented the Naïve Bayes Algorithm over 200 years ago and offered a mathematical proof. The abbreviation of this algorithm – NB algorithm – is quite attractive. The fundamental idea involved in the Naïve Bayes algorithm will be presented in this section, and its basic application will be discussed, such as how to use the algorithm to identify irregular operations within the context of the web.
It can be asserted that spam is the most notorious by-product of the Internet to humans; because of its flooding, the Internet has become overloaded. In addition, it has had a serious impact on the routine life and tasks of humans. Firms and large mail service providers will often offer the ability to divert spam. One of the most widely used machine learning algorithms by these companies is based on the Naïve Bayes text classification algorithm. The key idea is to use a text classification model using Naïve Bayes by learning from the samples of normal email and spam.
The term that is usually used to signify various classification machine learning algorithms that are based on Bayes’ theorem is Bayes classification. The NB classification refers to a classification technique that depends on the independent hypothesis of Bayes theorem and characteristic settings. It was over 250 years ago that the British mathematician Bayes formulated this algorithm, which now has an unparalleled position in the information domain. The basis of NB is a straightforward assumption: for a specific target value, attributes are conditional independent from one another.
There are limited estimated factors of NB, and the algorithm is quite straightforward. To obtain a comprehensive understanding of the Naïve Bayes algorithm, a real-life example can be presented.
Entertainment websites such as Hulu usually obtain data from customers in the form of feedback to enhance their services. The positive data that is obtained from the customers can be evaluated and trained in a way that it provides a novel recommender mechanism that is going to help in enhancing trust with the user. An important part can also be played by the Naïve Bayes algorithm for an automatic document classification by employing the data that is available. This algorithm is also vital in OCR identification and machine translation as it offers precision and advanced prediction.
Logistic Regression Algorithms
The most fundamental and frequently used model in the machine learning domain is logistic regression. In addition, the derivation and expansion of the ideals of logistic regression are highly critical skills for an algorithmic engineer. The sophisticated and widespread use of logistic regression is also signified by medical pathology diagnosis, mailbox classification as spam, and bank personal credit analysis.
After opening a browser to visit a web page, familiar ads are frequently observed towards the corner of the page, which appears to be linked to the latest topics and content that the user has browsed. It makes use of logistic regression, where ad services are able to automatically determine the ads that you will click on the basis of your history, topics, content, etc.
Logistic regression is a term used to represent regression analysis and is another classification and prediction machine learning algorithm. The possibilities of future outcomes are predicted through logistic regression by presenting historical data. The dependent variable is the outcome we expect to attain, while the independent variable is the latent factor that has an impact on the outcomes. There may be one or more than one independent variable.
When there is a single independent variable, the regression analysis is known as unitary regression analysis. For example, in a football match, all the players from both the teams, the previous results, the home and away games, the time of the game, the weather, and the referee determine the team that is going to win or lose. If the result of the game is y, the winning is 1, while the loss mark is 0. This signifies a classic binary classification problem and a logistic regression algorithm can be used to solve it.
When discussing logistic regression, it is important to discuss linear regression because it is highly likely that the two terms may be confused with one another. It is frequently believed that linear regression is directly related to the target of the constant variable. More simply, the applications of linear regression aim to determine a single variable data with a high level of accuracy.
For instance, consider the number of kidnapping cases that may occur in a region in a year or the number of votes that may be received by a party candidate in the upcoming elections. On the other hand, logistic regression is different from it in the sense that it tries to determine a result by putting forward categorical questions that may bring about two or more variables. For instance, in the healthcare sector, logistic regression is used to evaluate a sudden epidemic such as Ebola.
Support Vector Machine
It is believed that the support vector machine is one of the simplest machine learning algorithms that can be used and is employed in different aspects of life, essentially in all classification problems, particularly binary classification issues. This can first be verified. Support vector machine is presented in several machine learning books as the foremost algorithm, demonstrating its significance.
With respect to SVM, the story of an angel and the devil is put forward. According to the legend, a game was played by the angel and the devil where the devil put two colored balls on the table. The angels were told by the devil to separate the balls using a stick. An angel felt that this was very easy and did it without thinking much about it. More balls were added by the devil, and as the number of balls increased, it was no longer possible to separate a few balls using the original stick.
The SVM is trying to discover the ideal place where the sticks can be kept by the angels so that there is a suitable distance between the balls on both sides and the sticks separating them. Considering the position of the stick selected by SVM for the angel, the stick is able to properly separate the two types of balls, even if new balls are added by the devil in between.
When the devil observes that the problem of distributing the ball in a linear manner with a stick has been solved by the angels, it puts forward a new challenge for the angel. It is suggested by the way the balls have been placed that there is not a single stick all over the world that can separate them flawlessly. However, the angel was quite powerful. When he hit the table, the balls flew into the air, and using psychokinesis, he pulled a piece of paper and held it between the balls. From the devil’s perspective, it seems that the balls are ideally cut by a curve.
After some time, “bored” scientists referred to the balls as “data”, the sticks as “classification surface”, the process determining the position of the biggest spaced-out stick “an optimization”, and the ability to hit a table to make the balls fly into the air as “nuclear mapping”. The paper that keeps the spheres apart in the air is known as a “classified hyperplane”. This is the legendary tale of SVM. In the practical world of machine learning, SVM encompasses all areas of knowledge and is a widely used fundamental model in interview questions.
The major use of a support vector machine is in face identification and handwriting recognition in practical life. The machine is able to differentiate a face and provides a positive variable in case it is a face and negative when it is not. Plant and animal species from all over the world were identified by well-known supercomputers, such as Watson by using SVM. The highly anticipated Google product from a few decades back called Google Goggles’ made use of a support vector machine to identify a distinct kind of text writing for handwriting detection and served as a greater resource for researchers carrying out excavations all over the primordial historical locations.
K-Means
The foremost unsupervised machine learning algorithms that have been put forward are the K-means and DBSCAN; these machine learning algorithms use less effort in data processing in comparison to supervised learning that needs human tagging.
A long time back, there were a group of monkeys, whose lives were quite simple and happy. One day, there was a fight among two monkeys due to a petty disagreement, and so two groups were formed that were soon out of control. The leader of the first group governed in a smooth manner. In the second group, a monkey suddenly rose to power by killing the original monkey king to become a king itself. Ultimately, the practices and management of the group became consistent and distinguished into two groups, which accomplished the clustering. This is a small tale used to describe the k-means algorithm.
The previous example shows how a large group is differentiated into smaller groups, for whatever reason. This process is known as clustering, and when this technique is used for the division of data into various smaller details, it becomes easier for you to obtain a comprehensive understanding of user dynamics and significance.
For instance, using the k-means machine learning algorithms in fantasy league teams can help you obtain a detailed understanding of the selected players. At times, the k-means algorithm is used in telecommunication companies to evaluate the existing call record data so that the call dynamics can be understood completely to make their service better. Researchers can use the k-means algorithm to specify an aspect of data to be studied rather than handling a significant amount of useless data.
DBSCAN Algorithm Overview
The example given above can be used to explain DBSCAN. There is no limitation on the number of smaller groups and number of original monkey kings, just the least number of members within the group. When the monkeys have the ability, they are able to bring together sufficient followers to gain the throne of the king. Hence, after many rounds of fighting, several groups of a smaller number of monkeys are created, where every group has its monkey king. This is how clustering is performed by DBSCAN.
DBSCAN refers to Density-based Clustering of Applications with Noise. DBSCAN is different from the k-means technique in that it can identify clusters of any shape and determine noise points without being aware of the number of clusters that are to be created. There is the widespread use of clustering machine learning algorithms in the fields of medicine, astronomy, e-commerce, and pharmacy. DBSCAN algorithm is particularly used by e-commerce websites like eBay and Amazon to assist their customers in selecting the products.
Consider a simple example to understand this. You are looking to purchase an Amazon Kindle, which is an e-reader device created by Amazon. While buying this product, the Amazon system will suggest the latest Amazon Kindle case as well as a touch screen pen to add to your purchase. This is how the DB clustering works in practical application. A large amount of data is examined by Amazon and patterns are determined on the basis of DB clustering to identify what has been purchased by other users when they bought Amazon Kindle. Considering this in layman’s terms will allow you to obtain a thorough understanding of clustering machine learning algorithms.
Apriori Algorithm
Another form of unsupervised learning is the association rule. Data sets are examined to determine possible association rules. The story of diapers and beer is a widely cited example. According to the legend, the data analysts at Wal-Mart found that diapers and beer are bought by a large number of customers at the same time, and so, these products are sold side by side, which increases the sales of both.
The association rule analysis leads to an objective phenomenon: a few associations are obvious, like purchasing salmon and mustard together; a few are conceivable, like diapers and beer, while a few are quite odd, like lighters and cheese. Apriori algorithm is the most popular association algorithm. This algorithm is used to identify associations among identical items to comprehend customer dynamics in a better manner.
There is extensive use of the Apriori algorithm in medical diagnosis applications to determine detailed symptoms of a specific disease following an analysis of a large number of patient records so as to offer improved and effective care to patients. Three fundamental ideas are first put forward: support, confidence, and frequent k-item sets. These concepts will be elaborated on using an example to offer an improved understanding. There is the widespread use of the Apriori algorithm in online grocery stores, which is why we will use an example of a grocery store.
Support: This identifies the product or item that is popular among the customers. For example, when bulk groceries are purchased by customers, over five customers in a group of ten purchased salt. It can be deduced from this that salt has good support among other items. Consider this from a layman perspective instead of a theoretical perspective.
Confidence: This is used to identify the item that is brought most often with salt. For instance, four out of six people who had bought salt also purchased chilies. Through confidence, we can decide to use a recommender system, or any other e-commerce application, in a grocery store.
Frequent: This will offer an evident example and help us identify whether a product can be recommended or not. It is not recommended when a frequency of lower than 1 is obtained.
The purpose of using the Apriori algorithm is to determine association rules that fulfil the lowest support threshold as well as the lowest confidence threshold. Lastly, strong rules are identified in all frequent sets, that is, association rules required by users are developed.
In mainstream ML libraries, there is limited support for Apriori; however, it can be implemented in a straightforward manner. Peter Harrington has recommended several resources that are available online in his book ‘The practice of Machine Learning’. Apriori machine learning algorithms are used by Shopify to identify recommended stores for their users. In addition, it can be used to identify adverse drug reactions by implementing complex algorithms.
FP-Growth Algorithm
Following several studies, it has been shown that better performance is exhibited by FP-Growth as compared to the Apriori algorithm. The same associative mapping is used by both to determine the frequent sets. Aeroplane booking websites use FP-Growth to look for similar flights at different destinations. The same support, confidence, and frequent parameters are used in this algorithm to obtain a comprehensive understanding of the frequent itemsets.
Markov Algorithm
Time series data exists to a large extent in the field of network security, like the order of website visits and system calls, operation command of the operator, and so on. In the practical word, issues are faced with obvious timing, like traffic lights at intersections, days when weather changes, and the situation in which we converse.
A Russian mathematician, Markovian, put forward the well-known Markov chain. The basis of the Hidden Markov Model (HMM) is the presumption that the condition of a continuous-time series is identified by events occurring before it, and the resultant time series can be referred to as the n-order Markov chain.
If we assume that the haze today is identified just by yesterday and the day before, a second-order Markov chain is created. If the weather was sunny yesterday and also on the day before yesterday, there is a 90% probability that it will be sunny today.
Now to make things a bit complicated, assume that you wish to find out about the smog in a city 2,000 km far from your location; however, you are unable to visit that area to see the condition. You can only feel the local wind, indicating that the air is hidden. It is possible to view wind conditions, and observable sequences can be determined from the obscure sequences. As there is a significant impact of wind on haze conditions, assuming that it is sunny 90% of the time when there is strong wind makes it possible to identify hidden sequences from preceding observation sequences by learning from samples. This is referred to as the implicit Markov model.
The best example to identify the Markov model in practical life is through Google page ranking. There are links on every web page and if links from a high domain rank page are available, then it is quite likely that you may receive a Google page rank. If a link from a high domain authority is present, then there are higher chances for you to become part of page one of Google ranking. Markov chain and its model are used by famous web applications such as Facebook, YouTube, and Twitter so that the most trending content can be identified.
Graph Algorithm
A straightforward and effective machine learning algorithm is a graph algorithm. It is used extensively in those fields that have a large number of unstructured network data like a social network, traffic, finance, search, and so on. There is a large amount of unstructured network data in the network security field in the wind control; hence the graph algorithm will be used.
There are a few relationships in the practical world that are difficult to present in a database table structure, like fan relations on Weibo, registration relationships among multiple domains, love handles on idol shows, etc. Here, the old data structure is useful. The edges related to a node in a directed graph have an outgoing edge and an incoming edge, and the two points related to it with a directed edge have a start and an end. On the other hand, a graph that does not have a direction to its edges is known as an undirected graph, while the lateral signifies a directed graph.
Graph machine learning algorithms are used in-cab applications like Uber to identify the smallest path that can be taken by the driver to reach your destination. A graph search is used by Facebook to easily identify mutual friends. There are various examples that can be presented with regard to graph machine learning algorithms.
Knowledge Mapping
When you search for renowned football players on Google, you will be shown a direct link to a Wikipedia entry of Ronaldo. The reason for this is that Baidu goes through the knowledge map between the various entities and relations and allows the search engine to better comprehend the intentions of the users. It then provides direct responses to the queries of the users.
When you search for a coffee café on your phone, you will be automatically shown the location of the coffee shop closest to you. The reason for this is that Google search, along with the knowledge atlas, integrates the user behavior information to offer more refined results to users that are consistent with the present condition of the search outcomes.
When searching for “Harry Potter” on Google, the results are not only relevant to Harry Potter, but also include information on Ron, Hermione, and other characters and works that the author has completed throughout her lifetime. The reason for this is that Google search uses the knowledge map to define the association between things, extend user search outcomes, and discover more content.
When looking for “Amazon” (Amazon River), for instance, the most pertinent information about Amazon is provided. There is a lot of information available about Amazon on the web, but it is not a website, it is the most searched river across the world.
The name of Greek Warrior Women will be shown if history is searched. In the future, these results will be shown in the “knowledge map” of Google search. The “Knowledge Atlas” of Google will not only receive professional information from Wikipedia, Freebase, or international overview but will also bring about an improvement in the depth and breadth of search results by performing large-scale information search analysis. Google’s database now has over 500 million items and over 3.5 billion relationships among different items. Different kinds of data are combined over the Internet and the possible relationship and value of data are further extracted.
When knowledge mapping is applied in the security domain, it is capable of examining the possible relationships between data. Integrating these possible connections can cause our thinking on data analysis to increase considerably.
Threat intelligence has gained a lot of popularity in the information security domain in the past few years. The basis of threat intelligence is extensive data analysis. The possible relationship is determined and remarkable data intelligence support is offered for other security products. Currently, it has developed as a substantial start-up company that caters to the field of threat intelligence.
In this age of connectivity, there are pervasive attack portals, and defenses are fully based on vulnerabilities or critical assets that are not accessible. Hence, if enterprises are looking to perform safe operations, they should adopt a more wide-ranging and efficient defensive outlook. To make up for this deficiency, threat intelligence has emerged and serves as an effective substitute for the conventional defense mechanisms.
On the basis of the attacker’s perspectives, threat intelligence depends on its extensive visibility and by comprehending the risks and threats throughout the internet. This enables us to understand the threats in a better way, including potential targets, instruments, and techniques employed, in addition to their knowledge of the Internet infrastructure across which the weapons are transferred, which allows them to function accurately and efficiently in case of a threat. A total of 10 intelligent companies were presented at the RSA conference of 2016, including well-known security firms Symantec and Dell Security.
Neural Network Algorithms
Generally, KNN, SVM, and other machine learning algorithms explained earlier are considered shallow learning, and the model’s recognition ability is dependent to a greater extent on the effectiveness of choice of features. In shallow learning, at least half the time should be spent on data cleaning and feature mining. These steps are often referred to as “feature engineering”, signifying its extensive workload.
The human brain signifies an intricate network of millions of neurons. A neuron is a cell that has an elongated process, including a cell body, a dendrite, and an axon. There can be one or multiple dendrites in every neuron which can receive stimuli and transfer excitation to the cell body. There is just one axon in every neuron that transfers excitement from the cell body to a different neuron or other tissue, like a muscle or gland.
Neural network algorithm involves stimulating the working principle of human neurons, various input parameters with their corresponding weights following the excitation function processing, signifying the output. The output can then be linked to the input of the subsequent stage of the neural network, which leads to the creation of a more intricate neural network.
There is a link between the biological nerve cells within the brain and other nerve cells. An artificial neural network is developed by creating a connection with the artificial nerve cells in the same manner. Various types of connections are available, and one of the most logical and most extensively used one is to create a layer by layer connection with the nerve cells. This kind of neural network is known as a feed-forward network.
In case the incorrect neurons are punished, the problem can be solved by identifying the incorrect neurons from the output layer and fine-tuning the weights of those neurons. This kind of algorithm is referred to as backpropagation.
Recurrent Neural Network
Algorithm Recurrent neural networks learn and automatically create codes and then transcribe poems and drawings.
Just like the ancients learned from the past, we have a tendency to examine things in combination with our prior experiences. We try to recall how we resolved issues like these in the past, even in those situations that we had not observed previously. This is consistent with the concept of recurrent neural networks.
There is the widespread use of recurrent neural networks in machine translation. Consider Google Translate for example, which is a popular machine translator that converts a poem from French to English. A recurrent neural network algorithm is used by this to identify its syntactical structure once every word from a group of data accessible to it has been translated. It examines the syntactical structure and grammatical rules to obtain a vivid translation, though it is not easy to execute it due to its high-power requirements.
Speech recognition and photo recognition apps also use this algorithm to identify the colors and items to the user. A recurrent neural network algorithm is used by Pandora to identify the song after listening to it from its extensive database of songs where it looks for the syntactical structure of a song.
Convolutional Neural Network
There is extensive use of convolutional neural network machine learning algorithms in image processing. It is capable of performing convolution processing and pooling and can extract sophisticated features from image data in the absence of complicated preprocessing, after which they are simultaneously examined further. There is an exponential decrease in computational complexity. A question that is put forward is how convolutional neural networks perform in the security domain.
Significant changes have been brought about in the lives of people due to image recognition like face recognition. Assessing the preceding MNIST datasets shows that the success rate of image recognition numbers was almost 95%. Hence, it is a convolutional neural network that has allowed image recognition to take off.
When an entirely connected neural network algorithm like DNN is used, every node in the preceding layer of the hidden layer has to be linked to every node in the subsequent layer. Accomplishing the training process within a limited hardware environment is next to impossible.
The issue of a large amount of computation due to complete connection within the domain of image processing can be solved by using a local connection. The theoretical foundation depends on the presumption that a thorough examination of all images that can be processed is not required by biological image recognition (comprehending the image only requires to be handled with local data). There has been extensive use of this hypothesis in the image processing domain, which demonstrates its simplicity and effectiveness.
A deep learning algorithm is an important branch of machine learning that is progressing significantly. It has a wide scope and can be used in various applications, mainly in robotics and image recognition.
Image recognition is the most recent trend in the field of technology. Face lock feature has been introduced by Apple, where some machine learning algorithms are used by the iPhone to identify your face. Though Apple has not explained how it is carried out, it has been found by researchers that convolutional neural network machine learning algorithms are used to carry out this unique function. Reality and virtual reality generations were augmented by deep learning and facial recognition in the previous years. Though they are not easy to program and execute, convolutional machine learning algorithms carry out the deep research.
Linear Models
There are several practical applications that use linear models because they are easy to implement and can be interpreted rapidly by the machines due to their straightforward nature. Linear models are used by Amazon gift cards for their implementation. It is possible to bring enhancements in linear models so that they can be used in higher-order applications, such as protein synthesis.
Linear regression: Considering a data set, it tries to learn a linear model to determine real-value output markers as precisely as it can.
Multi-Category Learning
We frequently come across multi-classification learning tasks. There are certain binary classification learning techniques that can be expanded directly to multi-classification; however, in more cases, we use binary classification to solve multi-classification issues on the basis of a few fundamental strategies.
The key idea of multi-classification learning is to avoid losing generality, which differentiates multi-classification activities in various binary classification tasks. A classifier is particularly trained for every binary classification task. The prediction outcomes of these classifiers are combined together to achieve the ultimate multi-classification outcomes. The important point is to divide multi-classification tasks and to combine multiple classifiers.
Practical Applications of Deep Learning and Machine Learning Algorithms
Captcha Detection
Captcha refers to public, completely automated program that is capable of differentiating between a computer and a human. It is designed with the aim of avoiding malicious password cracking, swiping, and forum irrigation. Captcha is now used commonly on various websites. The fundamental assumption made by Captcha is that because the computer is not able to give the answer, the user giving the answers to the questions is human. Due to the development of several captcha cracking technologies, attack and defense are complementary. The reader can go over the captcha security and other important points.
To present the recognition of digital captcha and the feature extraction techniques used in the captcha, and MNIST dataset can be used. These include one-dimensional vector, two-dimensional vector, the model employed, and the relevant verification outcomes. These consist of the k-nearest neighbor, deep learning, and support vector machine.
Spam Detection
As evident from the results of a partial survey, 80% of people working in companies claiming to have a spam-free environment waste about 10 minutes weekly in spam management. Spamming or sending spam emails is one of the unwanted side-effects associated with the advancements in the Internet, especially for those having corporate email accounts. Dealing with spam on a commercial account on daily basis can be a highly challenging task for people responsible for email management.
Currently, China comes third on the list of countries dealing with the most spam. The majority of the enterprises in China are suffering from an inefficient anti-spam atmosphere, and this problem is escalating rapidly. 85% of the system resources and a huge part of network resources employed by the mail server in commercial email service providers are invested to detect malicious spam delivered. Apart from this monumental resource loss, the network hangs and disrupts everyday email exchanges within businesses.
Spammers and hackers prefer to target corporate email accounts because they offer superior-quality communication and ease-of-access through the white-listed international anti-spam platform. As the issue of spam grows more crucial, it makes it harder for the network or the company’s server to work smoothly.
Spammers can increase their domain of action by getting into these commercial email inboxes. This also makes it easy for them to send more spam in a limited amount of time. This affects the efficiency of the commercial email users as well as anti-spam organizations and recipients of “hero broken wrist”. When email users or global companies with anti-spam systems receive spam, they add these addresses to the global spam database. In this manner, international branches of the companies stay safe from spam.
While large-scale businesses prefer to set-up their personal email systems or use commercial email security solutions such as CISCO, Blue Coat, Websense, Zscaler, and McAfee, small or medium-sized local businesses strive to deal with the detrimental effects of spam. To tackle this issue, small companies often take help from budget-friendly, small-scale service providers who provide an email system set to 163 and QQ Enterprise Mailbox as the primary professional mail service provider.
In order to design an efficient anti-spam system that can restrict spam, anti-spam organizations, and local and global email service providers must work hand in hand and share real-time data of spammers.
Emails having a large amount of data can be assessed and spam can be detected using Enron. Since the first step of boosting the performance of a machine is feeding data, this data can then help construct a training model. This model will be responsible for the detection of spam types and e-mails that contain suspicious content.
We can normally use a number of deep-learning and machine-learning principles and machine learning algorithms for spam detection. However, the Bayes algorithm has proven to be the most beneficial when dealing with spam. No wonder this algorithm is used by Gmail. The convolutional neural network is also quite efficient, but it requires more data and large-scale application.
According to the findings of the Naïve Bayes experiment, the word count of word bag extraction and the probability of detection of spam is indeed useful, but a middle way is also achievable. Combining TF and IDF will help make a word bag model which will further enhance spam detection abilities.
Numerous parameters, like the number of layers in the neural network and the text length of the sample, need to be altered to get the most out of the aforementioned methods of spam detection. For English language users, various tenses, standardization of singular and plural nouns, and use of common filler words, also needs adjustment.
Negative Review Identification
We must address the water army to begin our discussion on negative review identification. As the name suggests, the water army is a forum of many employees that form the network staff working for the network public relations company. They are responsible to reply to emails and are promoted by the employer. About a hundred thousand professionals from the force that works on a single thread-writing campaign. They can choose to work as part-time or full-time employees. These positions can also be filled by freelancers, also known as the “online water army”.
By the end of 2009, the network water army was acknowledged by CCTV as a phenomenon taking the world by storm. This water army has managed to attract several online netizens who are interested in long-term jobs. Spreading awareness within the community has contributed tremendously in attracting these individuals.
Moreover, public sentiments are most affected by the existence of such an army as it has facilitated in targeting and defaming public figures and companies. Thus, the presence of the water army has had a profound and adverse effect on the entire social structure. A number of social media giants and renowned platforms have filled their ranks generously with operational support personnel and made them responsible for manual identification of negative comments.
However, one can wonder about the possibility of refining machine learning and automating this manual process. We believe that this has been achieved to some degree with the help of advanced spam detection methods.
For instance, the IMDB dataset can be used as the first step towards the automatic identification of negative reviews. This includes the datasets employed and the extraction methods used, such as the word bag and TF-IDF model, vocabulary model, and Word2Vec and Doc2Vec models. The applied models are introduced, such as naive Bayes, support vector machine, and deep learning, along with their resultant validation results.
Harassing Phone Calls Detection
Another drawback of the freedom given through the Internet is harassment through phone calls and text messages. It is a serious crisis as statistics have shown that a citizen inputs his/her personal details online for various purposes, for instance: when filling a license or card application, online shopping, or booking an appointment with a doctor.
Companies working under different trade names ask for personal information from Internet users. Since these users do not have enough knowledge about how to keep themselves protected in an online environment, they often fall into their traps. Similarly, you may find a number of junk texts in your mobile inbox from a variety of mobile phone or private business numbers. In fact, over 90% of these messages are opened by their recipients.
Most of the random messages are sent to advertise for different products and services, but some are deceitful and may lure you into their fraud by offering mouth-watering prizes. Fraudulent messages from different provinces or even countries are sent to inform you about a lottery which you have won.
Moreover, international students renting houses might fall prey to scam messages from people pretending to be their landlord and asking them to deposit the rent in a particular account. Another target group of such messages is the senior citizens or middle-aged people who are not very familiar with the latest technology. Swindlers ask such people to transfer their savings into a “safe” account. However, fraudsters do not target a single group or class and send messages in the hope of getting lucky by fooling people.
An example of the harassment message detection technology is the SMS Spam Collection dataset. This dataset comprises feature extraction methods, which help detect common text messages meant for harassment. These methods include the word bag and TF-IDF model, vocabulary model, and Word2Vec and Doc2Vec model. Moreover, training models like Naive Bayes, support vector machine, XGBoost, and MLP machine learning algorithms along with their corresponding verification results are also introduced.
ADFA-LD dataset is one such example used for Linux backdoor detection. It employs 2-Gram and TF-IDF as feature extraction methods and Naive Bayes, XGBoost, and multilayer perceptron as classification machine learning algorithms.
Linux Backdoor Detection
Linux, the popular operating system, is an open-sourced platform and enjoys a great reputation among people working in the domain of network and security. It offers a well-maintained network and a wide range of online applications. However, one problematic aspect of this operating system is its application which makes it susceptible to attacks. In fact, even minor attacks can give a hard time to the network and system analysts hired by a company.
While intrusion detection systems are efficient in dealing with simpler issues, they fail to tackle more complicated attacks performed by experienced and dangerous hackers. This shows that machine learning would be a preferred replacement to analyze and detect intrusion. Linux backdoor detection can make use of well-trained systems that are fed with a huge amount of data.
The Inside Job conducted a survey in 2015 which showed that one or more of the company’s employees are usually responsible for a majority of the biggest cybercrime scandals that take place there. Other reasons can be denial-of-service attacks and online attacks. This means that businesses are still at risk when it comes to the protection of confidential information and assets, even though numerous security products, like data leak prevention (DLP), are available in the market.
Consider this: On April 5, 2017, DigitalOcean, a reputed VPS provider, ended up deleting its entire production database. Another incident took place on January 31, 2017, when an extremely tired system administrator tried to delete an empty directory, but the instructions were mistakenly sent to another server’s command window. He realized his mistake within 27 minutes, which was still too late as data of 295.5 GB out of 300 GB was lost.
Similarly, gitlab.com failed to retrieve database data for 6 hours. It undoubtedly sounds shocking as the risk of having your own employees, intentionally or unintentionally, harming your database is disappointing as well as highly detrimental.
Let’s look at the consequences of the incident that took place at DigitalOcean. The control panel and API of the company got stuck for 4 hours and 56 minutes. As soon as they received the alerts that the public services were down, it took them 3 minutes to realize what had happened with the primary database. Within 4 minutes, data recovery from an older database copy began. The restoration of the data back to the master backup copy took almost 4 hours.
What do these events show? Restoration of the data from the copy of an online server is a long and tiring process. The loss of valuable data and time was mainly the result of a seemingly innocent configuration mistake of an engineer, but a program for automated testing also mistakenly used certificates from the production environment. Because of this, the CEO of DigitalOcean had to issue a written apology in the end.
Malicious operations entail the unwanted and erroneous as well as planned and malicious operations performed by a company’s own employee. There is a chance that abnormal user behavior may be missed at first, but an in-depth investigation is needed to find out what is legitimate and what is malicious. Such malicious activities can only be detected and stopped on time when the company uses high-tech programs, such as User Behavior Analysis (UBA).
UBA is a state-of-the-art technology used for data security and the detection of malicious activities. It employs a special security analysis algorithm along with a user’s everyday working on the system. This algorithm can detect everything from the initial login operation to each task performed by the user. The system performs two major functions: First, it builds a standard for what’s considered normal and what’s not. Second, it promptly detects a user’s unusual behavior and alerts the security analysts to keep tabs on it.
Web Shell Detection
Web shell or web back door can be defined as a command implementation environment and may exist in the form of ASP, PHP, JSP, or CGI. It is mostly used to intrude a system and obtain limited access into the webserver via a web service port.
Platforms that test the susceptibility of the system, usually, find great use in web shell detection. Moreover, a number of scanners employing machine learning and deep learning approaches have proven to be successful in the automatic detection of web shells. A scanner with a greater sense of web-security protocols can be easily built by anyone who is proficient in programming.
Some of the common web shell detection techniques are as follows:
Static Detection: This method detects the web shell process by matching the characteristic value, the characteristic code, and the function of risky activities.
Dynamic Detection: It helps to detect the performance of time-related features, like database operations, sensitive file reading, etc.
Grammar Detection: It uses the stripping code as well as the annotation through the analysis variable, the string, the function, the language structure technique, as per the PHP language scans compilation method. While this technique has the setback of false positives, the key danger function capture is realized to efficiently address the issue of under-reporting.
