Data Mining Practical Machine Learning Tools and Techniques

Pervasive growth and usage of the Internet and mobile applications have expanded cyberspace. The cyberspace has become more vulnerable to automated and prolonged cyberattacks. Cyber security techniques provide enhancements in security measures to detect and react against cyberattacks. The previously used security systems are no longer sufficient because cybercriminals are smart enough to evade conventional security systems. Conventional security systems lack efficiency in detecting previously unseen and polymorphic security attacks. Machine learning (ML) techniques are playing a vital role in numerous applications of cyber security. However, despite the ongoing success, there are significant challenges in ensuring the trustworthiness of ML systems. There are incentivized malicious adversaries present in the cyberspace that are willing to game and exploit such ML vulnerabilities. This paper aims to provide a comprehensive overview of the challenges that ML techniques face in protecting cyberspace against attacks, by presenting a literature on ML techniques for cyber security including intrusion detection, spam detection, and malware detection on computer networks and mobile networks in the last decade. It also provides brief descriptions of each ML method, frequently used security datasets, essential ML tools, and evaluation metrics to evaluate a classification model. It finally discusses the challenges of using ML techniques in cyber security. This paper provides the latest extensive bibliography and the current trends of ML in cyber security.

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A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

Cyberspace has become an indispensable factor for all areas of the modern world. The world is becoming more and more dependent on the internet for everyday living. The increasing dependency on the internet has also widened the risks of malicious threats. On account of growing cybersecurity risks, cybersecurity has become the most pivotal element in the cyber world to battle against all cyber threats, attacks, and frauds. The expanding cyberspace is highly exposed to the intensifying possibility of being attacked by interminable cyber threats. The objective of this survey is to bestow a brief review of different machine learning (ML) techniques to get to the bottom of all the developments made in detection methods for potential cybersecurity risks. These cybersecurity risk detection methods mainly comprise of fraud detection, intrusion detection, spam detection, and malware detection. In this review paper, we build upon the existing literature of applications of ML models in cybersecurity and provide a comprehensive review of ML techniques in cybersecurity. To the best of our knowledge, we have made the first attempt to give a comparison of the time complexity of commonly used ML models in cybersecurity. We have comprehensively compared each classifier’s performance based on frequently used datasets and sub-domains of cyber threats. This work also provides a brief introduction of machine learning models besides commonly used security datasets. Despite having all the primary precedence, cybersecurity has its constraints compromises, and challenges. This work also expounds on the enormous current challenges and limitations faced during the application of machine learning techniques in cybersecurity.

Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

Deep learning to filter SMS spam

As the use of the Internet is increasing, people are connected virtually using social media platforms such as text messages, Facebook, Twitter, etc. This has led to increase in the spread of unsolicited messages known as spam which is used for marketing, collecting personal information, or just to offend the people. Therefore, it is crucial to have a strong spam detection architecture that could prevent these types of messages. Spam detection in noisy platform such as Twitter is still a problem due to short text and high variability in the language used in social media. In this paper, we propose a novel deep learning architecture based on Convolutional Neural Network (CNN) and Long Short Term Neural Network (LSTM). The model is supported by introducing the semantic information in representation of the words with the help of knowledge-bases such as WordNet and ConceptNet. Use of these knowledge-bases improves the performance by providing better semantic vector representation of testing words which earlier were having random value due to not seen in the training. Proposed Experimental results on two benchmark datasets show the effectiveness of the proposed approach with respect to the accuracy and F1-score.

Spam detection in social media using convolutional and long short term memory neural network

Data Mining is a promising field and is applied in multiple domains for its predictive capabilities. Data in the real world cannot be readily used for data mining as it suffers from the problems of multidimensionality, unbalance and missing values. It is difficult to use its predictive capabilities by novice users. It is difficult for a beginner to find the relevant set of attributes from a large pool of data available. The paper presents a Robust Prediction Model that finds a relevant set of attributes; resolves the problems of unbalanced and multidimensional real-life datasets and helps in finding patterns for informed decision making. Model is tested upon five different datasets in the domain of Health Sector, Education, Business and Fraud Detection. The results showcase the robust behaviour of the model and its applicability in various domains.

Robust Prediction Model for Multidimensional and Unbalanced Datasets

Data mining is used to extract hidden information from a large set of data. From the various data mining algorithms available, frequent data mining is one of the most relevant algorithms. This algo...

TID based data and task parallelism for frequent data mining

In recent years, the social networking sites are becoming the most popular and easiest way to connect with one another. There is a remarkable increase in the usage of social networking sites which provides various services including e-mail, instant message sharing, news groups, blogging, chat groups, and the latest trend of instagrams, snapchats. These services are provided by some popular sites like Facebook, MySpace, YouTube, Twitter as they are the most visited online sites. The rapid growth of social networking sites is resulting as major sources of the database related to numerous amounts of users and lead to privacy and security issues. A broad area of data mining is focused on providing privacy and has introduced a field known as privacy preserving data mining(PPDM). This technique includes various approaches such as generalization, suppression, randomization, anonymization and cryptography. In order to provide insight to the user about their privacy a model has been propose in this paper. The proposed model has used the machine learning algorithms based on classifications such as Decision tree (DT), K-Nearest Neighbour, Naive Bayes.

Privacy Preserving Data Mining Model for the Social Networking

With the rampant increase in the use of social media, the volume of spam text has also increase exponentially. Our research focuses on the spam detection of short text messages using classical machine learning methods. We have conducted our experiments on two datasets: Twitter dataset which we have compiled by collecting public live tweets and manually labelled them and SMS Spam dataset which is available in UCI repository. Results are compared using Accuracy and F1 measure. Since the wide use of social networking sites have led people to use the language very loosely and the length of text is limited, therefore the accuracy is not that high in case of Twitter as compared of mobile text messages.

Manisha Sharma

Papers

Robust Prediction Model for Multidimensional and Unbalanced Datasets

TID based data and task parallelism for frequent data mining

Privacy Preserving Data Mining Model for the Social Networking

Spam Detection from Short Text Messages