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.

/pdf/a-survey-on-machine-learning-techniques-for-cyber-security-i849l0zme0.pdf

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

Classifying spam is a topic of ongoing research in the area of natural language processing, especially with the increase in the usage of the Internet for social networking. This has given rise to the increase in spam activity by the spammers who try to take commercial or non-commercial advantage by sending the spam messages. In this paper, we have implemented an evolving area of technique known as deep learning technique. A special architecture known as Long Short Term Memory (LSTM), a variant of the Recursive Neural Network (RNN) is used for spam classification. It has an ability to learn abstract features unlike traditional classifiers, where the features are hand-crafted. Before using the LSTM for classification task, the text is converted into semantic word vectors with the help of word2vec, WordNet and ConceptNet. The classification results are compared with the benchmark classifiers like SVM, Naive Bayes, ANN, k-NN and Random Forest. Two corpuses are used for comparison of results: SMS Spam Collection dataset and Twitter dataset. The results are evaluated using metrics like Accuracy and F measure. The evaluation of the results shows that LSTM is able to outperform traditional machine learning methods for detection of spam with a considerable margin.

Optimizing semantic LSTM for spam detection

This article describes how spam detection in the social media text is becoming increasing important because of the exponential increase in the spam volume over the network. It is challenging, espec...

Spam Detection on Social Media Using Semantic Convolutional Neural Network

This paper is an insight about the online social media which is the most common form of media now a days and is been used most widely. It throws light on types of social media, various types of users and its major functional blocks.

Social Media: A Review

The acceptance of any vaccine relies on the belief and perception towards it. After a wait of almost 10 months, the COVID-19 vaccine is ready with the first phase in progress in India. The aim of this study is to assess the impact on the acceptance intentions of COVID-19 vaccine among the general population of India after the vaccine is inoculated to health care workers in the first phase. An empirical study was conducted by analyzing the data collected by a self-administered questionnaire. The various variables that were addressed were the socio-demographic variables, past behavior of participants towards such seasonal
influenza vaccine, awareness about the vaccine and adoption intention post vaccination drive. Logistic regression was used to identify the association between various variables and the predicting variables for the vaccine acceptance. Majority of them were ready for the COVID-19 vaccine. However, there was a decline in the acceptance rate post vaccination drive. Age, Gender and Region were found as the major factors affecting this decision.
Aims: To analyse, 1. The shift in confidence level in COVID-19 vaccine; 2. The role of Social Influence (SI) towards COVID-19 vaccine; 3. The role of past behavior towards seasonal influenza vaccines (Swine Flu, Ebola or similar) in acceptance of COVID-19 vaccine; 4. The association bet ween awareness of the COVID-19 vaccine and rate of adoption.
Materials and Methods: The study was conducted by analyzing the data collected by a self-administered questionnaire that was shared online across India in January 2021 – February 2021. The variables that were addressed through the questionnaire were the socio-demographic variables, past behavior of participants towards such seasonal influenza vaccine, awareness about the vaccine and adoption intention post vaccination drive. Associations between various variables were observed during analysis. Logistic regression was also used to identify the predicting

https://www.jmra.in/journal-article-file/14086

Gauri Jain

Papers

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

Optimizing semantic LSTM for spam detection

Spam Detection on Social Media Using Semantic Convolutional Neural Network

Social Media: A Review

COVID-19 Vaccination drive: Impact on the acceptance of vaccine among the general population of India