Spam Detection In Social Networks Research Articles

Revolutionizing Cybersecurity with Machine Learning: A Comprehensive Review and Future Directions

In the realm of computing, data science has revolutionized cybersecurity operations and technologies. The key to creating automated and intelligent security systems lies in extracting patterns or insights from cybersecurity data and building data-driven models. Data science, encompassing various scientific approaches, machine learning techniques, processes, and systems, studies real-world occurrences through data analysis. Machine learning techniques, known for their flexibility, scalability, and adaptability to new and unknown challenges, have been applied across many scientific fields. Cybersecurity is rapidly expanding due to significant advancements in social networks, cloud and web technologies, online banking, mobile environments, smart grids, and more. Various machine learning techniques have effectively addressed a wide range of computer security issues. This article reviews several machine learning applications in cybersecurity, including phishing detection, network intrusion detection, keystroke dynamics authentication, cryptography, human interaction proofs, spam detection in social networks, smart meter energy consumption profiling, and security concerns associated with machine learning techniques themselves. The methodology involves collecting a large dataset of phishing and legitimate instances, extracting relevant features such as email headers, content, and URLs, and training a machine learning model using supervised learning algorithms. These models can effectively identify phishing emails and websites with high accuracy and low false positive rates. To enhance phishing detection, it is recommended to continuously update the training dataset to include new phishing techniques and employ ensemble methods that combine multiple machine learning models for improved performance

Spam Detection in Social Networks Using Machine Learning

Many social media platforms have emerged as a result of the online social network's (OSN) rapid expansion. They have become important in day-to-day life, and spammers have turned their attention to them. Spam detection is done in two different ways, such as machine learning (ML) and expert-based detection. The expert-based detection technique’s accuracy depends on expert knowledge, and the manual process is a time-consuming task. Thus, ML-based spam detection is preferred in OSN. Spam identification on social networks is a difficult operation involving a variety of factors, and spam and ham have resulted in an imbalanced data distribution, which gives an advantage to spammers for corrupting our devices. Spam detection based on ML algorithms like Logistic Regression (LR), K-Nearest Neighbour (KNN), Decision Trees (DT), Random Forest (RF), Support Vector Machine (SVM), and XGB, Voting Classifier (VC), and many other algorithms are used to design the address balance and to attain high assessment accuracy. There is a non-balance issue. Text is vectorized by vectorizers and all the relative results are stored. The experimental result shows that, as compared to KN, NB, ETC, RF, SVC, LR, XGB, and DT, the proposed VC provides a higher classification accuracy rate of 97.96%. The proposed methods are effective in identifying balanced and imbalanced datasets, as evidenced by the validation results. The website was created to detect messages as spam or not.

Machine learning algorithm-based spam detection in social networks

Machine learning algorithm-based spam detection in social networks

Machine Learning in Cybersecurity: Techniques and Challenges

In the computer world, data science is the force behind the recent dramatic changes in cybersecurity's operations and technologies. The secret to making a security system automated and intelligent is to extract patterns or insights related to security incidents from cybersecurity data and construct appropriate data-driven models. Data science, also known as diverse scientific approaches, machine learning techniques, processes, and systems, is the study of actual occurrences via the use of data. Due to its distinctive qualities, such as flexibility, scalability, and the capability to quickly adapt to new and unknowable obstacles, machine learning techniques have been used in many scientific fields. Due to notable advancements in social networks, cloud and web technologies, online banking, mobile environments, smart grids, etc., cyber security is a rapidly expanding sector that requires a lot of attention. Such a broad range of computer security issues have been effectively addressed by various machine learning techniques. This article covers several machine-learning applications in cyber security. Phishing detection, network intrusion detection, keystroke dynamics authentication, cryptography, human interaction proofs, spam detection in social networks, smart meter energy consumption profiling, and security concerns with machine learning techniques themselves are all covered in this study. The methodology involves collecting a large dataset of phishing and legitimate instances, extracting relevant features such as email headers, content, and URLs, and training a machine-learning model using supervised learning algorithms. Machine learning models can effectively identify phishing emails and websites with high accuracy and low false positive rates. To enhance phishing detection, it is recommended to continuously update the training dataset to include new phishing techniques and to employ ensemble methods that combine multiple machine learning models for better performance.

Spam Detection in Social Networks Using Machine Learning Algorithms

Social networking websites have become more and more popular recently. Users use them to meet new people and communicate their most recent thoughts and actions to their existing acquaintances. The website among these that is growing the quickest is social media. Due to its popularity, many spammers attempt to flood actual users' accounts with spam messages. This paper considers three social networks, Twitter, Facebook, and Instagram, for experimentation. The classification of the data into spam and non-spam using four machine learning techniques, including SVM, KNN, decision trees, and Random Forest. The results obtained from the experiments show that the proposed approach can accurately detect spam in social networks. Implementing such algorithms could help social network platforms improve user experience by reducing the prevalence of spam and fraudulent activity

A feature selection approach for spam detection in social networks using gravitational force-based heuristic algorithm

A feature selection approach for spam detection in social networks using gravitational force-based heuristic algorithm

Cost-Based Heterogeneous Learning Framework for Real-Time Spam Detection in Social Networks With Expert Decisions

With the widespread use of social networks, spam messages against them have become a major issue. Spam detection methods can be broadly divided into expert-based and machine learning-based detection methods. When experts participate in spam detection, the detection accuracy is fairly high. However, this method is highly time-consuming and expensive. Conversely, methods using machine learning have the advantage of automation, but their accuracy is relatively low. This paper proposes a spam-detection framework that combines and fully exploits the advantages of both methods. To reduce the workload of the experts, all messages are first analyzed via a primary machine learning filter, and those that are determined to be normal messages are allowed through, whereas suspicious messages are flagged. The flagged messages are subsequently analyzed by an expert to enhance the overall system accuracy. In the filtering process, cost-based machine learning is used to prevent the fatal error of misidentifying a spam message as a normal message. In addition, to obviate the continuously evolving spam trends, a module that periodically updates the expert-diagnosis results on the training dataset is incorporated into the framework. The results of experiments conducted, on an imbalanced dataset of spam tweets and normal tweets in a ratio similar to the actual situation in real life, indicate that the proposed framework has a spam-detection rate of almost 92.8%, which is higher than that of the conventional machine learning technique. Furthermore, the proposed framework delivered stable high performance even in an environment where social network messages changed continuously, unlike the conventional technique, which exhibited large performance deviations.

A Heterogeneous Ensemble Learning Framework for Spam Detection in Social Networks with Imbalanced Data

The popularity of social networks provides people with many conveniences, but their rapid growth has also attracted many attackers. In recent years, the malicious behavior of social network spammers has seriously threatened the information security of ordinary users. To reduce this threat, many researchers have mined the behavior characteristics of spammers and have obtained good results by applying machine learning algorithms to identify spammers in social networks. However, most of these studies overlook class imbalance situations that exist in real world data. In this paper, we propose a heterogeneous stacking-based ensemble learning framework to ameliorate the impact of class imbalance on spam detection in social networks. The proposed framework consists of two main components, a base module and a combining module. In the base module, we adopt six different base classifiers and utilize this classifier diversity to construct new ensemble input members. In the combination module, we introduce cost sensitive learning into deep neural network training. By setting different costs for misclassification and dynamically adjusting the weights of the prediction results of the base classifiers, we can integrate the input members and aggregate the classification results. The experimental results show that our framework effectively improves the spam detection rate on imbalanced datasets.

Machine Learning Algorithms for Spam Detection in Social Networks

Most of the web based social systems like Face book, twitter, other mailing systems and social networks are developed for users to share their information, to interact and engage with the community. Most of the times these social networks will give some troubles to the users by spam messages, threaten messages, hackers and so on.. Many of the researchers worked on this and gave several approaches to detect the spam, hackers and other trouble shoots. In this paper we are discussing some tools to detect the spam messages in social networks. Here we are using RF, SVM, KNN and MLP machine learning algorithms across rapid miner and WEKA. It gives the better results when compared with other tools.

Understanding Security Threats in Spam Detection on Social Networks

Social network has become a very popular way for internet users to communicate and interact online. The socia; networks provide a platform to maintain a contact with friends. Increasing social network’s popularity allows all of them to collect large amounts of personal details about their users. Globally, the issue of identifying spammers have received great attention due to its practical relevance in the field of social network analysis. Social network community users are fed with irrelevant information while surfing, due to spammer's activity. Spam pervades any information system such as e-mail or web, social, blog or reviews platform. The aim of this paper is to examine previous works in the field of spam detection in social networks, the study attempts to review various spam detection frameworks which details about the detection and elimination of spam's in various sources, By classification and Clustering Method of spam detection and by raising security awareness among the users of social networks and stake holders , by prescribing a strategic approach or data mining approach for analyzing the nature of spam detection on social networks.

Spam Detection in Social Networks Using Correlation Based Feature Subset Selection

Spam Detection in Social Networks Using Correlation Based Feature Subset Selection