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Summary Twitter spam has long been a critical but difficult problem to be addressed. So far, researchers have developed a series of machine learning–based methods and blacklisting techniques to detect spamming activities on Twitter. According to our investigation, current methods and techniques have achieved the accuracy of around 87%. However, because of the problems of spam drift and information fabrication, these machine learning–based methods cannot efficiently detect spam activities in real‐life scenarios. Meanwhile, the blacklisting method also cannot catch up with the variations of spamming activities, as manually inspecting suspicious URLs is extremely timeconsuming. In this paper, we proposed a novel technique based on deep‐learning technique to address the above challenges. The syntax of each tweet will be learned through WordVector and trained by deep learning. We then constructed a binary classifier to differentiate spam and regular tweets. In experiments, we collected and labeled a 10‐day real tweet dataset as ground truth to evaluate our proposed method. We first went for empirical analysis with a series of comparisons to other methods: (1) performance of different classifiers, (2) other existing text‐based methods, and (3) nontext‐based detection techniques. According to the experiment results, our proposed method largely outperformed previous methods. We further conducted principle component analysis on typical methods to theoretically justify the outperformance of our method. We extracted all kinds of features via dimensionality reduction. It was found that our features were most distinct among all the detection methods. This well demonstrated the outperformance of our method.

The recent popular game, Pokémon GO, created two symbiotic social networks by location-based mobile augmented reality (LMAR) technique. One is in the physical world among players, and another one is in the cyber world among players' avatars. To date, there is no study that has explored the formation of

Spear Phishing is one of the most harmful cyber-attacks facing business and individuals worldwide. In recent years, considerable research has been conducted into the use of Machine Learning (ML) techniques to detect spear phishing emails. ML-based solutions may suffer from zero-day attacks-unseen attacks unaccounted for in the training data. As new attacks emerge, classifiers trained on older data are unable to detect these new variety of attacks resulting in increasingly inaccurate predictions. Spear Phishing detection also faces scalability challenges due to the growth of the required features which is proportional to the number of the senders within a receiver mailbox. This differs from traditional phishing attacks which typically perform only a binary classification between 'phishing' and 'benign' emails. Therefore, we devise a possible solution to these problems, named RAIDER: Reinforcement AIded Spear Phishing DEtectoR. A reinforcement-learning based feature evaluation system that can automatically find the optimum features for detecting different types of attacks. By leveraging a reward and penalty system, RAIDER allows for autonomous features selection. RAIDER also keeps the number of features to a minimum by selecting only the significant features to represent phishing emails and detect spear phishing attacks. After extensive evaluation of RAIDER over 11,000 emails and across 3 attack scenarios, our results suggest that using reinforcement learning to automatically identify the significant features could reduce the required features dimensions by 55% in comparison to existing ML-based systems. It also improves the accuracy of detecting spoofing attacks by 4% from 90% to 94%. In addition, RAIDER demonstrates reasonable detection accuracy even against sophisticated attack named "Known Sender" in which spear phishing emails greatly resemble those of the impersonated sender. By evaluating and updating the feature set, RAIDER is able to increase accuracy by close to 20% from 49% to 62% when detecting Known Sender attacks.