This approach determines the angle between any of the two lines for one of the anomaly candidate specifically where the first line is in connection with relevant data points along with centers of adjacent points and the other line is any parallel axis line. Major goal is to maintain the accuracy of detection in the circumstance of high dimensionality detecting cyber threat.

This paper focuses on an approach that can select feature subspaces of social media which have meaningful information and thereby conduct anomaly detection in the projected subspace correspondingly.

In real time applications related to social media, conventional anomaly detection techniques are not applicable as the accuracy is degraded due to higher dimensionality thereby hampering these applications leading to cyber threat. According to the results, the presented hybrid bagged model-based SMD technology achieved 98 percent accuracy. e e ectiveness of the presented method is evaluated in terms of true-negative rates, accuracy, recall, precision, false-positive rate, f-measure, and false-negative rate the outcomes of three studies are compared. Further, correlation feature selection (CFS) is employed in this research to select the required features from the preprocessed data. Moreover, tokenization, stemming, and stop word removal are performed in the preprocessing stage. e database is split into multiple sets and provided as input to each method in this procedure. e proposed framework categorizes the e-mail into ham and spam. us, this research proposes a novel machine learning-based hybrid bagging method for e-mail spam identi cation by combining two machine learning methods: random forest and J48 (decision tree). Various spam identi cation techniques have been suggested and evaluated in the past, but the results show that the more research in this regard is required to enhance accuracy and to reduce training time and error rate. e purpose of spammers is to spread false information by sending annoying messages that catch the attention of the public.

Service providers and consumers nd it challenging to distinguish between spam and nonspam e-mails. We recommended deep leaning and deep adversarial learning as the future techniques that can effectively handle the menace of spam emails. Our review compares the strengths and drawbacks of existing machine learning approaches and the open research problems in spam filtering. Discussion on general email spam filtering process, and the various efforts by different researchers in combating spam through the use machine learning techniques was done. The preliminary discussion in the study background examines the applications of machine learning techniques to the email spam filtering process of the leading internet service providers (ISPs) like Gmail, Yahoo and Outlook emails spam filters. Our review covers survey of the important concepts, attempts, efficiency, and the research trend in spam filtering. We present a systematic review of some of the popular machine learning based email spam filtering approaches. Machine learning methods of recent are being used to successfully detect and filter spam emails. The upsurge in the volume of unwanted emails called spam has created an intense need for the development of more dependable and robust antispam filters.

Spark email spamsieve