The experimental results concluded that the proposed method is more successful than other methods or showed the same performance even though it did not use manual feature extraction techniques. The performance of the proposed algorithm was also compared with the results of studies conducted with the same data set in the literature. In addition to this, the performances of classical machine learning algorithms, neural networks, and the proposed multimodal convolutional neural networks-based deep learning algorithm are compared, and their performance is revealed. In this study, a novel multimodal convolutional neural network-based deep learning architecture and singular value decomposition-based image feature extraction method are proposed to classify malware files using intermediate-level feature fusion. However, the capability of deep learning methods to automatically extract complex features in a way simplifies this arduous process. The studies carried out to classify malware with statistical machine learning-based analysis methods are generally based on complex and challenging feature extraction methods, and manual feature extraction is a very tedious process. Some of these methods are basically based on statistical analysis, some on static and dynamic analysis methods, and some on machine learning methods. Today, there are many different methods for analyzing and detecting malware. Model achieves an accuracy of 97% with an error rate of 1.2%. Study uses a profile hidden Markov network to select and train the network structure and employs the deep neural network as a classifier network structure.
The study proposes a string match algorithm used as deep learning ensemble on a hybrid spam filtering technique to normalize noisy features, expand text and use semantic dictionaries of disambiguation to train underlying learning heuristics and effectively classify SMS into legitimate and spam classes. An effective spam filter studies are limited as short-text message service (SMS) are 140bytes, 160-characters, and rippled with abbreviation and slangs that further inhibits the effective training of models. Spams are unsolicited message or inappropriate contents. Mobile smartphones continue to adopt the use of short messages services accompanied with a scenario for spamming to thrive. This popularity, usage and adoption ease, mobility, and portability of the mobile smartphone devices have allowed for its acceptability and popularity. Advances in technology and the proliferation of mobile device have continued to advance the ubiquitous nature of computing alongside their many prowess and improved features it brings as a disruptive technology to aid information sharing amongst many online users.
0 kommentar(er)
