Malware Classification Based on Multilayer Perception and Word2Vec for IoT Security

With the construction of smart cities, the number of Internet of Things (IoT) devices is growing rapidly, leading to an explosive growth of malware designed for IoT devices. These malware pose a serious threat to the security of IoT devices. The traditional malware classification methods mainly rely on feature engineering. To improve accuracy, a large number of different types of features will be extracted from malware files in these methods. That brings a high complexity to the classification. To solve these issues, a malware classification method based on Word2Vec and Multilayer Perception (MLP) is proposed in this article. First, for one malware sample, Word2Vec is used to calculate a word vector for all bytes of the binary file and all instructions in the assembly file. Second, we combine these vectors into a 256x256x2-dimensional matrix. Finally, we designed a deep learning network structure based on MLP to train the model. Then the model is used to classify the testing samples. The experimental results prove that the method has a high accuracy of 99.54%.

Malware Classification Using Machine Learning and Image Processing

Malware is routinely used for illegal reasons, and new malware variants are discovered every day. Computer vision in computer security is one of the most significant disciplines of research today, and it has witnessed tremendous growth in the preceding decade due to its efficacy. We employed research in machine-learning and deep-learning technology such as Logistic Regression, ANN, CNN, transfer learning on CNN, and LSTM to arrive at our conclusions. We have published analysis-based results from a range of categorization models in the literature. InceptionV3 was trained using a transfer learning technique, which yielded reasonable results when compared with other methods such as LSTM. On the test dataset, the transferring learning technique was about 98.76 percent accurate, while on the train dataset, it was around 99.6 percent accurate. Keywords: Malware, illegal activity, Deep learning, Network Security,

Education and Interpretation on Public Lands: Lessons from Research and New Directions

Decades of research confirm that interpretation and environmental education on public lands can accomplish a wide variety of positive outcomes for participants, ranging from personal learning and growth to stewardship behaviors both onand off-site. This research note offers a brief summary of the state-of-the-field of interpretation and environmental education research as applied to public lands. It highlights the general state of knowledge and identifies opportunities for researchers to further enhance our understanding about education on public lands to maximize benefits for visitors and managers alike. In particular, we emphasize the value of large-scale comparative studies as well as collaborative approaches to adaptive management, in which researchers support active experimentation through iterative data collection and analysis within a learning network of multiple program providers. This latter approach promotes evidenced-based learning within a larger community practice in which participants can benefit from the diverse knowledge, experiences, and data that each brings into the network.