The threat of malware in the Internet of Things (IoT) is ever-present given that many IoT systems today rely on the Android operating system. There has been a consistent rise in Android malware recently, with new variants adopting sophisticated detection avoidance techniques, including various forms of obfuscation. Hence, there is a need to improve the effectiveness of Android malware detection as obfuscation becomes more prevalent in the wild. In this article, we present a novel approach for obfuscated malware detection in IoT Android applications based on the visualization of app executables with Markov images. The app images are trained using a convolutional neural network (CNN) to detect obfuscated malware and for the identification of the obfuscation type. We evaluate the performance of the proposed system by experimenting with four different classification models using 12000 Android applications. The CNN model created to distinguish between malware and benign apps obtained an accuracy of 99.41%. The model for identifying obfuscated malware from benign applications obtained 99.65% accuracy while the model created to identify obfuscated malware from non-obfuscated malware yielded an accuracy of 99.81%. The model for classifying obfuscated malware into 14 different obfuscation categories obtained an accuracy of 99.67%. These results show that CNN models trained from Markov images generated using application byte code can be highly effective for obfuscated malware detection and classification. Moreover, our proposed system provides a more sustainable and cost-effective method for obfuscated malware detection compared to the manual feature-engineering-based approaches that are more prevalent in the current literature.

This paper is based on image steganography that is Least Significant Bits (LSB) techniques on images to enhance the security of the communication. The LSB-based technique is the most challenging one because it is difficult to differentiate between the cover-object and stego-object, if few LSB bits of the cover object are replaced. The LSB approach combined with F5 algorithm and matrix embedding which is applied on both spatial and frequency domain of an image. The Mean squared error (MSE) and Peak signal to noise ratio (PSNR) was used as the performance measure to compare the different LSB techniques. This paper shows that MSE and PSNR of LSB techniques with Matrix embedding yields better results

In the current scenario, learning has taken a completely new dimension. New methods of teaching like E-learning and collaborative learning etc have computerized the learning process to a large extent. But the teachers' presence is still mandatory at all times. Also the current systems have low reliability so the teacher has to monitor them constantly. To solve this problem this paper introduces an automated question answering system. This proposed system answers the queries posted by the student in a more interactive way like a virtual teacher (chatbot system). The Q&A knowledge base can be accessed and modified by the instructor who could also know the areas where the students are more prone to doubts thus helping the student as well as the instructor.

This paper proposes software based malware capturing and detection method. Which implements an efficient malware capturing and detection method in honeypot environment. It includes collecting the logs from the network using honeypot system. It create incident table based on the logs, in the test case we are using metasploit frame work for attacking the honeypot system through LibEmu manually. In the metasploit there are thousands of malicious payloads are available, using the payloads we can exploit the vulnerabilities present in the services on the honeypot.