This paper presents drone classification at millimeter-wave (mmWave) radars using the deep learning (DL) technique. The adoption mmWave technology in radar systems enables better resolution and aid in detecting smaller drones. Using radar cross-section (RCS) signature enables us to detect malicious drones and suitable action can be taken by respective authorities. Existing drone classification converts the RCS signature into images and then performs drone classification using a convolution neural network (CNN). Converting every signature into an image induces additional computation overhead; further CNN model is trained considering fixed learning rate. Thus, when using CNN-based drone classification under a highly dynamic environment exhibit poor classification accuracy. This paper present an improved long short-term memory (LSTM) by introducing a weight optimization model that can reduce computation overhead by not allowing the gradient to not flow through hidden states of the LSTM model. Further, present adaptive learning rate optimizing (ALRO) model for training the LSTM model. Experiment outcome shows LSTM-ALRO achieves much better drone detection accuracies of 99.88% when compared with the existing CNN-based drone classification model.

Data-driven public security networking and computer systems are always under threat from malicious codes known as malware; therefore, a large amount of research and development is taking place to find effective countermeasures. These countermeasures are mainly based on dynamic and statistical analysis. Because of the obfuscation techniques used by the malware authors, security researchers and the anti-virus industry are facing a colossal issue regarding the extraction of hidden payloads within packed executable extraction. Based on this understanding, we first propose a method to de-obfuscate and unpack the malware samples. Additional, cross-method-based big data analysis to dynamically and statistically extract features from malware has been proposed. The Application Programming Interface (API) call sequences that reflect the malware behavior of its code have been used to detect behavior such as network traffic, modifying a file, writing to stderr or stdout, modifying a registry value, creating a process. Furthermore, we include a similarity analysis and machine learning algorithms to profile and classify malware behaviors. The experimental results of the proposed method show that malware detection accuracy is very useful to discover potential threats and can help the decision-maker to deploy appropriate countermeasures.

Recently, Unmanned Aerial Vehicles (UAVs) have made significant impacts on our daily lives with the advancement of technologies and their applications. Tracking UAVs have become more important because they not only provide location-based services, but are also faced with serious security threats and vulnerabilities. UAVs are smaller in nature, move with high speed, and operate in a low-altitude environment, which makes it conceivable to track UAVs using fixed or mobile radars. Kalman Filter (KF)-based methodologies are widely used for extracting valuable trajectory information from samples composed of noisy information. As UAVs’ trajectories resemble uncertain behavior, the traditional KF-based methodologies have poor tracking accuracy. Recently, the Diffusion-Map-based KF (DMK) was introduced for modeling uncertainties in the environment without prior knowledge. However, the model has poor accuracy when operating in environments with higher noise. In order to achieve better tracking performance, this paper presents the Uncertainty and Error-Aware KF (UEAKF) for tracking UAVs. The UEAKF-based tracking method provides a good tradeoff among preceding estimate confidence and forthcoming measurement under dynamic environments; the resulting filter is robust and nonlinear in nature. The experimental results showed that the UEAKF-based UAV tracking model achieves much better Root Mean Square Error (RMSE) performance compared to the existing particle filter-based and DMK-based UAV tracking models.

In modern society, the real-time emotion adaptive driving system for providing safety to drivers and emotion-based services are being researched. However, in the service process have problem of personal information might get leaked. Therefore, a robust personal information protection method is required for face recognition services based on real-time images. In this study, we propose a real-time streaming image based PingPong256 (PP2) algorithm, line-segment feature analysis (LFA), convolutional recurrent neural network (CRNN) model for facial sentiment analysis. The proposed method applied the PP2 algorithm to images for encryption and decryption for the security of the real-time images collected by image devices. For transmitting images to a server, LFA, as a dimensionality reduction algorithm, is used to extract facial information. PP2 encrypts and decrypts an image through a linear feedback shift register with a different length and sets a random value other than 0 so that inferring the initial value of encryption becomes difficult, and then executes the random operations approximately 1,000 times. The LFA analyzes the line segments of an image, assigns a different unique number depending on its type, and cumulatively adds them to generate a Line-Segment map (LS-map) with a size of $16\times16$ . The LS-map is used as an input of the CRNN model designed in this study, and the facial expressions are classified. Performance evaluation compares the accuracy of face recognition by using the proposed method with the loss rate for other models. Performance evaluation renders excellence to the accuracy of face recognition and loss rate comparison.

The evolution of the internet has led to the growth of smart application requirements on the go in the vehicular ad hoc network ($\mathrm{VANET}$). $\mathrm{VANET}$ enables vehicles to communicate smartly among themselves wirelessly. Increasing usage of wireless technology induces many security vulnerabilities. Therefore, effective security and authentication mechanism is needed to prevent an intruder. However, authentication may breach user privacy such as location or identity. Cryptography-based approach aids in preserving the privacy of the user. However, the existing security models incur communication and key management overhead since they are designed considering a third-party server. To overcome the research issue, this work presents an efficient security model namely secure performance enriched channel allocation ($\mathrm{S}-\mathrm{PECA}$) by using commutative $\mathrm{RSA}$. This work further presents the commutative property of the proposed security scheme. Experiments conducted to evaluate the performance of the proposed $\mathrm{S}-\mathrm{PECA}$ over state-of-the-art models show significant improvement. The outcome shows that $\mathrm{S}-\mathrm{PECA}$ minimizes collision and maximizes system throughput considering different radio propagation environments.