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Security has become one of the important factors for any network communication and transmission of data packets. An organization with an optimal security system can lead to a successful business and can earn huge profit on the business they are doing. Different network devices are linked to route, compute, monitor, and communicate various real-time developments. The hackers are trying to attack the network and want to draw the organization’s significant information for its own profits. During the communication, if an intrusion or eavesdropping occurs, it will lead to a severe disfigurement of the whole communication network, and the data will be controlled by wrong malicious users. Identification of attack is a way to identify the security violations and analyze the measures in a computer network. An identification system, which is effective and accurate, can add security to the existing system for secure and smooth communication among end to end nodes and can work efficiently in the identification of attack on data packets. The role of information security is to design and protect the entire data of networks and maintain its confidentiality, integrity, and availability for their right users. Therefore, there is a need for end to end security management, which will ensure the security and privacy of the network and will save the data inside networks from malicious users. As the network devices are growing, so the level of threats is also increasing for these devices. The proposed research is an endeavor toward the detection of data packets attack by using the rough set theory for a secure end to end communication. The experimental work was performed by the RSES tool. The accuracy of the K-NN was 88% for the total objects of 8459. For cross validation purposes, the decision rules and decomposition tree algorithms were used. The DR algorithm showed accuracy of 59.1%, while the DT showed accuracy of 61.5%. The experimental results of the proposed study show that the research is capable of detecting data packets attack.

Molecular generation is an important but challenging task in drug design, as it requires optimization of chemical compound structures as well as many complex properties. Most of the existing methods use deep learning models to generate molecular representations. However, these methods are faced with the problems of generation validity and semantic information of labels. Considering these challenges, we propose a cross-adversarial learning method for molecular generation, CRAG for short, which integrates both the facticity of VAE-based methods and the diversity of GAN-based methods to further exploit the complex properties of Molecules. To be specific, an adversarially regularized encoder-decoder is used to transform molecules from simplified molecular input linear entry specification (SMILES) into discrete variables. Then, the discrete variables are trained to predict property and generate adversarial samples through projected gradient descent with corresponding labels. Our CRAG is trained using an adversarial pattern. Extensive experiments on two widely used benchmarks have demonstrated the effectiveness of our proposed method on a wide spectrum of metrics. We also utilize a novel metric named Novel/Sample to measure the overall generation effectiveness of models. Therefore, CRAG is promising for AI-based molecular design in various chemical applications.

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