Internet of Things (IoT) edge sensors and the data they collect are crucial to modern business systems. Such cloud systems achieve the optimal execution of dynamic tasks by integrating remote resources. The money and lives of industry owners are at stake when hackers compromise data or forge source devices after introducing advanced persistent threats (APT). Many problems exist, such as advanced persistent threats (APTs) that inject malicious code into susceptible edge devices, unsafe data transfer, inconsistent trust across stakeholders, non-compliant data storage methods, etc. Hence, this paper proposes the Blockchain-assisted Dynamic Attack Detection Method (BCDADM) for administering inbound and outbound security in data acquisition and dissemination. Integrating end-to-end authentication using blockchain data on reputation and sequence discrimination is a security mechanism for outbound communication. The blockchain paradigm governs data collection and dissemination via processing terminal and industry-wide integrity verification and categorization. The suggested method is to apply the key aspects of the deep transfer learning (DTL) algorithm to the ResNet model. After the APT filtering at the edge is successful, the data is sent to the DHT that is linked with it. The registration, authentication, and validation of IoT sensors are guaranteed by the Consortium Blockchain (CBC) network. Data and APT detection transactions are recorded on the immutable ledger. According to the findings of the experiments, the suggested design outperforms the conventional one in terms of privacy and security.