Recently Internet of Things (IoT) attains tremendous popularity, although this promising technology leads to a variety of security obstacles. The conventional solutions do not suit the new dilemmas brought by the IoT ecosystem. Conversely, Artificial Immune Systems (AIS) is intelligent and adaptive systems mimic the human immune system which holds desirable properties for such a dynamic environment and provides an opportunity to improve IoT security. In this work, we develop a novel hybrid Deep Learning and Dendritic Cell Algorithm (DeepDCA) in the context of an Intrusion Detection System (IDS). The framework adopts Dendritic Cell Algorithm (DCA) and Self Normalizing Neural Network (SNN). The aim of this research is to classify IoT intrusion and minimize the false alarm generation. Also, automate and smooth the signal extraction phase which improves the classification performance. The proposed IDS selects the convenient set of features from the IoT-Bot dataset, performs signal categorization using the SNN then use the DCA for classification. The experimentation results show that DeepDCA performed well in detecting the IoT attacks with a high detection rate demonstrating over 98.73% accuracy and low false-positive rate. Also, we compared these results with State-of-the-art techniques, which showed that our model is capable of performing better classification tasks than SVM, NB, KNN, and MLP. We plan to carry out further experiments to verify the framework using a more challenging dataset and make further comparisons with other signal extraction approaches. Also, involve in real-time (online) attack detection.

In cybersecurity, a network intrusion detection system (NIDS) is a critical component in networks. It monitors network traffic and flags suspicious activities. To effectively detect malicious traffic, several detection techniques, including machine learning-based NIDSs (ML-NIDSs), have been proposed and implemented. However, in much of the existing ML-NIDS research, the experimental settings do not accurately reflect real-world scenarios where new attacks are constantly emerging. Thus, the robustness of intrusion detection systems against zero-day and adversarial attacks is a crucial area that requires further investigation. In this paper, we introduce and develop a framework named SGAN-IDS. This framework constructs adversarial attack flows designed to evade detection by five BlackBox ML-based IDSs. SGAN-IDS employs generative adversarial networks and self-attention mechanisms to generate synthetic adversarial attack flows that are resilient to detection. Our evaluation results demonstrate that SGAN-IDS has successfully constructed adversarial flows for various attack types, reducing the detection rate of all five IDSs by an average of 15.93%. These findings underscore the robustness and broad applicability of the proposed model.

In the critical domain of fire detection, essential for safeguarding public safety and industrial assets, traditional methods have struggled with issues like low accuracy and slow response times, compromising real-time detection capabilities. Our response to this challenge is the development of a novel algorithm built upon the enhanced YOLOv7 framework, which integrates the CBS module with the cutting-edge ConvNeXtBlock to form the CN-B network module—a design that allows for effective longitudinal and transverse normalization, expanding the receptive field to capture a more comprehensive set of global information without the penalty of increased memory usage. This is further bolstered by the introduction of the SIoU loss function, which dramatically improves model convergence speed, ensuring a swift and accurate learning process. Rigorous testing on the BoWFire dataset has proven the algorithm's superior performance, boasting an average fire detection accuracy of 89.74% and improving detection speed by 2.9 milliseconds per image, outperforming the original YOLOv7 network by a substantial margin and demonstrating a 2.1% increase in precision. Comparative experiments with eight leading networks validate our model's adherence to the stringent requirements of real-time and accurate fire detection, positioning our improved YOLOv7 network as a significant advancement in the field of fire detection technology.