The Internet of Things (IoT) is currently seeing tremendous growth due to new technologies and big data. Research in the field of IoT security is an emerging topic. IoT networks are becoming more vulnerable to new assaults as a result of the growth in devices and the production of massive data. In order to recognize the attacks, an intrusion detection system is required. In this work, we suggested a Deep Learning (DL) model for intrusion detection to categorize various attacks in the dataset. We used a filter-based approach to pick out the most important aspects and limit the number of features, and we built two different deep-learning models for intrusion detection. For model training and testing, we used two publicly accessible datasets, NSL-KDD and UNSW-NB 15. First, we applied the dataset on the Deep neural network (DNN) model and then the same dataset on Convolution Neural Network (CNN) model. For both datasets, the DL model had a better accuracy rate. Because DL models are opaque and challenging to comprehend, we applied the idea of explainable Artificial Intelligence (AI) to provide a model explanation. To increase confidence in the DNN model, we applied the explainable AI (XAI) Local Interpretable Model-agnostic Explanations (LIME ) method, and for better understanding, we also applied Shapley Additive Explanations (SHAP).

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Internet of Things (IoT) applications are growing in popularity for being widely used in many real-world services. In an IoT ecosystem, many devices are connected with each other via internet, making IoT networks more vulnerable to various types of cyber attacks, thus a major concern in its deployment is network security and user privacy. To protect IoT networks against various attacks, an efficient and practical Intrusion Detection System (IDS) could be an effective solution. In this paper, a novel anomaly-based IDS system for IoT networks is proposed using Deep Learning technique. Particularly, a filter-based feature selection Deep Neural Network (DNN) model where highly correlated features are dropped has been presented. Further, the model is tuned with various parameters and hyper parameters. The UNSW-NB15 dataset comprising of four attack classes is utilized for this purpose. The proposed model achieved an accuracy of 84%. Generative Adversarial Networks (GANs) were used to generate synthetic data of minority attacks to resolve class imbalance issues in the dataset and achieved 91% accuracy with balanced class dataset.

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IoT has gained immense popularity recently with advancements in technologies and big data. IoT network is dynamically increasing with the addition of devices, and the big data is generated within the network, making the network vulnerable to attacks. Thus, network security is essential, and an intrusion detection system is needed. In this paper, we proposed a deep learning-based model for detecting intrusions or attacks in IoT networks. We constructed a DNN model, applied a filter method for feature reduction, and tuned the model with different parameters. We also compared the performance of DNN with other machine learning techniques in terms of accuracy, and the proposed DNN model with weight decay of 0.0001 and dropout rate of 0.01 achieved an accuracy of 0.993, and the reduced loss on the NSL-KDD dataset having five classes. DL models are a black box and hard to understand, so we explained the model predictions using LIME.

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IoT is widely used in many fields, and with the expansion of the network and increment of devices, there is the dynamic growth of data in IoT systems, making the system more vulnerable to various attacks. Nowadays, network security is the primary issue in IoT, and there is a need for the system to detect intruders. In this paper, we constructed a deep learning CNN model for NIDS and utilized the NSL-KDD benchmark dataset, consisting of four attack classes, for evaluating the model’s performance. We applied the filter method for feature reduction where highly correlated features are dropped. Our 2D-CNN model achieved an accuracy of 99.4% with reduced loss. We also compared the performance of DNN and CNN models in terms of accuracy and other evaluation metrics.@en

In recent years, the explosive growth of the Internet has led to an increment in the number of Distributed Denial of Service (DDoS) attacks. HTTP Flooding is a critical DDoS attack that targets HTTP servers to prohibit users from receiving HTTP services. Moreover, it saturates the link bandwidth and consumes network resources. Because the attack is launched at the application layer, it is difficult to defend against it using current countermeasures such as firewall or Intrusion Prevention System (IPS). In this paper, we propose SHFD, which leverages the Software-Defined Networking (SDN) paradigm to mitigate HTTP flooding attacks. We implement SHFD as a defender module on the SDN controller to detect and mitigate the attack in the first place. Experimental results gathered from Mininet confirm that SHFD brings a significant improvement of 13% in detection time and 29% in the number of blocked malicious flows compared to the state-of-the-art approaches.

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