Enhancing Intrusion Detection and Mitigation in Ad Hoc Networks Using an AI-Driven Deep Learning Approach

In order to solve the problem of network intrusion detection in the field of network security, the application and effect analysis of deep reinforcement learning in computer network traffic management were proposed. By constructing deep Q network (DQN) as the main model, it is applied to network traffic analysis and intrusion detection tasks. In the experiment, NSL-KDD data set was used to train and test the model. The experimental results show that DQN is significantly improved compared with DNN in terms of accuracy, reaching 0 94, DQN can more accurately predict network intrusion behavior and improve the overall accuracy. DQN also excels in precision and recall. Its accuracy is 0.95, compared with that of DNN 89. At the same time, the recall rate of DQN is 0 91, relative to 0 84 has also improved to a certain extent, that is, DQN can more effectively capture real intrusion events. Conclusion: Through the interactive learning between intelligent system and environment, DQN shows higher intelligence and accuracy in network intrusion detection. The experimental results provide strong support for network security technology based on reinforcement learning, and provide new ideas and methods for future network intrusion detection research and application.

The security of the Industrial Internet of Things (IIoT) is of vital importance, and the Network Intrusion Detection System (NIDS) plays an indispensable role in this. Although there is an increasing number of studies on the use of deep learning technology to achieve network intrusion detection, the limited local data of the device may lead to poor model performance because deep learning requires large-scale datasets for training. Some solutions propose to centralize the local datasets of devices for deep learning training, but this may involve user privacy issues. To address these challenges, this study proposes a novel federated learning (FL)-based approach aimed at improving the accuracy of network intrusion detection while ensuring data privacy protection. This research combines convolutional neural networks with attention mechanisms to develop a new deep learning intrusion detection model specifically designed for the IIoT. Additionally, variational autoencoders are incorporated to enhance data privacy protection. Furthermore, an FL framework enables multiple IIoT clients to jointly train a shared intrusion detection model without sharing their raw data. This strategy significantly improves the model's detection capability while effectively addressing data privacy and security issues. To validate the effectiveness of the proposed method, a series of experiments were conducted on a real-world Internet of Things (IoT) network intrusion dataset. The experimental results demonstrate that our model and FL approach significantly improve key performance metrics such as detection accuracy, precision, and false-positive rate (FPR) compared to traditional local training methods and existing models.

Attention-Based Hybrid Deep Learning Model for Intrusion Detection in IIoT Networks

The volume of data transferred across communication infrastructures has recently increased due to technological advancements in cloud computing, the Internet of Things (IoT), and automobile networks. The network systems transmit diverse and heterogeneous data in dispersed environments as communication technology develops. The communications using these networks and daily interactions depend on network security systems to provide secure and reliable information. On the other hand, attackers have increased their efforts to render systems on networks susceptible. An efficient intrusion detection system is essential since technological advancements embark on new kinds of attacks and security limitations. This paper implements a hybrid model for Intrusion Detection (ID) with Machine Learning (ML) and Deep Learning (DL) techniques to tackle these limitations. The proposed model makes use of Extreme Gradient Boosting (XGBoost) and convolutional neural networks (CNN) for feature extraction and then combines each of these with long short-term memory networks (LSTM) for classification. Four benchmark datasets CIC IDS 2017, UNSW NB15, NSL KDD, and WSN DS were used to train the model for binary and multi-class classification. With the increase in feature dimensions, current intrusion detection systems have trouble identifying new threats due to low test accuracy scores. To narrow down each dataset’s feature space, XGBoost, and CNN feature selection algorithms are used in this work for each separate model. The experimental findings demonstrate a high detection rate and good accuracy with a relatively low False Acceptance Rate (FAR) to prove the usefulness of the proposed hybrid model.

<span lang="EN-US">With cyber threats becoming increasingly sophisticated, existing intrusion detection systems (IDS) in next generation networks (NGNs) are subjected to more false-positives and struggles to offer robust security feature, highlighting a critical need for more adaptive and reliable threat detection mechanisms. This research introduces a novel IDS that leverages a dueling deep Q-network (DQN) a reinforcement learning algorithm within game-theoretic framework simulating a multi-agent adversarial learning scenario to address these challenges. By employing a customized OpenAI Gym environment for realistic threat simulation and advanced dueling DQN mechanisms for reduced overestimation bias, the proposed scheme significantly enhances the adaptability and accuracy of intrusion detection. Comparative analysis against current state-of-the-art methods reveals that the proposed system achieves superior performance, with accuracy and F1-score improvements to 95.02% and 94.68%, respectively. These results highlight the potential scope of the proposed adaptive IDS to provide a robust defense against the dynamic threat landscape in NGNs.</span>

Network intrusion detection is of great significance for network security in Local Area Network (LAN). Traditional methods such as firewalls do not completely protect against attacks on the LAN due to lack of continuous learning. Recently, the ability of convolutional neural networks (CNN) to extract features in the field of computer vision has received extensive attention. CNN can automatically extract effective complex features to adapt to constantly changing environments, which is especially important in network intrusion detection. In this paper, we focus on network security in the LAN. We propose an approach based on CNN to implement intrusion detection in LAN. This approach can effectively identify network attacks and has an accuracy of 98.34% on the KDD99 dataset. The experimental results show that the proposed approach based on the CNN has high accuracy in intrusion detection.

With the development of computer network technology, network security has become more and more important, and intrusion detection has become an important means of network attack detection. In recent years, machine learning has played an irreplaceable role in many fields. In order to improve the accuracy of intrusion detection, many machine learning algorithms have been applied in intrusion detection models. Through the learning of training samples in KDDCUP99 intrusion data, this paper uses the relevant theory of neural network to construct an intrusion detection classification model based on optimized convolutional neural network and long short-term memory network, which is used to distinguish between normal state and various intrusion states. Convolutional neural networks, deep neural networks and traditional decision tree algorithms are compared in details in terms of accuracy and loss. The experimental results show that the prediction accuracy of the algorithm proposed in this paper is 0.972, and the test loss is 0.045, which effectively improves the classification accuracy of intrusion detection. Finally, the future development direction and prospects of the algorithm are prospected to further improve the security of computer networks.

Network intrusion detection system plays an important role in network security. Aiming at the problem that it is difficult to extract subtle intrusion features in the process of intrusion detection, a network intrusion detection model based on neural network feature extraction and particle swarm optimization algorithm to optimize support vector machine is proposed. In this method, the one-dimensional network data is constructed into two-dimensional matrix data, which is used as the input of convolutional neural network, and the feature information is extracted from the full connection layer. Finally, the accuracy of intrusion detection is improved by the optimized classifier. In order to verify the detection performance of this method, this model is compared with two-dimensional convolutional neural network and particle swarm optimization algorithm to optimize support vector machine. The experimental results show that the model can not only improve the accuracy of intrusion detection, but also perform well in small sample detection.

Intrusion detection is a critical component of secure information systems. Data Intrusion Detection Processing System often contains a lot of redundancy and noise features, bringing the system a large amount of computing resources, a long training time, a poor real-time, and a bad detection rate. For high dimensional data, feature selection can find the information-rich feature subset, thus enhance the classification accuracy and efficiency. Based on a improved feature selection algorithm, this paper proposes a lightweight intrusion detection model with computational efficiency and high detection accuracy. The algorithm is based on information gain and SVM. Its principle is to group all data features according to information gain, and then to choose the feature subset with the best classification accuracy according to SVM algorithm(the classification accuracy of SVM is defined as intrusion Detection accuracy). The experimental results demonstrated that our approach can find features subsets with higher classification accuracy compared with feature selection algorithm based on information gain and GA.

Network intrusion detection is an important component of network security. Currently, the popular detection technology used the traditional machine learning algorithms to train the intrusion samples, so as to obtain the intrusion detection model. However, these algorithms have the disadvantage of low detection rate. Deep learning is more advanced technology that automatically extracts features from samples. In view of the fact that the accuracy of intrusion detection is not high in traditional machine learning technology, this paper proposes a network intrusion detection model based on convolutional neural network algorithm. The model can automatically extract the effective features of intrusion samples, so that the intrusion samples can be accurately classified. Experimental results on KDD99 datasets show that the proposed model can greatly improve the accuracy of intrusion detection.

In order to improve the ability of network intrusion detection and blind separation, an improved network intrusion detection algorithm is proposed based on improved neural network. The network intrusion information transmission channel model is constructed, the feature extraction and signal separation of network intrusion are carried out by adaptive weighted control method, and the correlation parameters of network intrusion are estimated by combining time-frequency joint estimation method. The node location and intrusion intensity of network intrusion are calculated accurately, and intrusion detection is carried out according to the result of parameter estimation. BP neural network is used to classify and identify network intrusion, the accuracy of intrusion detection and the ability of blind source location are improved. The simulation results show that the accuracy of network intrusion detection is higher, and the location accuracy of intrusion information source is higher, and the network security performance is improved.

Attacks against computer system are viewed to be the most serious threat in the modern world. A zero-day vulnerability is an unknown vulnerability to the vendor of the system. Deep learning techniques are widely used for anomaly-based intrusion detection. The technique gives a satisfactory result for known attacks but for zero-day attacks the models give contradictory results. In this work, at first, two separate environments were setup to collect training and test data for zero-day attack. Zero-day attack data were generated by simulating real-time zero-day attacks. Ranking of the features from the train and test data was generated using explainable AI (XAI) interface. From the collected training data more attack data were generated by applying time series generative adversarial network (TGAN) for top 12 features. The train data was concatenated with the AWID dataset. A hybrid deep learning model using Long short-term memory (LSTM) and Convolutional neural network (CNN) was developed to test the zero-day data against the GAN generated concatenated dataset and the original AWID dataset. Finally, it was found that the result using the concatenated dataset gives better performance with 93.53% accuracy, where the result from only AWID dataset gives 84.29% accuracy.

We focus on detecting intrusions in ad hoc networks using the misuse detection technique. We allow for detection modules that periodically fail to detect attacks and also generate false positives. Combining theories of hypothesis testing and approximation algorithms, we develop a framework to counter different threats while minimizing the resource consumption. We obtain computationally simple optimal rules for aggregating and thereby minimizing the errors in the decisions of the nodes executing the intrusion detection software (IDS) modules. But, we show that the selection of the optimal set of nodes for executing the IDS is an NPhard problem. We describe a polynomial complexity, distributed selection algorithm, Maximum Unsatisfied Neighbors in Extended Neighborhood (MUNEN) that attains the best possible approximation ratio. The aggregation rules and MUNEN can be executed by mobile nodes with limited processing power. The overall framework provides a good balance between complexity and performance for attaining robust intrusion detection in ad hoc networks. Comments Copyright 2006 IEEE. Reprinted from Proceedings of the 25th IEEE International Conference on Computer Communications (INFOCOM 2006), April 2006, 14 pages. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. This journal article is available at ScholarlyCommons: http://repository.upenn.edu/ese_papers/333 A Statistical Framework for Intrusion Detection in Ad Hoc Networks Dhanant Subhadrabandhu, Saswati Sarkar, Farooq Anjum Abstract— We focus on detecting intrusions in ad hoc networks using the misuse detection technique. We allow for detection modules that periodically fail to detect attacks and also generate false positives. Combining theories of hypothesis testing and approximation algorithms, we develop a framework to counter different threats while minimizing the resource consumption. We obtain computationally simple optimal rules for aggregating and thereby minimizing the errors in the decisions of the nodes executing the intrusion detection software (IDS) modules. But, we show that the selection of the optimal set of nodes for executing the IDS is an NP-hard problem. We describe a polynomial complexity, distributed selection algorithm, “Maximum Unsatisfied Neighbors in Extended Neighborhood” (MUNEN) that attains the best possible approximation ratio. The aggregation rules and MUNEN can be executed by mobile nodes with limited processing power. The overall framework provides a good balance between complexity and performance for attaining robust intrusion detection in ad hoc networks. We focus on detecting intrusions in ad hoc networks using the misuse detection technique. We allow for detection modules that periodically fail to detect attacks and also generate false positives. Combining theories of hypothesis testing and approximation algorithms, we develop a framework to counter different threats while minimizing the resource consumption. We obtain computationally simple optimal rules for aggregating and thereby minimizing the errors in the decisions of the nodes executing the intrusion detection software (IDS) modules. But, we show that the selection of the optimal set of nodes for executing the IDS is an NP-hard problem. We describe a polynomial complexity, distributed selection algorithm, “Maximum Unsatisfied Neighbors in Extended Neighborhood” (MUNEN) that attains the best possible approximation ratio. The aggregation rules and MUNEN can be executed by mobile nodes with limited processing power. The overall framework provides a good balance between complexity and performance for attaining robust intrusion detection in ad hoc networks.

Cyber-physical system (CPS) security has become more important in the age of Industry 4.0 because of the quick integration of automation and the Internet of Things. The goal of this project is to create a strong intrusion detection and control system that can recognize and lessen security risks in CPS settings. The suggested approach makes use of deep learning (DL) and reinforcement learning (RL) techniques. To guarantee data consistency, pre-processing procedures such as mean-based imputation and min–max scaling come after data collection. ADASYN data augmentation is used to address class imbalance, while entropy analysis and statistical techniques are used to extract key features. The intrusion detection phase uses a combination of deep convolutional neural networks (DCNN) and bidirectional long short-term memory (BI-LSTM) networks to capture both spatial and temporal relationships in the data, while a hybrid feature selection technique improves the model’s performance. A deep Q-network (DQN) handles attack mitigation and uses reinforcement learning to adjust to new threats. Detecting attack patterns with high sensitivity (0.984), specificity (0.983), and accuracy (0.991626) for dataset 1, the accuracy of dataset 2 is 0.985 for 70% of training and 0.988 for 80% of training, and the Proposed-DBID-Net architecture enhances CPS security in Industry 4.0. The evaluation phase emphasizes how crucial feature selection is to maximize the model’s accuracy. In conclusion, this study offers a thorough and flexible method for protecting CPS in Industry 4.0, guaranteeing accuracy and scalability across changing cyber threats.

SDN provides centralised control and programmability, but because of its open and centralised architecture, it is extremely susceptible to cyberattacks like Distributed Denial of Service (DDoS), infiltration, and botnets. In terms of accuracy and flexibility, traditional intrusion detection systems frequently fall short of the changing requirements of SDN settings. In order to solve this, we suggest a hybrid deep learning model that incorporates Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN), augmented with an Attention mechanism. In order to increase accuracy and interpretability, CNN layers take out spatial information from traffic data, LSTM layers record temporal dependencies, and the Attention mechanism highlights important elements. The CICIDS 2017 dataset is used to train and assess the model, utilising pre - processing methods such as class balancing, label encoding, and normalisation. According to experimental results, our model outperforms conventional models such standalone CNNs and statistical techniques, achieving an accuracy of 93.43%. It performs admirably in a variety of attack scenarios, such as DDoS, probe, and penetration. This study establishes the foundation for real-time, scalable deployment and demonstrates the potential of hybrid deep learning models in SDN cybersecurity. Future research will concentrate on improving the detection of zero-day attacks and tailoring the model for edge computing settings with TensorFlow Lite. Key Words: SDN Security, Intrusion Detection, CNN-LSTM Hybrid, Attention Mechanism, Cyberattack Detection.