
@article{Meng2014,
   author = {Weizhi Meng and Wenjuan Li and Lam-For Kwok},
   doi = {10.1016/j.cose.2014.02.006},
   issn = {01674048},
   journal = {Computers \& Security},
   month = {6},
   pages = {189-204},
   title = {EFM: Enhancing the performance of signature-based network intrusion detection systems using enhanced filter mechanism},
   volume = {43},
   year = {2014},
}
@inproceedings{Erlacher2018,
   author = {Felix Erlacher and Falko Dressler},
   doi = {10.1109/NOMS.2018.8406247},
   isbn = {978-1-5386-3416-5},
   booktitle = {NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium},
   month = {4},
   pages = {1-8},
   publisher = {IEEE},
   title = {FIXIDS: A high-speed signature-based flow intrusion detection system},
   year = {2018},
}
@article{Obaidat2020,
   abstract = {<p>The Internet of Things (IoT) has experienced constant growth in the number of devices deployed and the range of applications in which such devices are used. They vary widely in size, computational power, capacity storage, and energy. The explosive growth and integration of IoT in different domains and areas of our daily lives has created an Internet of Vulnerabilities (IoV). In the rush to build and implement IoT devices, security and privacy have not been adequately addressed. IoT devices, many of which are highly constrained, are vulnerable to cyber attacks, which threaten the security and privacy of users and systems. This survey provides a comprehensive overview of IoT in regard to areas of application, security architecture frameworks, recent security and privacy issues in IoT, as well as a review of recent similar studies on IoT security and privacy. In addition, the paper presents a comprehensive taxonomy of attacks on IoT based on the three-layer architecture model; perception, network, and application layers, as well as a suggestion of the impact of these attacks on CIA objectives in representative devices, are presented. Moreover, the study proposes mitigations and countermeasures, taking a multi-faceted approach rather than a per layer approach. Open research areas are also covered to provide researchers with the most recent research urgent questions in regard to securing IoT ecosystem.</p>},
   author = {Muath A. Obaidat and Suhaib Obeidat and Jennifer Holst and Abdullah Al Hayajneh and Joseph Brown},
   doi = {10.3390/computers9020044},
   issn = {2073-431X},
   issue = {2},
   journal = {Computers},
   month = {5},
   pages = {44},
   title = {A Comprehensive and Systematic Survey on the Internet of Things: Security and Privacy Challenges, Security Frameworks, Enabling Technologies, Threats, Vulnerabilities and Countermeasures},
   volume = {9},
   year = {2020},
}
@article{Mandapuram2017,
   abstract = {<p>The Internet of Things is assisting in developing a new and more intelligent world in which everything will fall under its purview. The issue of security is by far the most critical concern and component regarding the Internet of Things. With billions or trillions of connected devices, it will be difficult for future generations to find solutions to the security concerns we face today. The Internet of Things (IoT) paves the way for various entities and applications that benefit humanity. Although it is the most significant accomplishment of the decade, it has also given rise to catastrophic scenarios due to security concerns such as threats, vulnerabilities, and attacks on connected and interconnected devices and objects. Despite this, it is still the most significant achievement of the decade. Businesses and organizations are backing the current paradigm shift by providing financial assistance to researchers and academics. The industry expected to have the most growth over this decade is the Internet of Things, which will connect trillions of different devices. It is anticipated that IoT will alter the method by which we communicate. The Internet of Things poses many significant dangers, including physical attacks, network attacks, encryption attacks, software attacks, authorization, surveillance, identity theft, vandalism, and secure communication. The findings of this research show that none of the IoT security architectures include a security layer.</p>},
   author = {Mounika Mandapuram},
   doi = {10.18034/ra.v5i3.650},
   issn = {2413-5224},
   issue = {3},
   journal = {ABC Research Alert},
   month = {12},
   pages = {49-55},
   title = {Security Risk Analysis of the Internet of Things},
   volume = {5},
   year = {2017},
}
@article{Gama2020,
   abstract = {Network data can be conveniently modeled as a graph signal, where data values are assigned to nodes of a graph that describes the underlying network topology. Successful learning from network data is built upon methods that effectively exploit this graph structure. In this work, we leverage graph signal processing to characterize the representation space of graph neural networks (GNNs). We discuss the role of graph convolutional filters in GNNs and show that any architecture built with such filters has the fundamental properties of permutation equivariance and stability to changes in the topology. These two properties offer insight about the workings of GNNs and help explain their scalability and transferability properties which, coupled with their local and distributed nature, make GNNs powerful tools for learning in physical networks. We also introduce GNN extensions using edge-varying and autoregressive moving average graph filters and discuss their properties. Finally, we study the use of GNNs in recommender systems and learning decentralized controllers for robot swarms.},
   author = {Fernando Gama and Elvin Isufi and Geert Leus and Alejandro Ribeiro},
   doi = {10.1109/MSP.2020.3016143},
   month = {3},
   title = {Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networks},
   year = {2020},
}
@article{Zhou2018,
   abstract = {Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein interface, and classifying diseases demand a model to learn from graph inputs. In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures (like the dependency trees of sentences and the scene graphs of images) is an important research topic which also needs graph reasoning models. Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs. In recent years, variants of GNNs such as graph convolutional network (GCN), graph attention network (GAT), graph recurrent network (GRN) have demonstrated ground-breaking performances on many deep learning tasks. In this survey, we propose a general design pipeline for GNN models and discuss the variants of each component, systematically categorize the applications, and propose four open problems for future research.},
   author = {Jie Zhou and Ganqu Cui and Shengding Hu and Zhengyan Zhang and Cheng Yang and Zhiyuan Liu and Lifeng Wang and Changcheng Li and Maosong Sun},
   month = {12},
   title = {Graph Neural Networks: A Review of Methods and Applications},
   year = {2018},
}
@article{Gama2020,
   author = {Fernando Gama and Elvin Isufi and Geert Leus and Alejandro Ribeiro},
   doi = {10.1109/MSP.2020.3016143},
   issn = {1053-5888},
   issue = {6},
   journal = {IEEE Signal Processing Magazine},
   month = {11},
   pages = {128-138},
   title = {Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networks},
   volume = {37},
   year = {2020},
}

@article{Munikoti2024,
   author = {Sai Munikoti and Deepesh Agarwal and Laya Das and Mahantesh Halappanavar and Balasubramaniam Natarajan},
   doi = {10.1109/TNNLS.2023.3283523},
   issn = {2162-237X},
   issue = {11},
   journal = {IEEE Transactions on Neural Networks and Learning Systems},
   month = {11},
   pages = {15051-15071},
   title = {Challenges and Opportunities in Deep Reinforcement Learning With Graph Neural Networks: A Comprehensive Review of Algorithms and Applications},
   volume = {35},
   year = {2024},
}
@article{Fan2022,
   author = {Wenqi Fan and Yao Ma and Qing Li and Jianping Wang and Guoyong Cai and Jiliang Tang and Dawei Yin},
   doi = {10.1109/TKDE.2020.3008732},
   issn = {1041-4347},
   issue = {5},
   journal = {IEEE Transactions on Knowledge and Data Engineering},
   month = {5},
   pages = {2033-2047},
   title = {A Graph Neural Network Framework for Social Recommendations},
   volume = {34},
   year = {2022},
}
@inproceedings{Chen2021,
   author = {Jianwen Chen and Shuangjia Zheng and Ying Song and Jiahua Rao and Yuedong Yang},
   city = {California},
   doi = {10.24963/ijcai.2021/309},
   isbn = {978-0-9992411-9-6},
   booktitle = {Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence},
   month = {8},
   pages = {2242-2248},
   publisher = {International Joint Conferences on Artificial Intelligence Organization},
   title = {Learning Attributed Graph Representation with Communicative Message Passing Transformer},
   year = {2021},
}
@inproceedings{Fan2019,
   author = {Wenqi Fan and Yao Ma and Qing Li and Yuan He and Eric Zhao and Jiliang Tang and Dawei Yin},
   city = {New York, NY, USA},
   doi = {10.1145/3308558.3313488},
   isbn = {9781450366748},
   booktitle = {The World Wide Web Conference},
   month = {5},
   pages = {417-426},
   publisher = {ACM},
   title = {Graph Neural Networks for Social Recommendation},
   year = {2019},
}
@article{Gama2020,
   abstract = {Network data can be conveniently modeled as a graph signal, where data values are assigned to nodes of a graph that describes the underlying network topology. Successful learning from network data is built upon methods that effectively exploit this graph structure. In this work, we leverage graph signal processing to characterize the representation space of graph neural networks (GNNs). We discuss the role of graph convolutional filters in GNNs and show that any architecture built with such filters has the fundamental properties of permutation equivariance and stability to changes in the topology. These two properties offer insight about the workings of GNNs and help explain their scalability and transferability properties which, coupled with their local and distributed nature, make GNNs powerful tools for learning in physical networks. We also introduce GNN extensions using edge-varying and autoregressive moving average graph filters and discuss their properties. Finally, we study the use of GNNs in recommender systems and learning decentralized controllers for robot swarms.},
   author = {Fernando Gama and Elvin Isufi and Geert Leus and Alejandro Ribeiro},
   doi = {10.1109/MSP.2020.3016143},
   month = {3},
   title = {Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networks},
   year = {2020},
}
@article{Lo2021,
   abstract = {This paper presents a new Network Intrusion Detection System (NIDS) based on Graph Neural Networks (GNNs). GNNs are a relatively new sub-field of deep neural networks, which can leverage the inherent structure of graph-based data. Training and evaluation data for NIDSs are typically represented as flow records, which can naturally be represented in a graph format. In this paper, we propose E-GraphSAGE, a GNN approach that allows capturing both the edge features of a graph as well as the topological information for network intrusion detection in IoT networks. To the best of our knowledge, our proposal is the first successful, practical, and extensively evaluated approach of applying GNNs on the problem of network intrusion detection for IoT using flow-based data. Our extensive experimental evaluation on four recent NIDS benchmark datasets shows that our approach outperforms the state-of-the-art in terms of key classification metrics, which demonstrates the potential of GNNs in network intrusion detection, and provides motivation for further research.},
   author = {Wai Weng Lo and Siamak Layeghy and Mohanad Sarhan and Marcus Gallagher and Marius Portmann},
   doi = {10.1109/NOMS54207.2022.9789878},
   month = {3},
   title = {E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT},
   url = {http://arxiv.org/abs/2103.16329 http://dx.doi.org/10.1109/NOMS54207.2022.9789878},
   year = {2021},
}
@article{Hu2021,
   abstract = {Background: Autism spectrum disorders (ASD) imply a spectrum of symptoms rather than a single phenotype. ASD could affect brain connectivity at different degree based on the severity of the symptom. Given their excellent learning capability, graph neural networks (GNN) methods have recently been used to uncover functional connectivity patterns and biological mechanisms in neuropsychiatric disorders, such as ASD. However, there remain challenges to develop an accurate GNN learning model and understand how specific decisions of these graph models are made in brain network analysis. Results: In this paper, we propose a graph attention network based learning and interpreting method, namely GAT-LI, which learns to classify functional brain networks of ASD individuals versus healthy controls (HC), and interprets the learned graph model with feature importance. Specifically, GAT-LI includes a graph learning stage and an interpreting stage. First, in the graph learning stage, a new graph attention network model, namely GAT2, uses graph attention layers to learn the node representation, and a novel attention pooling layer to obtain the graph representation for functional brain network classification. We experimentally compared GAT2 model’s performance on the ABIDE I database from 1035 subjects against the classification performances of other well-known models, and the results showed that the GAT2 model achieved the best classification performance. We experimentally compared the influence of different construction methods of brain networks in GAT2 model. We also used a larger synthetic graph dataset with 4000 samples to validate the utility and power of GAT2 model. Second, in the interpreting stage, we used GNNExplainer to interpret learned GAT2 model with feature importance. We experimentally compared GNNExplainer with two well-known interpretation methods including Saliency Map and DeepLIFT to interpret the learned model, and the results showed GNNExplainer achieved the best interpretation performance. We further used the interpretation method to identify the features that contributed most in classifying ASD versus HC. Conclusion: We propose a two-stage learning and interpreting method GAT-LI to classify functional brain networks and interpret the feature importance in the graph model. The method should also be useful in the classification and interpretation tasks for graph data from other biomedical scenarios.},
   author = {Jinlong Hu and Lijie Cao and Tenghui Li and Shoubin Dong and Ping Li},
   doi = {10.1186/s12859-021-04295-1},
   issn = {14712105},
   issue = {1},
   journal = {BMC Bioinformatics},
   keywords = {Classification,Functional brain networks,Graph attention networks,Model interpretation,Resting-state functional connectivity data},
   month = {12},
   pmid = {34294047},
   publisher = {BioMed Central Ltd},
   title = {GAT-LI: a graph attention network based learning and interpreting method for functional brain network classification},
   volume = {22},
   year = {2021},
}


@article{,
   abstract = {The Internet of Things (IoT) is becoming an integral part of our daily life including health, environment, homes, military, etc. The enormous growth of IoT in recent years has attracted hackers to take advantage of their computation and communication capabilities to perform different types of attacks. The major concern is that IoT devices have several vulnerabilities that can be easily exploited to form IoT botnets consisting of millions of IoT devices and posing significant threats to Internet security. In this context, DDoS attacks originating from IoT botnets is a major problem in today’s Internet that requires immediate attention. In this paper, we propose a Security Information and Event Management-based IoT botnet DDoS attack detection and mitigation system. This system detects and blocks DDoS attack traffic from compromised IoT devices by monitoring specific packet types including TCP SYN, ICMP and DNS packets originating from these devices. We discuss a prototype implementation of the proposed system and we demonstrate that SIEM based solutions can be configured to accurately identify and block malicious traffic originating from compromised IoT devices.},
   author = {Basheer Al-Duwairi and Wafaa Al-Kahla and Mhd Ammar AlRefai and Yazid Abedalqader and Abdullah Rawash and Rana Fahmawi},
   doi = {10.11591/ijece.v10i2.pp2182-2191},
   issn = {2088-8708},
   issue = {2},
   journal = {International Journal of Electrical and Computer Engineering (IJECE)},
   month = {4},
   pages = {2182},
   title = {SIEM-based detection and mitigation of IoT-botnet DDoS attacks},
   volume = {10},
   year = {2020},
}

@article{Wu2021,
   author = {Zonghan Wu and Shirui Pan and Fengwen Chen and Guodong Long and Chengqi Zhang and Philip S. Yu},
   doi = {10.1109/TNNLS.2020.2978386},
   issn = {2162-237X},
   issue = {1},
   journal = {IEEE Transactions on Neural Networks and Learning Systems},
   month = {1},
   pages = {4-24},
   title = {A Comprehensive Survey on Graph Neural Networks},
   volume = {32},
   year = {2021},
}

@book{,
   author = {B. Claise},
   edition = {RFC},
   pages = {1-33},
   title = {Cisco systems netflow services export version 9},
   volume = {3954},
   year = {2004},
}

@article{,
   author = {Saraladeve .L and Chandrasekar .A and Nithya .TNithya .T},
   doi = {10.54216/jcim.150112},
   issn = {27697851},
   issue = {1},
   journal = {Journal of Cybersecurity and Information Management},
   publisher = {ASPG Publishing LLC},
   title = {A Multiclass Attack Classification Framework for IoT Using Hybrid Deep Learning Model},
   volume = {15},
   year = {2025},
}

@ARTICLE{9469914,
  author={Ullah, Imtiaz and Mahmoud, Qusay H.},
  journal={IEEE Access}, 
  title={Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks}, 
  year={2021},
  volume={9},
  number={},
  pages={103906-103926},
  keywords={Internet of Things;Deep learning;Security;Intrusion detection;Convolutional neural networks;Computational modeling;Neural networks;Internet of Things;anomaly detection;IoT intrusion detection;machine learning;deep learning;transfer learning;network security;convolutional neural network},
  doi={10.1109/ACCESS.2021.3094024}}

@article{Sarhan2021,
   abstract = {Network Intrusion Detection Systems (NIDSs) are important tools for the protection of computer networks against increasingly frequent and sophisticated cyber attacks. Recently, a lot of research effort has been dedicated to the development of Machine Learning (ML) based NIDSs. As in any ML-based application, the availability of high-quality datasets is critical for the training and evaluation of ML-based NIDS. One of the key problems with the currently available datasets is the lack of a standard feature set. The use of a unique and proprietary set of features for each of the publicly available datasets makes it virtually impossible to compare the performance of ML-based traffic classifiers on different datasets, and hence to evaluate the ability of these systems to generalise across different network scenarios. To address that limitation, this paper proposes and evaluates standard NIDS feature sets based on the NetFlow network meta-data collection protocol and system. We evaluate and compare two NetFlow-based feature set variants, a version with 12 features, and another one with 43 features.},
   author = {Mohanad Sarhan and Siamak Layeghy and Marius Portmann},
   doi = {10.1007/s11036-021-01843-0},
   month = {1},
   title = {Towards a Standard Feature Set for Network Intrusion Detection System Datasets},
   year = {2021},
}

@article{Ring2019,
   author = {Markus Ring and Sarah Wunderlich and Deniz Scheuring and Dieter Landes and Andreas Hotho},
   doi = {10.1016/j.cose.2019.06.005},
   issn = {01674048},
   journal = {Computers \& Security},
   month = {9},
   pages = {147-167},
   title = {A survey of network-based intrusion detection data sets},
   volume = {86},
   year = {2019},
}


@article{Koroniotis2019,
   author = {Nickolaos Koroniotis and Nour Moustafa and Elena Sitnikova and Benjamin Turnbull},
   doi = {10.1016/j.future.2019.05.041},
   issn = {0167739X},
   journal = {Future Generation Computer Systems},
   month = {11},
   pages = {779-796},
   title = {Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset},
   volume = {100},
   year = {2019},
}

@article{DBLP:journals/corr/abs-2011-09144,
  author       = {Mohanad Sarhan and
                  Siamak Layeghy and
                  Nour Moustafa and
                  Marius Portmann},
  title        = {NetFlow Datasets for Machine Learning-based Network Intrusion Detection
                  Systems},
  journal      = {CoRR},
  volume       = {abs/2011.09144},
  year         = {2020},
  url          = {https://arxiv.org/abs/2011.09144},
  eprinttype    = {arXiv},
  eprint       = {2011.09144},
  timestamp    = {Wed, 25 Nov 2020 16:34:14 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2011-09144.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{Atuhurra2024,
   abstract = {With the rapidly spreading usage of Internet of Things (IoT) devices, a network intrusion detection system (NIDS) plays an important role in detecting and protecting various types of attacks in the IoT network. To evaluate the robustness of the NIDS in the IoT network, the existing work proposed a realistic botnet dataset in the IoT network (Bot-IoT dataset) and applied it to machine learning-based anomaly detection. This dataset contains imbalanced normal and attack packets because the number of normal packets is much smaller than that of attack ones. The nature of imbalanced data may make it difficult to identify the minority class correctly. In this thesis, to address the class imbalance problem in the Bot-IoT dataset, we propose a binary classification method with synthetic minority over-sampling techniques (SMOTE). The proposed classifier aims to detect attack packets and overcome the class imbalance problem using the SMOTE algorithm. Through numerical results, we demonstrate the proposed classifier's fundamental characteristics and the impact of imbalanced data on its performance.},
   author = {Jesse Atuhurra and Takanori Hara and Yuanyu Zhang and Masahiro Sasabe and Shoji Kasahara},
   month = {3},
   title = {Dealing with Imbalanced Classes in Bot-IoT Dataset},
   year = {2024}
}

@article{Malik2022,
   abstract = {Internet of things (IoT) services are turning out to be more domineering with the rising security considerations fading with time. All this owes to the propagating heterogeneity and budding technologies teamed up with resource-constrained IoT systems, sculpting smart systems to be more susceptible to cyber-attacks. The security challenges such as privacy, scalability, authenticity, trust, and centralization thwart the quick adaptation of the smart services; hence, effective solutions are needed to be in place. Traditional approaches of intrusion detection mechanisms have become irrelevant now, as the bad actors often use obfuscation techniques to evade detections. Moreover, these techniques collapse, while detecting zero-day attacks. Hence, there is a need to use an intelligent mechanism based on machine learning (ML) and deep learning (DL), to detect attacks. In this study, the authors have proposed an intrusion detection engine with a deep belief network (DBN) being the core. The implementation of DBN_Classifier is performed using TensorFlow 2.0 and evaluated using a sample of the TON_IOT_Weather dataset. The findings indicate that the proposed engine outperforms the other state-of-the-art techniques with an average accuracy of 86.3%.},
   author = {Rayeesa Malik and Yashwant Singh and Zakir Ahmad Sheikh and Pooja Anand and Pradeep Kumar Singh and Tewabe Chekole Workneh},
   doi = {10.1155/2022/7892130},
   issn = {2042-3195},
   journal = {Journal of Advanced Transportation},
   month = {4},
   pages = {1-17},
   title = {An Improved Deep Belief Network IDS on IoT-Based Network for Traffic Systems},
   volume = {2022},
   year = {2022}
}


@inproceedings{Shaker2023,
   author = {Bassam Noori Shaker and Bahaa Qasim Al-Musawi and Mohammed Falih Hassan},
   city = {New York, NY, USA},
   doi = {10.1145/3603273.3635058},
   isbn = {9798400708268},
   booktitle = {Proceedings of the 2023 International Conference on Advances in Artificial Intelligence and Applications},
   month = {11},
   pages = {15-21},
   publisher = {ACM},
   title = {A Comparative Study of IDS-Based Deep Learning Models for IoT Network},
   year = {2023}
}


@article{Dahou2022,
   abstract = {This study proposes a novel framework to improve intrusion detection system (IDS) performance based on the data collected from the Internet of things (IoT) environments. The developed framework relies on deep learning and metaheuristic (MH) optimization algorithms to perform feature extraction and selection. A simple yet effective convolutional neural network (CNN) is implemented as the core feature extractor of the framework to learn better and more relevant representations of the input data in a lower-dimensional space. A new feature selection mechanism is proposed based on a recently developed MH method, called Reptile Search Algorithm (RSA), which is inspired by the hunting behaviors of the crocodiles. The RSA boosts the IDS system performance by selecting only the most important features (an optimal subset of features) from the extracted features using the CNN model. Several datasets, including KDDCup-99, NSL-KDD, CICIDS-2017, and BoT-IoT, were used to assess the IDS system performance. The proposed framework achieved competitive performance in classification metrics compared to other well-known optimization methods applied for feature selection problems.},
   author = {Abdelghani Dahou and Mohamed Abd Elaziz and Samia Allaoua Chelloug and Mohammed A. Awadallah and Mohammed Azmi Al-Betar and Mohammed A. A. Al-qaness and Agostino Forestiero},
   doi = {10.1155/2022/6473507},
   issn = {1687-5273},
   journal = {Computational Intelligence and Neuroscience},
   month = {6},
   pages = {1-15},
   title = {Intrusion Detection System for IoT Based on Deep Learning and Modified Reptile Search Algorithm},
   volume = {2022},
   year = {2022},
}

@article{Altaf2023,
   author = {Tanzeela Altaf and Xu Wang and Wei Ni and Guangsheng Yu and Ren Ping Liu and Robin Braun},
   doi = {10.1016/j.iot.2023.100818},
   issn = {25426605},
   journal = {Internet of Things},
   month = {7},
   pages = {100818},
   title = {A new concatenated Multigraph Neural Network for IoT intrusion detection},
   volume = {22},
   year = {2023}
}

@article{Ullah2021,
   author = {Imtiaz Ullah and Qusay H. Mahmoud},
   doi = {10.1109/ACCESS.2021.3132127},
   issn = {2169-3536},
   journal = {IEEE Access},
   pages = {165907-165931},
   title = {A Framework for Anomaly Detection in IoT Networks Using Conditional Generative Adversarial Networks},
   volume = {9},
   year = {2021},
}




@article{Cai2018,
   author = {Hongyun Cai and Vincent W. Zheng and Kevin Chen-Chuan Chang},
   doi = {10.1109/TKDE.2018.2807452},
   issn = {1041-4347},
   issue = {9},
   journal = {IEEE Transactions on Knowledge and Data Engineering},
   month = {9},
   pages = {1616-1637},
   title = {A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications},
   volume = {30},
   year = {2018},
}

@inproceedings{Chang2021,
   author = {Heng Chang and Yu Rong and Tingyang Xu and Wenbing Huang and Somayeh Sojoudi and Junzhou Huang and Wenwu Zhu},
   city = {New York, NY, USA},
   doi = {10.1145/3459637.3482187},
   isbn = {9781450384469},
   booktitle = {Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
   month = {10},
   pages = {2905-2909},
   publisher = {ACM},
   title = {Spectral Graph Attention Network with Fast Eigen-approximation},
   year = {2021},
}
@article{He2023,
   author = {Zerong He and Teng Tian and Qizhe Wu and Xi Jin},
   doi = {10.1109/TCSII.2023.3280180},
   issn = {1549-7747},
   issue = {11},
   journal = {IEEE Transactions on Circuits and Systems II: Express Briefs},
   month = {11},
   pages = {4211-4215},
   title = {FTW-GAT: An FPGA-Based Accelerator for Graph Attention Networks With Ternary Weights},
   volume = {70},
   year = {2023},
}
@inbook{Huang2019,
   author = {Junjie Huang and Huawei Shen and Liang Hou and Xueqi Cheng},
   doi = {10.1007/978-3-030-30493-5_53},
   pages = {566-577},
   title = {Signed Graph Attention Networks},
   year = {2019},
}
@article{Gao2021,
   author = {Jianliang Gao and Jun Gao and Xiaoting Ying and Mingming Lu and Jianxin Wang},
   doi = {10.1109/TKDE.2021.3105544},
   issn = {1041-4347},
   journal = {IEEE Transactions on Knowledge and Data Engineering},
   pages = {1-1},
   title = {Higher-order Interaction Goes Neural: A Substructure Assembling Graph Attention Network for Graph Classification},
   year = {2021},
}
@article{Peng2022,
   author = {Yanfei Peng and Guozhen Tan and Huaiwei Si and Jianping Li},
   doi = {10.1016/j.sysarc.2022.102505},
   issn = {13837621},
   journal = {Journal of Systems Architecture},
   month = {5},
   pages = {102505},
   title = {DRL-GAT-SA: Deep reinforcement learning for autonomous driving planning based on graph attention networks and simplex architecture},
   volume = {126},
   year = {2022},
}
@article{Wang2022,
   author = {Zijian Wang and Rafael Sacks and Timson Yeung},
   doi = {10.1016/j.autcon.2021.104039},
   issn = {09265805},
   journal = {Automation in Construction},
   month = {2},
   pages = {104039},
   title = {Exploring graph neural networks for semantic enrichment: Room type classification},
   volume = {134},
   year = {2022},
}
@article{Hamilton2017,
   abstract = {Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions.},
   author = {William L. Hamilton and Rex Ying and Jure Leskovec},
   month = {6},
   title = {Inductive Representation Learning on Large Graphs},
   year = {2017},
}

@article{Sheng2024,
   abstract = {In recent years, graph convolutional neural networks (GCNs) have become a popular research topic due to their outstanding performance in various complex network data mining tasks. However, current research on graph neural networks lacks understanding of the high-order structural features of networks, focusing mostly on node features and first-order neighbor features. This article proposes two new models, MGAT and MGATv2, by introducing high-order structure motifs that frequently appear in networks and combining them with graph attention mechanisms. By introducing a mixed information matrix based on motifs, the generation process of graph attention coefficients is improved, allowing the model to capture higher-order structural features. Compared with the latest research on various graph neural networks, both MGAT and MGATv2 achieve good results in node classification tasks. Furthermore, through various experimental studies on real datasets, we demonstrate that the introduction of network structural motifs can effectively enhance the expressive power of graph neural networks, indicating that both high-order structural features and attribute features are important components of network feature learning.},
   author = {Jinfang Sheng and Yufeng Zhang and Bin Wang and Yaoxing Chang},
   doi = {10.3390/math12020293},
   issn = {2227-7390},
   issue = {2},
   journal = {Mathematics},
   month = {1},
   pages = {293},
   title = {MGATs: Motif-Based Graph Attention Networks},
   volume = {12},
   year = {2024}
}

  @article{Ceron2019,
   abstract = {IoT botnets have been used to launch Distributed Denial-of-Service (DDoS) attacks affecting the Internet infrastructure. To protect the Internet from such threats and improve security mechanisms, it is critical to understand the botnets’ intents and characterize their behavior. Current malware analysis solutions, when faced with IoT, present limitations in regard to the network access containment and network traffic manipulation. In this paper, we present an approach for handling the network traffic generated by the IoT malware in an analysis environment. The proposed solution can modify the traffic at the network layer based on the actions performed by the malware. In our study case, we investigated the Mirai and Bashlite botnet families, where it was possible to block attacks to other systems, identify attacks targets, and rewrite botnets commands sent by the botnet controller to the infected devices.},
   author = {João Marcelo Ceron and Klaus Steding-Jessen and Cristine Hoepers and Lisandro Zambenedetti Granville and Cíntia Borges Margi},
   doi = {10.3390/s19030727},
   issn = {1424-8220},
   issue = {3},
   journal = {Sensors},
   month = {2},
   pages = {727},
   title = {Improving IoT Botnet Investigation Using an Adaptive Network Layer},
   volume = {19},
   year = {2019}
}
@inproceedings{Kambourakis2017,
   author = {Georgios Kambourakis and Constantinos Kolias and Angelos Stavrou},
   doi = {10.1109/MILCOM.2017.8170867},
   isbn = {978-1-5386-0595-0},
   booktitle = {MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM)},
   month = {10},
   pages = {267-272},
   publisher = {IEEE},
   title = {The Mirai botnet and the IoT Zombie Armies},
   year = {2017}
}
@inproceedings{Cozzi2020,
   author = {Emanuele Cozzi and Pierre-Antoine Vervier and Matteo Dell'Amico and Yun Shen and Leyla Bilge and Davide Balzarotti},
   city = {New York, NY, USA},
   doi = {10.1145/3427228.3427256},
   isbn = {9781450388580},
   booktitle = {Annual Computer Security Applications Conference},
   month = {12},
   pages = {1-16},
   publisher = {ACM},
   title = {The Tangled Genealogy of IoT Malware},
   year = {2020}
}
@article{Torabi2021,
   author = {Sadegh Torabi and Mirabelle Dib and Elias Bou-Harb and Chadi Assi and Mourad Debbabi},
   doi = {10.1109/LNET.2021.3076600},
   issn = {2576-3156},
   issue = {3},
   journal = {IEEE Networking Letters},
   month = {9},
   pages = {161-165},
   title = {A Strings-Based Similarity Analysis Approach for Characterizing IoT Malware and Inferring Their Underlying Relationships},
   volume = {3},
   year = {2021}
}
@inproceedings{Su2018,
   author = {Jiawei Su and Vargas Danilo Vasconcellos and Sanjiva Prasad and Sgandurra Daniele and Yaokai Feng and Kouichi Sakurai},
   doi = {10.1109/COMPSAC.2018.10315},
   isbn = {978-1-5386-2666-5},
   booktitle = {2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC)},
   month = {7},
   pages = {664-669},
   publisher = {IEEE},
   title = {Lightweight Classification of IoT Malware Based on Image Recognition},
   year = {2018}
}
@inproceedings{Pradhyumna2021,
   author = {P Pradhyumna and G P Shreya and Mohana},
   doi = {10.1109/ICESC51422.2021.9532631},
   isbn = {978-1-6654-2867-5},
   booktitle = {2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC)},
   month = {8},
   pages = {1183-1189},
   publisher = {IEEE},
   title = {Graph Neural Network (GNN) in Image and Video Understanding Using Deep Learning for Computer Vision Applications},
   year = {2021},
}
@article{Vatter2024,
   abstract = {Graph neural networks (GNNs) are an emerging research field. This specialized deep neural network architecture is capable of processing graph structured data and bridges the gap between graph processing and deep learning. As graphs are everywhere, GNNs can be applied to various domains including recommendation systems, computer vision, natural language processing, biology, and chemistry. With the rapid growing size of real-world graphs, the need for efficient and scalable GNN training solutions has come. Consequently, many works proposing GNN systems have emerged throughout the past few years. However, there is an acute lack of overview, categorization, and comparison of such systems. We aim to fill this gap by summarizing and categorizing important methods and techniques for large-scale GNN solutions. Additionally, we establish connections between GNN systems, graph processing systems, and deep learning systems.},
   author = {Jana Vatter and Ruben Mayer and Hans-Arno Jacobsen},
   doi = {10.1145/3597428},
   issn = {0360-0300},
   issue = {1},
   journal = {ACM Computing Surveys},
   month = {1},
   pages = {1-37},
   title = {The Evolution of Distributed Systems for Graph Neural Networks and Their Origin in Graph Processing and Deep Learning: A Survey},
   volume = {56},
   year = {2024},
}
@inproceedings{Auten2020,
   author = {Adam Auten and Matthew Tomei and Rakesh Kumar},
   doi = {10.1109/DAC18072.2020.9218751},
   isbn = {978-1-7281-1085-1},
   booktitle = {2020 57th ACM/IEEE Design Automation Conference (DAC)},
   month = {7},
   pages = {1-6},
   publisher = {IEEE},
   title = {Hardware Acceleration of Graph Neural Networks},
   year = {2020},
}
@inproceedings{Arka2021,
   author = {Aqeeb Iqbal Arka and Janardhan Rao Doppa and Partha Pratim Pande and Biresh Kumar Joardar and Krishnendu Chakrabarty},
   doi = {10.23919/DATE51398.2021.9473949},
   isbn = {978-3-9819263-5-4},
   booktitle = {2021 Design, Automation \& Test in Europe Conference \& Exhibition (DATE)},
   month = {2},
   pages = {1667-1672},
   publisher = {IEEE},
   title = {ReGraphX: NoC-enabled 3D Heterogeneous ReRAM Architecture for Training Graph Neural Networks},
   year = {2021},
}
@inproceedings{Zhang2020,
   author = {Zhen Zhang and Jiajun Bu and Martin Ester and Jianfeng Zhang and Chengwei Yao and Zhao Li and Can Wang},
   city = {New York, NY, USA},
   doi = {10.1145/3366423.3380076},
   isbn = {9781450370233},
   booktitle = {Proceedings of The Web Conference 2020},
   month = {4},
   pages = {3049-3055},
   publisher = {ACM},
   title = {Learning Temporal Interaction Graph Embedding via Coupled Memory Networks},
   year = {2020}
}
@inproceedings{Zhang2019,
   author = {Chuxu Zhang and Dongjin Song and Chao Huang and Ananthram Swami and Nitesh V. Chawla},
   city = {New York, NY, USA},
   doi = {10.1145/3292500.3330961},
   isbn = {9781450362016},
   booktitle = {Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
   month = {7},
   pages = {793-803},
   publisher = {ACM},
   title = {Heterogeneous Graph Neural Network},
   year = {2019}
}
