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Liu, Weiyou, Liu, Xu, Di, Xiaoqiang, Qi, Hui.  2019.  A novel network intrusion detection algorithm based on Fast Fourier Transformation. 2019 1st International Conference on Industrial Artificial Intelligence (IAI). :1–6.
Deep learning techniques have been widely used in intrusion detection, but their application on convolutional neural networks (CNN) is still immature. The main challenge is how to represent the network traffic to improve performance of the CNN model. In this paper, we propose a network intrusion detection algorithm based on representation learning using Fast Fourier Transformation (FFT), which is first exploration that converts traffic to image by FFT to the best of our knowledge. Each traffic is converted to an image and then the intrusion detection problem is turned to image classification. The experiment results on NSL-KDD dataset show that the classification performence of the algorithm in the CNN model has obvious advantages compared with other algorithms.
Kanimozhi, V., Jacob, T. Prem.  2019.  Artificial Intelligence based Network Intrusion Detection with Hyper-Parameter Optimization Tuning on the Realistic Cyber Dataset CSE-CIC-IDS2018 using Cloud Computing. 2019 International Conference on Communication and Signal Processing (ICCSP). :0033–0036.
One of the latest emerging technologies is artificial intelligence, which makes the machine mimic human behavior. The most important component used to detect cyber attacks or malicious activities is the Intrusion Detection System (IDS). Artificial intelligence plays a vital role in detecting intrusions and widely considered as the better way in adapting and building IDS. In trendy days, artificial intelligence algorithms are rising as a brand new computing technique which will be applied to actual time issues. In modern days, neural network algorithms are emerging as a new artificial intelligence technique that can be applied to real-time problems. The proposed system is to detect a classification of botnet attack which poses a serious threat to financial sectors and banking services. The proposed system is created by applying artificial intelligence on a realistic cyber defense dataset (CSE-CIC-IDS2018), the very latest Intrusion Detection Dataset created in 2018 by Canadian Institute for Cybersecurity (CIC) on AWS (Amazon Web Services). The proposed system of Artificial Neural Networks provides an outstanding performance of Accuracy score is 99.97% and an average area under ROC (Receiver Operator Characteristic) curve is 0.999 and an average False Positive rate is a mere value of 0.001. The proposed system using artificial intelligence of botnet attack detection is powerful, more accurate and precise. The novel proposed system can be implemented in n machines to conventional network traffic analysis, cyber-physical system traffic data and also to the real-time network traffic analysis.
Peng, Wang, Kong, Xiangwei, Peng, Guojin, Li, Xiaoya, Wang, Zhongjie.  2019.  Network Intrusion Detection Based on Deep Learning. 2019 International Conference on Communications, Information System and Computer Engineering (CISCE). :431–435.
With the continuous development of computer network technology, security problems in the network are emerging one after another, and it is becoming more and more difficult to ignore. For the current network administrators, how to successfully prevent malicious network hackers from invading, so that network systems and computers are at Safe and normal operation is an urgent task. This paper proposes a network intrusion detection method based on deep learning. This method uses deep confidence neural network to extract features of network monitoring data, and uses BP neural network as top level classifier to classify intrusion types. The method was validated using the KDD CUP'99 dataset from the Lincoln Laboratory of the Massachusetts Institute of Technology. The results show that the proposed method has a significant improvement over the traditional machine learning accuracy.
Khan, Riaz Ullah, Zhang, Xiaosong, Alazab, Mamoun, Kumar, Rajesh.  2019.  An Improved Convolutional Neural Network Model for Intrusion Detection in Networks. 2019 Cybersecurity and Cyberforensics Conference (CCC). :74–77.
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.
OUIAZZANE, Said, ADDOU, Malika, BARRAMOU, Fatimazahra.  2019.  A Multi-Agent Model for Network Intrusion Detection. 2019 1st International Conference on Smart Systems and Data Science (ICSSD). :1–5.
The objective of this paper is to propose a distributed intrusion detection model based on a multi agent system. Mutli Agent Systems (MAS) are very suitable for intrusion detection systems as they meet the characteristics required by the networks and Big Data issues. The MAS agents cooperate and communicate with each other to ensure the effective detection of network intrusions without the intervention of an expert as used to be in the classical intrusion detection systems relying on signature matching to detect known attacks. The proposed model helped to detect known and unknown attacks within big computer infrastructure by responding to the network requirements in terms of distribution, autonomy, responsiveness and communication. The proposed model is capable of achieving a good and a real time intrusion detection using multi-agents paradigm and Hadoop Distributed File System (HDFS).
singh, Kunal, Mathai, K. James.  2019.  Performance Comparison of Intrusion Detection System Between Deep Belief Network (DBN)Algorithm and State Preserving Extreme Learning Machine (SPELM) Algorithm. 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). :1–7.
This paper work is focused on Performance comparison of intrusion detection system between DBN Algorithm and SPELM Algorithm. Researchers have used this new algorithm SPELM to perform experiments in the area of face recognition, pedestrian detection, and for network intrusion detection in the area of cyber security. The scholar used the proposed State Preserving Extreme Learning Machine(SPELM) algorithm as machine learning classifier and compared it's performance with Deep Belief Network (DBN) algorithm using NSL KDD dataset. The NSL- KDD dataset has four lakhs of data record; out of which 40% of data were used for training purposes and 60% data used in testing purpose while calculating the performance of both the algorithms. The experiment as performed by the scholar compared the Accuracy, Precision, recall and Computational Time of existing DBN algorithm with proposed SPELM Algorithm. The findings have show better performance of SPELM; when compared its accuracy of 93.20% as against 52.8% of DBN algorithm;69.492 Precision of SPELM as against 66.836 DBN and 90.8 seconds of Computational time taken by SPELM as against 102 seconds DBN Algorithm.
Nagamani, Ch., Chittineni, Suneetha.  2018.  Network Intrusion Detection Mechanisms Using Outlier Detection. 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT). :1468–1473.
The recognition of intrusions has increased impressive enthusiasm for information mining with the acknowledgment that anomalies can be the key disclosure to be produced using extensive network databases. Intrusions emerge because of different reasons, for example, mechanical deficiencies, changes in framework conduct, fake conduct, human blunder and instrument mistake. Surely, for some applications the revelation of Intrusions prompts more intriguing and helpful outcomes than the disclosure of inliers. Discovery of anomalies can prompt recognizable proof of framework blames with the goal that executives can take preventive measures previously they heighten. A network database framework comprises of a sorted out posting of pages alongside programming to control the network information. This database framework has been intended to empower network operations, oversee accumulations of information, show scientific outcomes and to get to these information utilizing networks. It likewise empowers network clients to gather limitless measure of information on unbounded territories of utilization, break down it and return it into helpful data. Network databases are ordinarily used to help information control utilizing dynamic capacities on sites or for putting away area subordinate data. This database holds a surrogate for each network route. The formation of these surrogates is called ordering and each network database does this errand in an unexpected way. In this paper, a structure for compelling access control and Intrusion Detection using outliers has been proposed and used to give viable Security to network databases. The design of this framework comprises of two noteworthy subsystems to be specific, Access Control Subsystem and Intrusion Detection Subsystem. In this paper preprocessing module is considered which clarifies the preparing of preprocessing the accessible information. And rain forest method is discussed which is used for intrusion detection.
Nikolov, Dimitar, Kordev, Iliyan, Stefanova, Stela.  2018.  Concept for network intrusion detection system based on recurrent neural network classifier. 2018 IEEE XXVII International Scientific Conference Electronics - ET. :1–4.
This paper presents the effects of problem based learning project on a high-school student in Technology school “Electronic systems” associated with Technical University Sofia. The problem is creating an intrusion detection system for Apache HTTP Server with duration 6 months. The intrusion detection system is based on a recurrent neural network classifier namely long-short term memory units.
Chandre, Pankaj Ramchandra, Mahalle, Parikshit Narendra, Shinde, Gitanjali Rahul.  2018.  Machine Learning Based Novel Approach for Intrusion Detection and Prevention System: A Tool Based Verification. 2018 IEEE Global Conference on Wireless Computing and Networking (GCWCN). :135–140.
Now a day, Wireless Sensor Networks are widely used in military applications by its applications, it is extended to healthcare, industrial environments and many more. As we know that, there are some unique features of WSNs such as limited power supply, minimum bandwidth and limited energy. So, to secure traditional network, multiple techniques are available, but we can't use same techniques to secure WSNs. So to increase the overall security of WSNs, we required new ideas as well as new approaches. In general, intrusion prevention is the primary issue in WSNs and intrusion detection already reached to saturation. Thus, we need an efficient solution for proactive intrusion prevention towards WSNs. Thus, formal validation of protocols in WSN is an essential area of research. This research paper aims to formally verify as well as model some protocol used for intrusion detection using AVISPA tool and HLPSL language. In this research paper, the results of authentication and DoS attacks were detected is presented, but there is a need to prevent such type of attacks. In this research paper, a system is proposed in order to avoid intrusion using machine learning for the wireless sensor network. So, the proposed system will be used for intrusion prevention in a wireless sensor network.
Mirza, Ali H., Cosan, Selin.  2018.  Computer network intrusion detection using sequential LSTM Neural Networks autoencoders. 2018 26th Signal Processing and Communications Applications Conference (SIU). :1–4.
In this paper, we introduce a sequential autoencoder framework using long short term memory (LSTM) neural network for computer network intrusion detection. We exploit the dimensionality reduction and feature extraction property of the autoencoder framework to efficiently carry out the reconstruction process. Furthermore, we use the LSTM networks to handle the sequential nature of the computer network data. We assign a threshold value based on cross-validation in order to classify whether the incoming network data sequence is anomalous or not. Moreover, the proposed framework can work on both fixed and variable length data sequence and works efficiently for unforeseen and unpredictable network attacks. We then also use the unsupervised version of the LSTM, GRU, Bi-LSTM and Neural Networks. Through a comprehensive set of experiments, we demonstrate that our proposed sequential intrusion detection framework performs well and is dynamic, robust and scalable.
Althubiti, Sara A., Jones, Eric Marcell, Roy, Kaushik.  2018.  LSTM for Anomaly-Based Network Intrusion Detection. 2018 28th International Telecommunication Networks and Applications Conference (ITNAC). :1–3.
Due to the massive amount of the network traffic, attackers have a great chance to cause a huge damage to the network system or its users. Intrusion detection plays an important role in ensuring security for the system by detecting the attacks and the malicious activities. In this paper, we utilize CIDDS dataset and apply a deep learning approach, Long-Short-Term Memory (LSTM), to implement intrusion detection system. This research achieves a reasonable accuracy of 0.85.
Abhilash, Goyal, Divyansh, Gupta.  2018.  Intrusion Detection and Prevention in Software Defined Networking. 2018 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS). :1–4.
Software defined networking is a concept proposed to replace traditional networks by separating control plane and data plane. It makes the network more programmable and manageable. As there is a single point of control of the network, it is more vulnerable to intrusion. The idea is to train the network controller by machine learning algorithms to let it make the intelligent decisions automatically. In this paper, we have discussed our approach to make software defined networking more secure from various malicious attacks by making it capable of detecting and preventing such attacks.
Anand Sukumar, J V, Pranav, I, Neetish, MM, Narayanan, Jayasree.  2018.  Network Intrusion Detection Using Improved Genetic k-means Algorithm. 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). :2441–2446.
Internet is a widely used platform nowadays by people across the globe. This has led to the advancement in science and technology. Many surveys show that network intrusion has registered a consistent increase and lead to personal privacy theft and has become a major platform for attack in the recent years. Network intrusion is any unauthorized activity on a computer network. Hence there is a need to develop an effective intrusion detection system. In this paper we acquaint an intrusion detection system that uses improved genetic k-means algorithm(IGKM) to detect the type of intrusion. This paper also shows a comparison between an intrusion detection system that uses the k-means++ algorithm and an intrusion detection system that uses IGKM algorithm while using smaller subset of kdd-99 dataset with thousand instances and the KDD-99 dataset. The experiment shows that the intrusion detection that uses IGKM algorithm is more accurate when compared to k-means++ algorithm.
Yu, Dunyi.  2018.  Research on Anomaly Intrusion Detection Technology in Wireless Network. 2018 International Conference on Virtual Reality and Intelligent Systems (ICVRIS). :540–543.
In order to improve the security of wireless network, an anomaly intrusion detection algorithm based on adaptive time-frequency feature decomposition is proposed. This paper analyzes the types and detection principles of wireless network intrusion detection, it adopts the information statistical analysis method to detect the network intrusion, constructs the traffic statistical analysis model of the network abnormal intrusion, and establishes the network intrusion signal model by combining the signal fitting method. The correlation matching filter is used to filter the network intrusion signal to improve the output signal-to-noise ratio (SNR), the time-frequency analysis method is used to extract the characteristic quantity of the network abnormal intrusion, and the adaptive correlation spectrum analysis method is used to realize the intrusion detection. The simulation results show that this method has high accuracy and strong anti-interference ability, and it can effectively guarantee the network security.
Vashist, Abhishek, Keats, Andrew, Pudukotai Dinakarrao, Sai Manoj, Ganguly, Amlan.  2019.  Securing a Wireless Network-on-Chip Against Jamming Based Denial-of-Service Attacks. 2019 IEEE Computer Society Annual Symposium on VLSI (ISVLSI). :320–325.
Wireless Networks-on-Chips (NoCs) have emerged as a panacea to the non-scalable multi-hop data transmission paths in traditional wired NoC architectures. Using low-power transceivers in NoC switches, novel Wireless NoC (WiNoC) architectures have been shown to achieve higher energy efficiency with improved peak bandwidth and reduced on-chip data transfer latency. However, using wireless interconnects for data transfer within a chip makes the on-chip communications vulnerable to various security threats from either external attackers or internal hardware Trojans (HTs). In this work, we propose a mechanism to make the wireless communication in a WiNoC secure against persistent jamming based Denial-of-Service attacks from both external and internal attackers. Persistent jamming attacks on the on-chip wireless medium will cause interference in data transfer over the duration of the attack resulting in errors in contiguous bits, known as burst errors. Therefore, we use a burst error correction code to monitor the rate of burst errors received over the wireless medium and deploy a Machine Learning (ML) classifier to detect the persistent jamming attack and distinguish it from random burst errors. In the event of jamming attack, alternate routing strategies are proposed to avoid the DoS attack over the wireless medium, so that a secure data transfer can be sustained even in the presence of jamming. We evaluate the proposed technique on a secure WiNoC in the presence of DoS attacks. It has been observed that with the proposed defense mechanisms, WiNoC can outperform a wired NoC even in presence of attacks in terms of performance and security. On an average, 99.87% attack detection was achieved with the chosen ML Classifiers. A bandwidth degradation of \textbackslashtextless;3% is experienced in the event of internal attack, while the wireless interconnects are disabled in the presence of an external attacker.
Poovendran, R, Billclinton., S, Darshan., R, Dinakar., R, Fazil., M.  2019.  Design and analysis of a mesh-based Adaptive Wireless Network-on Chips Architecture With Irregular Network Routing. 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN). :1–6.
The metallic interface for between core messages expends wealth influence and lesser throughput which are huge in Network-on Chip (NoC) structures. We proposed a remote Network-on-Chip (NoC) building Wireless Network-on Chip that uses power and imperatives gainful remote handsets to improve higherenergy and throughput by altering channels as indicated by traffic plans. Our proposed computations uses interface use bits of knowledge to redispensreal platforms, and a vitality funds of 29-35%. Wireless channels and a token sharing arrangement to totally use the remote information transmission successfully. Remote/electrical topological with results demonstrates a through-put advancement of 69%, a speedup between 1.7-2.9X on real platform, and an power savings of 25-38%.
Kenarangi, Farid, Partin-Vaisband, Inna.  2019.  Security Network On-Chip for Mitigating Side-Channel Attacks. 2019 ACM/IEEE International Workshop on System Level Interconnect Prediction (SLIP). :1–6.
Hardware security is a critical concern in design and fabrication of integrated circuits (ICs). Contemporary hardware threats comprise tens of advance invasive and non-invasive attacks for compromising security of modern ICs. Numerous attack-specific countermeasures against the individual threats have been proposed, trading power, area, speed, and design complexity of a system for security. These typical overheads combined with strict performance requirements in advanced technology nodes and high complexity of modern ICs often make the codesign of multiple countermeasures impractical. In this paper, on-chip distribution networks are exploited for detecting those hardware security threats that require non-invasive, yet physical interaction with an operating device-under-attack (e.g., measuring equipment for collecting sensitive information in side-channel attacks). With the proposed approach, the effect of the malicious physical interference with the device-under-attack is captured in the form of on-chip voltage variations and utilized for detecting malicious activity in the compromised device. A machine learning (ML) security IC is trained to predict system security based on sensed variations of signals within on-chip distribution networks. The trained ML ICs are distributed on-chip, yielding a robust and high-confidence security network on-chip. To halt an active attack, a variety of desired counteractions can be executed in a cost-effective manner upon the attack detection. The applicability and effectiveness of these security networks is demonstrated in this paper with respect to power, timing, and electromagnetic analysis attacks.
Su, Yu, Wu, Jing, Long, Chengnian, Li, Shaoyuan.  2018.  Event-triggered Control for Networked Control Systems Under Replay Attacks. 2018 Chinese Automation Congress (CAC). :2636—2641.
With wide application of networked control systems(N CSs), NCSs security have encountered severe challenges. In this paper, we propose a robust event-triggered controller design method under replay attacks, and the control signal on the plant is updated only when the event-triggering condition is satisfied. We develop a general random replay attack model rather than predetermined specific patterns for the occurrences of replay attacks, which allows to obtain random states to replay. We show that the proposed event-triggered control (ETC) scheme, if well designed, can tolerate some consecutive replay attacks, without affecting the corresponding closed-loop system stability and performance. A numerical examples is finally given to illustrate the effectiveness of our method.
Ali, Yasir, Shen, Zhen, Zhu, Fenghua, Xiong, Gang, Chen, Shichao, Xia, Yuanqing, Wang, Fei-Yue.  2018.  Solutions Verification for Cloud-Based Networked Control System using Karush-Kuhn-Tucker Conditions. 2018 Chinese Automation Congress (CAC). :1385—1389.
The rapid development of the Cloud Computing Technologies (CCTs) has amended the conventional design of resource-constrained Network Control System (NCS) to the powerful and flexible design of Cloud-Based Networked Control System (CB-NCS) by relocating the processing part to the cloud server. This arrangement has produced many internets based exquisite applications. However, this new arrangement has also raised many network security challenges for the cloud-based control system related to cyber-physical part of the system. In the absence of robust verification methodology, an attacker can launch the modification attack in order to destabilize or take control of NCS. It is desirable that there shall be a solution authentication methodology used to verify whether the incoming solutions are coming from the cloud or not. This paper proposes a methodology used for the verification of the receiving solution to the local control system from the cloud using Karush-Kuhn-Tucker (KKT) conditions, which is then applied to actuator after verification and thus ensure the stability in case of modification attack.
Zhi-wen, Wang, Yang, Cheng.  2018.  Bandwidth Allocation Strategy of Networked Control System under Denial-of-Service Attack. 2018 4th Annual International Conference on Network and Information Systems for Computers (ICNISC). :49—55.
In this paper, security of networked control system (NCS) under denial of service (DoS) attack is considered. Different from the existing literatures from the perspective of control systems, this paper considers a novel method of dynamic allocation of network bandwidth for NCS under DoS attack. Firstly, time-constrained DoS attack and its impact on the communication channel of NCS are introduced. Secondly, details for the proposed dynamic bandwidth allocation structure are presented along with an implementation, which is a bandwidth allocation strategy based on error between current state and equilibrium state and available bandwidth. Finally, a numerical example is given to demonstrate the effectiveness of the proposed bandwidth allocation approach.
Ming, Liang, Zhao, Gang, Huang, Minhuan, Kuang, Xiaohui, Li, Hu, Zhang, Ming.  2018.  Security Analysis of Intelligent Transportation Systems Based on Simulation Data. 2018 1st International Conference on Data Intelligence and Security (ICDIS). :184—187.
Modern vehicles in Intelligent Transportation Systems (ITS) can communicate with each other as well as roadside infrastructure units (RSUs) in order to increase transportation efficiency and road safety. For example, there are techniques to alert drivers in advance about traffic incidents and to help them avoid congestion. Threats to these systems, on the other hand, can limit the benefits of these technologies. Securing ITS itself is an important concern in ITS design and implementation. In this paper, we provide a security model of ITS which extends the classic layered network security model with transportation security and information security, and gives a reference for designing ITS architectures. Based on this security model, we also present a classification of ITS threats for defense. Finally a proof-of-concept example with malicious nodes in an ITS system is also given to demonstrate the impact of attacks. We analyzed the threat of malicious nodes and their effects to commuters, like increasing toll fees, travel distances, and travel times etc. Experimental results from simulations based on Veins shows the threats will bring about 43.40% more total toll fees, 39.45% longer travel distances, and 63.10% more travel times.
Yang, Zai-xin, Gao, Chen, Wang, Yun-min.  2018.  Security and Stability Control System Simulation Using RTDS. 2018 13th World Congress on Intelligent Control and Automation (WCICA). :1737—1740.
Analyzing performance of security and stability control system is of great importance for the safe and stable operation of the power grid. Digital dynamic experimental model is built by real time digital simulation (RTDS) in order to research security and stability system of Inner Mongolia in northern 500kV transmission channel. The whole process is closed-loop dynamic real-time simulation. According to power grid network testing technology standard, all kinds of stability control devices need to be tested in a comprehensive system. Focus on the following items: security and stability control strategy, tripping criterion as well as power system low frequency oscillations. Results of the trial indicated that the simulation test platform based on RTDS have the ability of detecting the safe and stable device. It can reflect the action behavior and control characteristics of the safe and stable device accurately. The device can be used in the case of low frequency oscillation of the system.
Vigneswaran, Rahul K., Vinayakumar, R., Soman, K.P., Poornachandran, Prabaharan.  2018.  Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security. 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1—6.
Intrusion detection system (IDS) has become an essential layer in all the latest ICT system due to an urge towards cyber safety in the day-to-day world. Reasons including uncertainty in finding the types of attacks and increased the complexity of advanced cyber attacks, IDS calls for the need of integration of Deep Neural Networks (DNNs). In this paper, DNNs have been utilized to predict the attacks on Network Intrusion Detection System (N-IDS). A DNN with 0.1 rate of learning is applied and is run for 1000 number of epochs and KDDCup-`99' dataset has been used for training and benchmarking the network. For comparison purposes, the training is done on the same dataset with several other classical machine learning algorithms and DNN of layers ranging from 1 to 5. The results were compared and concluded that a DNN of 3 layers has superior performance over all the other classical machine learning algorithms.
Huang, Yifan, Chung, Wingyan, Tang, Xinlin.  2018.  A Temporal Recurrent Neural Network Approach to Detecting Market Anomaly Attacks. 2018 IEEE International Conference on Intelligence and Security Informatics (ISI). :160—162.
In recent years, the spreading of malicious social media messages about financial stocks has threatened the security of financial market. Market Anomaly Attacks is an illegal practice in the stock or commodities markets that induces investors to make purchase or sale decisions based on false information. Identifying these threats from noisy social media datasets remains challenging because of the long time sequence in these social media postings, ambiguous textual context and the difficulties for traditional deep learning approaches to handle both temporal and text dependent data such as financial social media messages. This research developed a temporal recurrent neural network (TRNN) approach to capturing both time and text sequence dependencies for intelligent detection of market anomalies. We tested the approach by using financial social media of U.S. technology companies and their stock returns. Compared with traditional neural network approaches, TRNN was found to more efficiently and effectively classify abnormal returns.
Shen, Weiguo, Wang, Wei.  2018.  Node Identification in Wireless Network Based on Convolutional Neural Network. 2018 14th International Conference on Computational Intelligence and Security (CIS). :238—241.
Aiming at the problem of node identification in wireless networks, a method of node identification based on deep learning is proposed, which starts with the tiny features of nodes in radiofrequency layer. Firstly, in order to cut down the computational complexity, Principal Component Analysis is used to reduce the dimension of node sample data. Secondly, a convolution neural network containing two hidden layers is designed to extract local features of the preprocessed data. Stochastic gradient descent method is used to optimize the parameters, and the Softmax Model is used to determine the output label. Finally, the effectiveness of the method is verified by experiments on practical wireless ad-hoc network.