Biblio

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2021-05-13
Mahmoud, Loreen, Praveen, Raja.  2020.  Artificial Neural Networks for detecting Intrusions: A survey. 2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN). :41–48.
Nowadays, the networks attacks became very sophisticated and hard to be recognized, The traditional types of intrusion detection systems became inefficient in predicting new types of attacks. As the IDS is an important factor in securing the network in the real time, many new effective IDS approaches have been proposed. In this paper, we intend to discuss different Artificial Neural Networks based IDS approaches, also we are going to categorize them in four categories (normal ANN, DNN, CNN, RNN) and make a comparison between them depending on different performance parameters (accuracy, FNR, FPR, training time, epochs and the learning rate) and other factors like the network structure, the classification type, the used dataset. At the end of the survey, we will mention the merits and demerits of each approach and suggest some enhancements to avoid the noticed drawbacks.
2021-02-23
Shah, A., Clachar, S., Minimair, M., Cook, D..  2020.  Building Multiclass Classification Baselines for Anomaly-based Network Intrusion Detection Systems. 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA). :759—760.
This paper showcases multiclass classification baselines using different machine learning algorithms and neural networks for distinguishing legitimate network traffic from direct and obfuscated network intrusions. This research derives its baselines from Advanced Security Network Metrics & Tunneling Obfuscations dataset. The dataset captured legitimate and obfuscated malicious TCP communications on selected vulnerable network services. The multiclass classification NIDS is able to distinguish obfuscated and direct network intrusion with up to 95% accuracy.
2021-03-01
Perisetty, A., Bodempudi, S. T., Shaik, P. Rahaman, Kumar, B. L. N. Phaneendra.  2020.  Classification of Hyperspectral Images using Edge Preserving Filter and Nonlinear Support Vector Machine (SVM). 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS). :1050–1054.
Hyperspectral image is acquired with a special sensor in which the information is collected continuously. This sensor will provide abundant data from the scene captured. The high voluminous data in this image give rise to the extraction of materials and other valuable items in it. This paper proposes a methodology to extract rich information from the hyperspectral images. As the information collected in a contiguous manner, there is a need to extract spectral bands that are uncorrelated. A factor analysis based dimensionality reduction technique is employed to extract the spectral bands and a weight least square filter is used to get the spatial information from the data. Due to the preservation of edge property in the spatial filter, much information is extracted during the feature extraction phase. Finally, a nonlinear SVM is applied to assign a class label to the pixels in the image. The research work is tested on the standard dataset Indian Pines. The performance of the proposed method on this dataset is assessed through various accuracy measures. These accuracies are 96%, 92.6%, and 95.4%. over the other methods. This methodology can be applied to forestry applications to extract the various metrics in the real world.
2021-01-15
Korolev, D., Frolov, A., Babalova, I..  2020.  Classification of Websites Based on the Content and Features of Sites in Onion Space. 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). :1680—1683.
This paper describes a method for classifying onion sites. According to the results of the research, the most spread model of site in onion space is built. To create such a model, a specially trained neural network is used. The classification of neural network is based on five different categories such as using authentication system, corporate email, readable URL, feedback and type of onion-site. The statistics of the most spread types of websites in Dark Net are given.
2021-02-16
Saxena, U., Sodhi, J., Singh, Y..  2020.  A Comprehensive Approach for DDoS Attack Detection in Smart Home Network Using Shortest Path Algorithm. 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). :392—395.
A Distributed Denial of Service (DDoS) attack is an attack that compromised the bandwidth of the whole network by choking down all the available network resources which are publically available, thus makes access to that resource unavailable. The DDoS attack is more vulnerable than a normal DoS attack because here the sources of attack origin are more than one, so users cannot even estimate how to detect and where to take actions so that attacks can be dissolved. This paper proposed a unique approach for DDoS detection using the shortest path algorithm. This Paper suggests that the remedy that must be taken in order to counter-affect the DDoS attack in a smart home network.
2021-09-07
Zhang, Xing, Cui, Xiaotong, Cheng, Kefei, Zhang, Liang.  2020.  A Convolutional Encoder Network for Intrusion Detection in Controller Area Networks. 2020 16th International Conference on Computational Intelligence and Security (CIS). :366–369.
Integrated with various electronic control units (ECUs), vehicles are becoming more intelligent with the assistance of essential connections. However, the interaction with the outside world raises great concerns on cyber-attacks. As a main standard for in-vehicle network, Controller Area Network (CAN) does not have any built-in security mechanisms to guarantee a secure communication. This increases risks of denial of service, remote control attacks by an attacker, posing serious threats to underlying vehicles, property and human lives. As a result, it is urgent to develop an effective in-vehicle network intrusion detection system (IDS) for better security. In this paper, we propose a Feature-based Sliding Window (FSW) to extract the feature of CAN Data Field and CAN IDs. Then we construct a convolutional encoder network (CEN) to detect network intrusion of CAN networks. The proposed FSW-CEN method is evaluated on real-world datasets. The experimental results show that compared to traditional data processing methods and convolutional neural networks, our method is able to detect attacks with a higher accuracy in terms of detection accuracy and false negative rate.
2021-02-10
Gomes, G., Dias, L., Correia, M..  2020.  CryingJackpot: Network Flows and Performance Counters against Cryptojacking. 2020 IEEE 19th International Symposium on Network Computing and Applications (NCA). :1—10.
Cryptojacking, the appropriation of users' computational resources without their knowledge or consent to obtain cryp-tocurrencies, is a widespread attack, relatively easy to implement and hard to detect. Either browser-based or binary, cryptojacking lacks robust and reliable detection solutions. This paper presents a hybrid approach to detect cryptojacking where no previous knowledge about the attacks or training data is needed. Our Cryp-tojacking Intrusion Detection Approach, Cryingjackpot, extracts and combines flow and performance counter-based features, aggregating hosts with similar behavior by using unsupervised machine learning algorithms. We evaluate Cryingjackpot experimentally with both an artificial and a hybrid dataset, achieving F1-scores up to 97%.
2021-08-05
Bogatu, Alex, Fernandes, Alvaro A. A., Paton, Norman W., Konstantinou, Nikolaos.  2020.  Dataset Discovery in Data Lakes. 2020 IEEE 36th International Conference on Data Engineering (ICDE). :709—720.
Data analytics stands to benefit from the increasing availability of datasets that are held without their conceptual relationships being explicitly known. When collected, these datasets form a data lake from which, by processes like data wrangling, specific target datasets can be constructed that enable value- adding analytics. Given the potential vastness of such data lakes, the issue arises of how to pull out of the lake those datasets that might contribute to wrangling out a given target. We refer to this as the problem of dataset discovery in data lakes and this paper contributes an effective and efficient solution to it. Our approach uses features of the values in a dataset to construct hash- based indexes that map those features into a uniform distance space. This makes it possible to define similarity distances between features and to take those distances as measurements of relatedness w.r.t. a target table. Given the latter (and exemplar tuples), our approach returns the most related tables in the lake. We provide a detailed description of the approach and report on empirical results for two forms of relatedness (unionability and joinability) comparing them with prior work, where pertinent, and showing significant improvements in all of precision, recall, target coverage, indexing and discovery times.
2021-04-08
Zhang, J., Liao, Y., Zhu, X., Wang, H., Ding, J..  2020.  A Deep Learning Approach in the Discrete Cosine Transform Domain to Median Filtering Forensics. IEEE Signal Processing Letters. 27:276—280.
This letter presents a novel median filtering forensics approach, based on a convolutional neural network (CNN) with an adaptive filtering layer (AFL), which is built in the discrete cosine transform (DCT) domain. Using the proposed AFL, the CNN can determine the main frequency range closely related with the operational traces. Then, to automatically learn the multi-scale manipulation features, a multi-scale convolutional block is developed, exploring a new multi-scale feature fusion strategy based on the maxout function. The resultant features are further processed by a convolutional stream with pooling and batch normalization operations, and finally fed into the classification layer with the Softmax function. Experimental results show that our proposed approach is able to accurately detect the median filtering manipulation and outperforms the state-of-the-art schemes, especially in the scenarios of low image resolution and serious compression loss.
2021-09-07
Choi, Ho-Jin, Lee, Young-Jun.  2020.  Deep Learning Based Response Generation using Emotion Feature Extraction. 2020 IEEE International Conference on Big Data and Smart Computing (BigComp). :255–262.
Neural response generation is to generate human-like response given human utterance by using a deep learning. In the previous studies, expressing emotion in response generation improve user performance, user engagement, and user satisfaction. Also, the conversational agents can communicate with users at the human level. However, the previous emotional response generation model cannot understand the subtle part of emotions, because this model use the desired emotion of response as a token form. Moreover, this model is difficult to generate natural responses related to input utterance at the content level, since the information of input utterance can be biased to the emotion token. To overcome these limitations, we propose an emotional response generation model which generates emotional and natural responses by using the emotion feature extraction. Our model consists of two parts: Extraction part and Generation part. The extraction part is to extract the emotion of input utterance as a vector form by using the pre-trained LSTM based classification model. The generation part is to generate an emotional and natural response to the input utterance by reflecting the emotion vector from the extraction part and the thought vector from the encoder. We evaluate our model on the emotion-labeled dialogue dataset: DailyDialog. We evaluate our model on quantitative analysis and qualitative analysis: emotion classification; response generation modeling; comparative study. In general, experiments show that the proposed model can generate emotional and natural responses.
2021-03-15
Toma, A., Krayani, A., Marcenaro, L., Gao, Y., Regazzoni, C. S..  2020.  Deep Learning for Spectrum Anomaly Detection in Cognitive mmWave Radios. 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications. :1–7.
Millimeter Wave (mmWave) band can be a solution to serve the vast number of Internet of Things (IoT) and Vehicle to Everything (V2X) devices. In this context, Cognitive Radio (CR) is capable of managing the mmWave spectrum sharing efficiently. However, Cognitive mmWave Radios are vulnerable to malicious users due to the complex dynamic radio environment and the shared access medium. This indicates the necessity to implement techniques able to detect precisely any anomalous behaviour in the spectrum to build secure and efficient radios. In this work, we propose a comparison framework between deep generative models: Conditional Generative Adversarial Network (C-GAN), Auxiliary Classifier Generative Adversarial Network (AC-GAN), and Variational Auto Encoder (VAE) used to detect anomalies inside the dynamic radio spectrum. For the sake of the evaluation, a real mmWave dataset is used, and results show that all of the models achieve high probability in detecting spectrum anomalies. Especially, AC-GAN that outperforms C-GAN and VAE in terms of accuracy and probability of detection.
2021-03-29
Moti, Z., Hashemi, S., Jahromi, A. N..  2020.  A Deep Learning-based Malware Hunting Technique to Handle Imbalanced Data. 2020 17th International ISC Conference on Information Security and Cryptology (ISCISC). :48–53.
Nowadays, with the increasing use of computers and the Internet, more people are exposed to cyber-security dangers. According to antivirus companies, malware is one of the most common threats of using the Internet. Therefore, providing a practical solution is critical. Current methods use machine learning approaches to classify malware samples automatically. Despite the success of these approaches, the accuracy and efficiency of these techniques are still inadequate, especially for multiple class classification problems and imbalanced training data sets. To mitigate this problem, we use deep learning-based algorithms for classification and generation of new malware samples. Our model is based on the opcode sequences, which are given to the model without any pre-processing. Besides, we use a novel generative adversarial network to generate new opcode sequences for oversampling minority classes. Also, we propose the model that is a combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) to classify malware samples. CNN is used to consider short-term dependency between features; while, LSTM is used to consider longer-term dependence. The experiment results show our method could classify malware to their corresponding family effectively. Our model achieves 98.99% validation accuracy.
2021-05-05
Hallaji, Ehsan, Razavi-Far, Roozbeh, Saif, Mehrdad.  2020.  Detection of Malicious SCADA Communications via Multi-Subspace Feature Selection. 2020 International Joint Conference on Neural Networks (IJCNN). :1—8.
Security maintenance of Supervisory Control and Data Acquisition (SCADA) systems has been a point of interest during recent years. Numerous research works have been dedicated to the design of intrusion detection systems for securing SCADA communications. Nevertheless, these data-driven techniques are usually dependant on the quality of the monitored data. In this work, we propose a novel feature selection approach, called MSFS, to tackle undesirable quality of data caused by feature redundancy. In contrast to most feature selection techniques, the proposed method models each class in a different subspace, where it is optimally discriminated. This has been accomplished by resorting to ensemble learning, which enables the usage of multiple feature sets in the same feature space. The proposed method is then utilized to perform intrusion detection in smaller subspaces, which brings about efficiency and accuracy. Moreover, a comparative study is performed on a number of advanced feature selection algorithms. Furthermore, a dataset obtained from the SCADA system of a gas pipeline is employed to enable a realistic simulation. The results indicate the proposed approach extensively improves the detection performance in terms of classification accuracy and standard deviation.
2021-08-11
Cordeiro, Renato, Gajaria, Dhruv, Limaye, Ankur, Adegbija, Tosiron, Karimian, Nima, Tehranipoor, Fatemeh.  2020.  ECG-Based Authentication Using Timing-Aware Domain-Specific Architecture. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 39:3373–3384.
Electrocardiogram (ECG) biometric authentication (EBA) is a promising approach for human identification, particularly in consumer devices, due to the individualized, ubiquitous, and easily identifiable nature of ECG signals. Thus, computing architectures for EBA must be accurate, fast, energy efficient, and secure. In this article, first, we implement an EBA algorithm to achieve 100% accuracy in user authentication. Thereafter, we extensively analyze the algorithm to show the distinct variance in execution requirements and reveal the latency bottleneck across the algorithm's different steps. Based on our analysis, we propose a domain-specific architecture (DSA) to satisfy the execution requirements of the algorithm's different steps and minimize the latency bottleneck. We explore different variations of the DSA, including one that features the added benefit of ensuring constant timing across the different EBA steps, in order to mitigate the vulnerability to timing-based side-channel attacks. Our DSA improves the latency compared to a base ARM-based processor by up to 4.24×, while the constant timing DSA improves the latency by up to 19%. Also, our DSA improves the energy by up to 5.59×, as compared to the base processor.
2021-01-15
Younus, M. A., Hasan, T. M..  2020.  Effective and Fast DeepFake Detection Method Based on Haar Wavelet Transform. 2020 International Conference on Computer Science and Software Engineering (CSASE). :186—190.
DeepFake using Generative Adversarial Networks (GANs) tampered videos reveals a new challenge in today's life. With the inception of GANs, generating high-quality fake videos becomes much easier and in a very realistic manner. Therefore, the development of efficient tools that can automatically detect these fake videos is of paramount importance. The proposed DeepFake detection method takes the advantage of the fact that current DeepFake generation algorithms cannot generate face images with varied resolutions, it is only able to generate new faces with a limited size and resolution, a further distortion and blur is needed to match and fit the fake face with the background and surrounding context in the source video. This transformation causes exclusive blur inconsistency between the generated face and its background in the outcome DeepFake videos, in turn, these artifacts can be effectively spotted by examining the edge pixels in the wavelet domain of the faces in each frame compared to the rest of the frame. A blur inconsistency detection scheme relied on the type of edge and the analysis of its sharpness using Haar wavelet transform as shown in this paper, by using this feature, it can determine if the face region in a video has been blurred or not and to what extent it has been blurred. Thus will lead to the detection of DeepFake videos. The effectiveness of the proposed scheme is demonstrated in the experimental results where the “UADFV” dataset has been used for the evaluation, a very successful detection rate with more than 90.5% was gained.
2021-05-18
Zheng, Wei, Gao, Jialiang, Wu, Xiaoxue, Xun, Yuxing, Liu, Guoliang, Chen, Xiang.  2020.  An Empirical Study of High-Impact Factors for Machine Learning-Based Vulnerability Detection. 2020 IEEE 2nd International Workshop on Intelligent Bug Fixing (IBF). :26–34.
Ahstract-Vulnerability detection is an important topic of software engineering. To improve the effectiveness and efficiency of vulnerability detection, many traditional machine learning-based and deep learning-based vulnerability detection methods have been proposed. However, the impact of different factors on vulnerability detection is unknown. For example, classification models and vectorization methods can directly affect the detection results and code replacement can affect the features of vulnerability detection. We conduct a comparative study to evaluate the impact of different classification algorithms, vectorization methods and user-defined variables and functions name replacement. In this paper, we collected three different vulnerability code datasets. These datasets correspond to different types of vulnerabilities and have different proportions of source code. Besides, we extract and analyze the features of vulnerability code datasets to explain some experimental results. Our findings from the experimental results can be summarized as follows: (i) the performance of using deep learning is better than using traditional machine learning and BLSTM can achieve the best performance. (ii) CountVectorizer can improve the performance of traditional machine learning. (iii) Different vulnerability types and different code sources will generate different features. We use the Random Forest algorithm to generate the features of vulnerability code datasets. These generated features include system-related functions, syntax keywords, and user-defined names. (iv) Datasets without user-defined variables and functions name replacement will achieve better vulnerability detection results.
2021-01-15
Nguyen, H. M., Derakhshani, R..  2020.  Eyebrow Recognition for Identifying Deepfake Videos. 2020 International Conference of the Biometrics Special Interest Group (BIOSIG). :1—5.
Deepfake imagery that contains altered faces has become a threat to online content. Current anti-deepfake approaches usually do so by detecting image anomalies, such as visible artifacts or inconsistencies. However, with deepfake advances, these visual artifacts are becoming harder to detect. In this paper, we show that one can use biometric eyebrow matching as a tool to detect manipulated faces. Our method could provide an 0.88 AUC and 20.7% EER for deepfake detection when applied to the highest quality deepfake dataset, Celeb-DF.
2021-02-01
Bai, Y., Guo, Y., Wei, J., Lu, L., Wang, R., Wang, Y..  2020.  Fake Generated Painting Detection Via Frequency Analysis. 2020 IEEE International Conference on Image Processing (ICIP). :1256–1260.
With the development of deep neural networks, digital fake paintings can be generated by various style transfer algorithms. To detect the fake generated paintings, we analyze the fake generated and real paintings in Fourier frequency domain and observe statistical differences and artifacts. Based on our observations, we propose Fake Generated Painting Detection via Frequency Analysis (FGPD-FA) by extracting three types of features in frequency domain. Besides, we also propose a digital fake painting detection database for assessing the proposed method. Experimental results demonstrate the excellence of the proposed method in different testing conditions.
2021-04-08
Mayer, O., Stamm, M. C..  2020.  Forensic Similarity for Digital Images. IEEE Transactions on Information Forensics and Security. 15:1331—1346.
In this paper, we introduce a new digital image forensics approach called forensic similarity, which determines whether two image patches contain the same forensic trace or different forensic traces. One benefit of this approach is that prior knowledge, e.g., training samples, of a forensic trace is not required to make a forensic similarity decision on it in the future. To do this, we propose a two-part deep-learning system composed of a convolutional neural network-based feature extractor and a three-layer neural network, called the similarity network. This system maps the pairs of image patches to a score indicating whether they contain the same or different forensic traces. We evaluated the system accuracy of determining whether two image patches were captured by the same or different camera model and manipulated by the same or a different editing operation and the same or a different manipulation parameter, given a particular editing operation. Experiments demonstrate applicability to a variety of forensic traces and importantly show efficacy on “unknown” forensic traces that were not used to train the system. Experiments also show that the proposed system significantly improves upon prior art, reducing error rates by more than half. Furthermore, we demonstrated the utility of the forensic similarity approach in two practical applications: forgery detection and localization, and database consistency verification.
2021-01-18
Bentahar, A., Meraoumia, A., Bendjenna, H., Chitroub, S., Zeroual, A..  2020.  Fuzzy Extractor-Based Key Agreement for Internet of Things. 020 1st International Conference on Communications, Control Systems and Signal Processing (CCSSP). :25–29.
The emergence of the Internet of Things with its constraints obliges researchers in this field to find light and accurate solutions to secure the data exchange. This document presents secure authentication using biometrics coupled with an effective key agreement scheme to save time and energy. In our scheme, the agreed key is used to encrypt transmission data between different IoT actors. While the fuzzy extractor based on the fuzzy vault principle, is used as authentication and as key agreement scheme. Besides, our system incorporates the Reed Solomon and Hamming codes to give some tolerance to errors. The experimental results have been discussed according to several recognition rates and computation times. Indeed, the recognition rate results have been compared to other works to validate our system. Also, we clarify how our system resists to specific transmission attacks without affecting lightness and accuracy.
2021-07-08
Lu, Yujun, Gao, BoYu, Long, Jinyi, Weng, Jian.  2020.  Hand Motion with Eyes-free Interaction for Authentication in Virtual Reality. 2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). :714—715.
Designing an authentication method is a crucial component to secure privacy in information systems. Virtual Reality (VR) is a new interaction platform, in which the users can interact with natural behaviours (e.g. hand, gaze, head, etc.). In this work, we propose a novel authentication method in which user can perform hand motion in an eyes-free manner. We evaluate the usability and security between eyes-engage and eyes-free input with a pilot study. The initial result revealed our purposed method can achieve a trade-off between usability and security, showing a new way to behaviour-based authentication in VR.
2021-03-29
Li, J., Wang, X., Liu, S..  2020.  Hash Retrieval Method for Recaptured Images Based on Convolutional Neural Network. 2020 2nd World Symposium on Artificial Intelligence (WSAI). :79–83.
For the purpose of outdoor advertising market researching, AD images are recaptured and uploaded everyday for statistics. But the quality of the recaptured advertising images are often affected by conditions such as angle, distance, and light during the shooting process, which consequently reduce either the speed or the accuracy of the retrieving algorithm. In this paper, we proposed a hash retrieval method based on convolutional neural networks for recaptured images. The basic idea is to add a hash layer to the convolutional neural network and then extract the binary hash code output by the hash layer to perform image retrieval in lowdimensional Hamming space. Experimental results show that the retrieval performance is improved compared with the current commonly used hash retrieval methods.
2021-09-07
Sunny, Jerin, Sankaran, Sriram, Saraswat, Vishal.  2020.  A Hybrid Approach for Fast Anomaly Detection in Controller Area Networks. 2020 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS). :1–6.
Recent advancements in the field of in-vehicle network and wireless communication, has been steadily progressing. Also, the advent of technologies such as Vehicular Adhoc Networks (VANET) and Intelligent Transportation System (ITS), has transformed modern automobiles into a sophisticated cyber-physical system rather than just a isolated mechanical device. Modern automobiles rely on many electronic control units communicating over the Controller Area Network (CAN) bus. Although protecting the car's external interfaces is an vital part of preventing attacks, detecting malicious activity on the CAN bus is an effective second line of defense against attacks. This paper proposes a hybrid anomaly detection system for CAN bus based on patterns of recurring messages and time interval of messages. The proposed method does not require modifications in CAN bus. The proposed system is evaluated on real CAN bus traffic with simulated attack scenarios. Results obtained show that our proposed system achieved a good detection rate with fast response times.
2021-02-23
Chen, W., Cao, H., Lv, X., Cao, Y..  2020.  A Hybrid Feature Extraction Network for Intrusion Detection Based on Global Attention Mechanism. 2020 International Conference on Computer Information and Big Data Applications (CIBDA). :481—485.
The widespread application of 5G will make intrusion detection of large-scale network traffic a mere need. However, traditional intrusion detection cannot meet the requirements by manually extracting features, and the existing AI methods are also relatively inefficient. Therefore, when performing intrusion detection tasks, they have significant disadvantages of high false alarm rates and low recognition performance. For this challenge, this paper proposes a novel hybrid network, RULA-IDS, which can perform intrusion detection tasks by great amount statistical data from the network monitoring system. RULA-IDS consists of the fully connected layer, the feature extraction layer, the global attention mechanism layer and the SVM classification layer. In the feature extraction layer, the residual U-Net and LSTM are used to extract the spatial and temporal features of the network traffic attributes. It is worth noting that we modified the structure of U-Net to suit the intrusion detection task. The global attention mechanism layer is then used to selectively retain important information from a large number of features and focus on those. Finally, the SVM is used as a classifier to output results. The experimental results show that our method outperforms existing state-of-the-art intrusion detection methods, and the accuracies of training and testing are improved to 97.01% and 98.19%, respectively, and presents stronger robustness during training and testing.
2021-03-29
Al-Janabi, S. I. Ali, Al-Janabi, S. T. Faraj, Al-Khateeb, B..  2020.  Image Classification using Convolution Neural Network Based Hash Encoding and Particle Swarm Optimization. 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI). :1–5.
Image Retrieval (IR) has become one of the main problems facing computer society recently. To increase computing similarities between images, hashing approaches have become the focus of many programmers. Indeed, in the past few years, Deep Learning (DL) has been considered as a backbone for image analysis using Convolutional Neural Networks (CNNs). This paper aims to design and implement a high-performance image classifier that can be used in several applications such as intelligent vehicles, face recognition, marketing, and many others. This work considers experimentation to find the sequential model's best configuration for classifying images. The best performance has been obtained from two layers' architecture; the first layer consists of 128 nodes, and the second layer is composed of 32 nodes, where the accuracy reached up to 0.9012. The proposed classifier has been achieved using CNN and the data extracted from the CIFAR-10 dataset by the inception model, which are called the Transfer Values (TRVs). Indeed, the Particle Swarm Optimization (PSO) algorithm is used to reduce the TRVs. In this respect, the work focus is to reduce the TRVs to obtain high-performance image classifier models. Indeed, the PSO algorithm has been enhanced by using the crossover technique from genetic algorithms. This led to a reduction of the complexity of models in terms of the number of parameters used and the execution time.