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1.
José Javier de Vicente Mohino Javier Bermejo Higuera Juan Ramón Bermejo Higuera Juan Antonio Sicilia Montalvo Manuel Sánchez Rubio José Javier Martínez Herraiz 《计算机、材料和连续体（英文）》2021,67(2):1447-1462
In a computer environment, an operating system is prone to malware, and even the Linux operating system is not an exception. In recent years, malware has evolved, and attackers have become more qualified compared to a few years ago. Furthermore, Linux-based systems have become more attractive to cybercriminals because of the increasing use of the Linux operating system in web servers and Internet of Things (IoT) devices. Windows is the most employed OS, so most of the research efforts have been focused on its malware protection rather than on other operating systems. As a result, hundreds of research articles, documents, and methodologies dedicated to malware analysis have been reported. However, there has not been much literature concerning Linux security and protection from malware. To address all these new challenges, it is necessary to develop a methodology that can standardize the required steps to perform the malware analysis in depth. A systematic analysis process makes the difference between good and ordinary malware analyses. Additionally, a deep malware comprehension can yield a faster and much more efficient malware eradication. In order to address all mentioned challenges, this article proposed a methodology for malware analysis in the Linux operating system, which is a traditionally overlooked field compared to the other operating systems. The proposed methodology is tested by a specific Linux malware, and the obtained test results have high effectiveness in malware detection. 相似文献
2.
Mohammed Altaf Ahmed Sara A Althubiti Dronamraju Nageswara Rao E. Laxmi Lydia Woong Cho Gyanendra Prasad Joshi Sung Won Kim 《计算机、材料和连续体（英文）》2022,73(3):4695-4711
Cyberattacks are developing gradually sophisticated, requiring effective intrusion detection systems (IDSs) for monitoring computer resources and creating reports on anomalous or suspicious actions. With the popularity of Internet of Things (IoT) technology, the security of IoT networks is developing a vital problem. Because of the huge number and varied kinds of IoT devices, it can be challenging task for protecting the IoT framework utilizing a typical IDS. The typical IDSs have their restrictions once executed to IoT networks because of resource constraints and complexity. Therefore, this paper presents a new Blockchain Assisted Intrusion Detection System using Differential Flower Pollination with Deep Learning (BAIDS-DFPDL) model in IoT Environment. The presented BAIDS-DFPDL model mainly focuses on the identification and classification of intrusions in the IoT environment. To accomplish this, the presented BAIDS-DFPDL model follows blockchain (BC) technology for effective and secure data transmission among the agents. Besides, the presented BAIDS-DFPDL model designs Differential Flower Pollination based feature selection (DFPFS) technique to elect features. Finally, sailfish optimization (SFO) with Restricted Boltzmann Machine (RBM) model is applied for effectual recognition of intrusions. The simulation results on benchmark dataset exhibit the enhanced performance of the BAIDS-DFPDL model over other models on the recognition of intrusions. 相似文献
3.
In recent years, as the popularity of anonymous currencies such as Bitcoin has made the tracking of ransomware attackers more difficult, the amount of ransomware attacks against personal computers and enterprise production servers is increasing rapidly. The ransomware has a wide range of influence and spreads all over the world. It is affecting many industries including internet, education, medical care, traditional industry, etc. This paper uses the idea of virus immunity to design an immunization solution for ransomware viruses to solve the problems of traditional ransomware defense methods (such as anti-virus software, firewalls, etc.), which cannot meet the requirements of rapid detection and immediate prevention of new outbreaks attacks. Our scheme includes two parts: server and client. The server provides an immune configuration file and configuration file management functions, including a configuration file module, a cryptography algorithm module, and a display module. The client obtains the immunization configuration file from server in real time, and performs the corresponding operations according to the configuration file to make the computer have an immune function for a specific ransomware, including an update module, a configuration file module, a cryptography algorithm module, a control module, and a log module. This scheme controls mutexes, services, files and registries respectively, to destroy the triggering conditions of the virus and finally achieve the purpose of immunizing a computer from a specific ransomware. 相似文献
4.
The outbreak of Covid-19 has taken the lives of many patients so far. The symptoms of COVID-19 include muscle pains, loss of taste and smell, coughs, fever, and sore throat, which can lead to severe cases of breathing difficulties, organ failure, and death. Thus, the early detection of the virus is very crucial. COVID-19 can be detected using clinical tests, making us need to know the most important symptoms/features that can enhance the decision process. In this work, we propose a modified multilayer perceptron (MLP) with feature selection (MLPFS) to predict the positive COVID-19 cases based on symptoms and features from patients’ electronic medical records (EMR). MLPFS model includes a layer that identifies the most informative symptoms to minimize the number of symptoms base on their relative importance. Training the model with only the highest informative symptoms can fasten the learning process and increase accuracy. Experiments were conducted using three different COVID-19 datasets and eight different models, including the proposed MLPFS. Results show that MLPFS achieves the best feature reduction across all datasets compared to all other experimented models. Additionally, it outperforms the other models in classification results as well as time. 相似文献
5.
Anwer Mustafa Hilal Siwar Ben Haj Hassine Souad Larabi-Marie-Sainte Nadhem Nemri Mohamed K. Nour Abdelwahed Motwakel Abu Sarwar Zamani Mesfer Al Duhayyim 《计算机、材料和连续体（英文）》2022,72(1):713-726
The development in Information and Communication Technology has led to the evolution of new computing and communication environment. Technological revolution with Internet of Things (IoTs) has developed various applications in almost all domains from health care, education to entertainment with sensors and smart devices. One of the subsets of IoT is Internet of Medical things (IoMT) which connects medical devices, hardware and software applications through internet. IoMT enables secure wireless communication over the Internet to allow efficient analysis of medical data. With these smart advancements and exploitation of smart IoT devices in health care technology there increases threat and malware attacks during transmission of highly confidential medical data. This work proposes a scheme by integrating machine learning approach and block chain technology to detect malware during data transmission in IoMT. The proposed Machine Learning based Block Chain Technology malware detection scheme (MLBCT-Mdetect) is implemented in three steps namely: feature extraction, Classification and blockchain. Feature extraction is performed by calculating the weight of each feature and reduces the features with less weight. Support Vector Machine classifier is employed in the second step to classify the malware and benign nodes. Furthermore, third step uses blockchain to store details of the selected features which eventually improves the detection of malware with significant improvement in speed and accuracy. ML-BCT-Mdetect achieves higher accuracy with low false positive rate and higher True positive rate. 相似文献
6.
Fahad F. Alruwaili 《计算机、材料和连续体（英文）》2023,75(1):99-115
The Internet of Things (IoT) paradigm enables end users to access networking services amongst diverse kinds of electronic devices. IoT security mechanism is a technology that concentrates on safeguarding the devices and networks connected in the IoT environment. In recent years, False Data Injection Attacks (FDIAs) have gained considerable interest in the IoT environment. Cybercriminals compromise the devices connected to the network and inject the data. Such attacks on the IoT environment can result in a considerable loss and interrupt normal activities among the IoT network devices. The FDI attacks have been effectively overcome so far by conventional threat detection techniques. The current research article develops a Hybrid Deep Learning to Combat Sophisticated False Data Injection Attacks detection (HDL-FDIAD) for the IoT environment. The presented HDL-FDIAD model majorly recognizes the presence of FDI attacks in the IoT environment. The HDL-FDIAD model exploits the Equilibrium Optimizer-based Feature Selection (EO-FS) technique to select the optimal subset of the features. Moreover, the Long Short Term Memory with Recurrent Neural Network (LSTM-RNN) model is also utilized for the purpose of classification. At last, the Bayesian Optimization (BO) algorithm is employed as a hyperparameter optimizer in this study. To validate the enhanced performance of the HDL-FDIAD model, a wide range of simulations was conducted, and the results were investigated in detail. A comparative study was conducted between the proposed model and the existing models. The outcomes revealed that the proposed HDL-FDIAD model is superior to other models. 相似文献
7.
J. Vijayaraj;B. Abirami;Sachi Nandan Mohanty;V. P. Kavitha; 《International journal of imaging systems and technology》2024,34(2):e23000
In our human body bones are the most significant part, which helps people to move and perform several activities. But, the cancer is caused by producing abnormal cell, which is rapidly spread to the whole parts of the body. Bone cancer is one of the critical types due to its malignancy more than other cancers. The approach involves preprocessing and segmentation of input images to remove noise and resize images, followed by feature extraction using a Convolutional histogram of oriented gradients (ConvHisOrGrad). The ROI extraction helps to accurately identify abnormal parts around the cancerous area. The Extreme Deep Convolutional learning machine (Ex-ConVLM) is used for normal and cancerous bone classification based on the texture properties of bone MRI images. The proposed technique was evaluated using a dataset of 220 bone MRIs for tumor classes classified as necrotic, non-tumor, and viable-tumor. Results showed that the proposed technique outperformed existing techniques with the highest accuracy of 97% for the necrotic tumor class, 98.2% for the non-tumor class, and 98.6% for the viable tumor class. The fine-tuned model shows promising performance in detecting malignancy in bone based on histological images. In summary, the proposed technique utilizes deep learning architectures and ROI extraction for the accurate identification of abnormal parts in bone MRI images, achieving state-of-the-art performance in the detection and categorization of bone cancers. 相似文献
8.
光场相机作为新一代的成像设备,能够同时捕获光线的空间位置和入射角度,然而其记录的光场存在空间分辨率和角度分辨率之间的制约关系,尤其子孔径图像有限的空间分辨率在一定程度上限制了光场相机的应用场景。因此本文提出了一种融合多尺度特征的光场图像超分辨网络,以获取更高空间分辨率的光场子孔径图像。该基于深度学习的网络框架分为三大模块：多尺度特征提取模块、全局特征融合模块和上采样模块。网络首先通过多尺度特征提取模块学习4D光场中固有的结构特征,然后采用融合模块对多尺度特征进行融合与增强,最后使用上采样模块实现对光场的超分辨率。在合成光场数据集和真实光场数据集上的实验结果表明,该方法在视觉评估和评价指标上均优于现有算法。另外本文将超分辨后的光场图像用于深度估计,实验结果展示出光场图像空间超分辨率能够增强深度估计结果的准确性。 相似文献
9.
Mohammed Maray Hamed Alqahtani Saud S. Alotaibi Fatma S. Alrayes Nuha Alshuqayran Mrim M. Alnfiai Amal S. Mehanna Mesfer Al Duhayyim 《计算机、材料和连续体（英文）》2023,74(2):3101-3115
Cloud Computing (CC) is the most promising and advanced technology to store data and offer online services in an effective manner. When such fast evolving technologies are used in the protection of computer-based systems from cyberattacks, it brings several advantages compared to conventional data protection methods. Some of the computer-based systems that effectively protect the data include Cyber-Physical Systems (CPS), Internet of Things (IoT), mobile devices, desktop and laptop computer, and critical systems. Malicious software (malware) is nothing but a type of software that targets the computer-based systems so as to launch cyber-attacks and threaten the integrity, secrecy, and accessibility of the information. The current study focuses on design of Optimal Bottleneck driven Deep Belief Network-enabled Cybersecurity Malware Classification (OBDDBN-CMC) model. The presented OBDDBN-CMC model intends to recognize and classify the malware that exists in IoT-based cloud platform. To attain this, Z-score data normalization is utilized to scale the data into a uniform format. In addition, BDDBN model is also exploited for recognition and categorization of malware. To effectually fine-tune the hyperparameters related to BDDBN model, Grasshopper Optimization Algorithm (GOA) is applied. This scenario enhances the classification results and also shows the novelty of current study. The experimental analysis was conducted upon OBDDBN-CMC model for validation and the results confirmed the enhanced performance of OBDDBN-CMC model over recent approaches. 相似文献
10.
由于水声目标辐射噪声的低信噪比特性,探测远距离水声目标具有一定挑战。为提升远距离水声目标探测的准确率,文章提出一种基于密集连接神经网络和自注意力机制的方法。该方法提取信号的梅尔倒谱系数作为特征,在密集连接神经网络头部添加自注意力模块以捕获关键信息,经过多个密集块后输出探测结果。在实测数据集上进行实验,分析了自注意力机制添加与否、输入特征不同、接收端深度不同时模型的性能变化。应用在未来几天的数据测试模型的任务中,探测范围在小于10 km时,探测准确率为93.3%,探测范围扩大至20 km时,探测准确率为90.34%。实验结果表明,模型在信噪比不小于-6 dB时实现了水声目标探测,在增加更多的低信噪比样本后,仍具有一定探测能力,且其性能优于其他模型。此外,训练集包含多种信噪比条件下的数据时,探测性能会有进一步提升。 相似文献
11.
Mohammad Hafiz Mohd Yusof Abdullah Mohd Zin Nurhizam Safie Mohd Satar 《计算机、材料和连续体（英文）》2022,72(2):2445-2466
Due to polymorphic nature of malware attack, a signature-based analysis is no longer sufficient to solve polymorphic and stealth nature of malware attacks. On the other hand, state-of-the-art methods like deep learning require labelled dataset as a target to train a supervised model. This is unlikely to be the case in production network as the dataset is unstructured and has no label. Hence an unsupervised learning is recommended. Behavioral study is one of the techniques to elicit traffic pattern. However, studies have shown that existing behavioral intrusion detection model had a few issues which had been parameterized into its common characteristics, namely lack of prior information (p (θ)), and reduced parameters (θ). Therefore, this study aims to utilize the previously built Feature Selection Model subsequently to design a Predictive Analytics Model based on Bayesian Network used to improve the analysis prediction. Feature Selection Model is used to learn significant label as a target and Bayesian Network is a sophisticated probabilistic approach to predict intrusion. Finally, the results are extended to evaluate detection, accuracy and false alarm rate of the model against the subject matter expert model, Support Vector Machine (SVM), k nearest neighbor (k-NN) using simulated and ground-truth dataset. The ground-truth dataset from the production traffic of one of the largest healthcare provider in Malaysia is used to promote realism on the real use case scenario. Results have shown that the proposed model consistently outperformed other models. 相似文献
12.
传统的语音情感识别方式采用的语音特征具有数据量大且无关特征多的特点,因此选择出与情感相关的语音特征具有重要意义。通过提出将注意力机制结合长短时记忆网络(Long Short Term Memory, LSTM),根据注意力权重进行特征选择,在两个数据集上进行了实验。结果发现:(1)基于注意力机制的LSTM相比于单独的LSTM模型,识别率提高了5.4%,可见此算法有效提高了模型的识别效果;(2)注意力机制是一种有效的特征选择方法。采用注意力机制选择出了具有实际物理意义的声学特征子集,此特征集相比于原有公用特征集在降低了维数的情况下,提高了识别准确率;(3)根据选择结果对声学特征进行分析,发现有声片段长度特征、无声片段长度特征、梅尔倒谱系数(Mel-Frequency Cepstral Coefficient, MFCC)、F0基频等特征与情感识别具有较大相关性。 相似文献
13.
Mavra Mehmood Talha Javed Jamel Nebhen Sidra Abbas Rabia Abid Giridhar Reddy Bojja Muhammad Rizwan 《计算机、材料和连续体（英文）》2022,70(1):91-107
Due to the widespread use of the internet and smart devices, various attacks like intrusion, zero-day, Malware, and security breaches are a constant threat to any organization's network infrastructure. Thus, a Network Intrusion Detection System (NIDS) is required to detect attacks in network traffic. This paper proposes a new hybrid method for intrusion detection and attack categorization. The proposed approach comprises three steps to address high false and low false-negative rates for intrusion detection and attack categorization. In the first step, the dataset is preprocessed through the data transformation technique and min-max method. Secondly, the random forest recursive feature elimination method is applied to identify optimal features that positively impact the model's performance. Next, we use various Support Vector Machine (SVM) types to detect intrusion and the Adaptive Neuro-Fuzzy System (ANFIS) to categorize probe, U2R, R2U, and DDOS attacks. The validation of the proposed method is calculated through Fine Gaussian SVM (FGSVM), which is 99.3% for the binary class. Mean Square Error (MSE) is reported as 0.084964 for training data, 0.0855203 for testing, and 0.084964 to validate multiclass categorization. 相似文献
14.
描述了一种通过声学信号检测玻璃制品缺陷的方法。在实现步骤上,首先采集了不同缺陷类型的玻璃瓶敲击声,然后经过频谱变换及小波包变换,将敲击信号映射至不同的变换域中,并在每个变换域中提取信号的特征,从而将样本的缺陷信息对应为统计特征和物理特征,并采用基于互信息量的特征选择算法对特征空间进行降维;降维后的特征子集作为后向传播神经网络的输入参数,再由该神经网络实现对玻璃缺陷的自动化检测。结果表明,在已有实验样本数据下,该缺陷检测算法能准确高效地检测出存在缺陷的样本,识别结果的F-值稳定在95%左右。 相似文献
15.
Since the web service is essential in daily lives, cyber security becomes moreand more important in this digital world. Malicious Uniform Resource Locator (URL) isa common and serious threat to cybersecurity. It hosts unsolicited content and lureunsuspecting users to become victim of scams, such as theft of private information,monetary loss, and malware installation. Thus, it is imperative to detect such threats.However, traditional approaches for malicious URLs detection that based on theblacklists are easy to be bypassed and lack the ability to detect newly generated maliciousURLs. In this paper, we propose a novel malicious URL detection method based on deeplearning model to protect against web attacks. Specifically, we firstly use auto-encoder torepresent URLs. Then, the represented URLs will be input into a proposed compositeneural network for detection. In order to evaluate the proposed system, we madeextensive experiments on HTTP CSIC2010 dataset and a dataset we collected, and theexperimental results show the effectiveness of the proposed approach. 相似文献
16.
Jaiteg Singh Tanya Gera Farman Ali Deepak Thakur Karamjeet Singh Kyung-sup Kwak 《计算机、材料和连续体（英文）》2021,66(3):2655-2670
Android has been dominating the smartphone market for more than a decade and has managed to capture 87.8% of the market share. Such popularity of Android has drawn the attention of cybercriminals and malware developers. The malicious applications can steal sensitive information like contacts, read personal messages, record calls, send messages to premium-rate numbers, cause financial loss, gain access to the gallery and can access the user’s geographic location. Numerous surveys on Android security have primarily focused on types of malware attack, their propagation, and techniques to mitigate them. To the best of our knowledge, Android malware literature has never been explored using information modelling techniques. Further, promulgation of contemporary research trends in Android malware research has never been done from semantic point of view. This paper intends to identify intellectual core from Android malware literature using Latent Semantic Analysis (LSA). An extensive corpus of 843 articles on Android malware and security, published during 2009–2019, were processed using LSA. Subsequently, the truncated singular Value Decomposition (SVD) technique was used for dimensionality reduction. Later, machine learning methods were deployed to effectively segregate prominent topic solutions with minimal bias. Apropos to observed term and document loading matrix values, this five core research areas and twenty research trends were identified. Further, potential future research directions have been detailed to offer a quick reference for information scientists. The study concludes to the fact that Android security is crucial for pervasive Android devices. Static analysis is the most widely investigated core area within Android security research and is expected to remain in trend in near future. Research trends indicate the need for a faster yet effective model to detect Android applications causing obfuscation, financial attacks and stealing user information. 相似文献
17.
Edge detection is one of the core steps of image processing and computer vision. Accurate and fine image edge will make further target detection and semantic segmentation more effective. Holistically-Nested edge detection (HED) edge detection network has been proved to be a deep-learning network with better performance for edge detection. However, it is found that when the HED network is used in overlapping complex multi-edge scenarios for automatic object identification. There will be detected edge incomplete, not smooth and other problems. To solve these problems, an image edge detection algorithm based on improved HED and feature fusion is proposed. On the one hand, features are extracted using the improved HED network: the HED convolution layer is improved. The residual variable convolution block is used to replace the normal convolution enhancement model to extract features from edges of different sizes and shapes. Meanwhile, the empty convolution is used to replace the original pooling layer to expand the receptive field and retain more global information to obtain comprehensive feature information. On the other hand, edges are extracted using Otsu algorithm: Otsu-Canny algorithm is used to adaptively adjust the threshold value in the global scene to achieve the edge detection under the optimal threshold value. Finally, the edge extracted by improved HED network and Otsu-Canny algorithm is fused to obtain the final edge. Experimental results show that on the Berkeley University Data Set (BSDS500) the optimal data set size (ODS) F-measure of the proposed algorithm is 0.793; the average precision (AP) of the algorithm is 0.849; detection speed can reach more than 25 frames per second (FPS), which confirms the effectiveness of the proposed method. 相似文献
18.
Mohammed Maray Badriyya B. Al-onazi Jaber S. Alzahrani Saeed Masoud Alshahrani Najm Alotaibi Sana Alazwari Mahmoud Othman Manar Ahmed Hamza 《计算机、材料和连续体（英文）》2023,74(3):5467-5482
The recognition of the Arabic characters is a crucial task in computer vision and Natural Language Processing fields. Some major complications in recognizing handwritten texts include distortion and pattern variabilities. So, the feature extraction process is a significant task in NLP models. If the features are automatically selected, it might result in the unavailability of adequate data for accurately forecasting the character classes. But, many features usually create difficulties due to high dimensionality issues. Against this background, the current study develops a Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition (SFODTL-AHCR) model. The projected SFODTL-AHCR model primarily focuses on identifying the handwritten Arabic characters in the input image. The proposed SFODTL-AHCR model pre-processes the input image by following the Histogram Equalization approach to attain this objective. The Inception with ResNet-v2 model examines the pre-processed image to produce the feature vectors. The Deep Wavelet Neural Network (DWNN) model is utilized to recognize the handwritten Arabic characters. At last, the SFO algorithm is utilized for fine-tuning the parameters involved in the DWNN model to attain better performance. The performance of the proposed SFODTL-AHCR model was validated using a series of images. Extensive comparative analyses were conducted. The proposed method achieved a maximum accuracy of 99.73%. The outcomes inferred the supremacy of the proposed SFODTL-AHCR model over other approaches. 相似文献
19.
Jaber S. Alzahrani Reem M. Alshehri Mohammad Alamgeer Anwer Mustafa Hilal Abdelwahed Motwakel Ishfaq Yaseen 《计算机、材料和连续体（英文）》2022,72(3):4267-4281
Recently, medical data classification becomes a hot research topic among healthcare professionals and research communities, which assist in the disease diagnosis and decision making process. The latest developments of artificial intelligence (AI) approaches paves a way for the design of effective medical data classification models. At the same time, the existence of numerous features in the medical dataset poses a curse of dimensionality problem. For resolving the issues, this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data (FSS-AICBD) technique. The FSS-AICBD technique intends to derive a useful set of features and thereby improve the classifier results. Primarily, the FSS-AICBD technique undergoes min-max normalization technique to prevent data complexity. In addition, the information gain (IG) approach is applied for the optimal selection of feature subsets. Also, group search optimizer (GSO) with deep belief network (DBN) model is utilized for biomedical data classification where the hyperparameters of the DBN model can be optimally tuned by the GSO algorithm. The choice of IG and GSO approaches results in promising medical data classification results. The experimental result analysis of the FSS-AICBD technique takes place using different benchmark healthcare datasets. The simulation results reported the enhanced outcomes of the FSS-AICBD technique interms of several measures. 相似文献
20.
由于海洋环境噪声复杂,噪声等级高,水下待识别目标信噪比低,从而造成了特征提取困难,目标识别率低的问题。基于此,文章提出了基于改进小波阈值的深度学习水下目标分类方法。此方法在传统小波阈值去噪的基础上提出了一种新的小波阈值函数,对于所采用的具体阈值将其与分解尺度相联系,从而实现降低背景噪声,提升水下目标分类识别率的目的。此方法对实测舰船辐射噪声信号进行小波分解,提取每一层的高频小波系数并对其进行处理;对处理完的信号再提取时频特征,最后将其输入后续的深度学习网络中。实验结果发现：在利用原有数据集情况下,利用基于改进小波阈值的深度学习进行水下目标的分类识别,采用卷积神经网络算法可达到88.56%的分类识别率。对前述实验结果进一步分析后,采用生成对抗网络的方法扩充数据样本,可达到96.673%的分类识别率。 相似文献