Biblio

3D reconstruction has piqued the interest of many disciplines, and many researchers have spent the last decade striving to improve on latest automated three-dimensional reconstruction systems. Three Dimensional models can be utilized to tackle a wide range of visualization problems as well as other activities. In this paper, we have implemented a method of Digital Surface Map (DSM) generation from Aerial images using Conditional Generative Adversarial Networks (c-GAN). We have used Seg-net architecture of Convolutional Neural Network (CNN) to segment the aerial images and then the U-net generator of c-GAN generates final DSM. The dataset we used is ISPRS Potsdam-Vaihingen dataset. We also review different stages if 3D reconstruction and how Deep learning is now being widely used to enhance the process of 3D data generation. We provide binary cross entropy loss function graph to demonstrate stability of GAN and CNN. The purpose of our approach is to solve problem of DSM generation using Deep learning techniques. We put forth our method against other latest methods of DSM generation such as Semi-global Matching (SGM) and infer the pros and cons of our approach. Finally, we suggest improvements in our methods that might be useful in increasing the accuracy.

With the advancement in Generative Adversarial Networks (GAN), it has become easier than ever to generate fake images. These images are more realistic and non-discernible by untrained eyes and can be used to propagate fake information on the Internet. In this paper, we propose a novel method to detect GAN generated fake images by using a combination of frequency spectrum of image and deep learning. We apply Discrete Fourier Transform to each of 3 color channels of the image to obtain its frequency spectrum which shows if the image has been upsampled, a common trend in most GANs, and then train a Capsule Network model with it. Conducting experiments on a dataset of almost 1000 images based on Unconditional data modeling (StyleGan2 - ADA) gave results indicating that the model is promising with accuracy over 99% when trained on the state-of-the-art GAN model. In theory, our model should give decent results when trained with one dataset and tested on another.

How to obtain accurate travel time predictions is among the most critical problems in Intelligent Transportation Systems (ITS). Recent literature has shown the effectiveness of machine learning models on travel time forecasting problems. However, most of these models predict travel time in a point estimation manner, which is not suitable for real scenarios. Instead of a determined value, the travel time within a future time period is a distribution. Besides, they all use grid structure data to obtain the spatial dependency, which does not reflect the traffic network's actual topology. Hence, we propose GCGTTE to estimate the travel time in a distribution form with Graph Deep Learning and Generative Adversarial Network (GAN). We convert the data into a graph structure and use a Graph Neural Network (GNN) to build its spatial dependency. Furthermore, GCGTTE adopts GAN to approximate the real travel time distribution. We test the effectiveness of GCGTTE with other models on a real-world dataset. Thanks to the fine-grained spatial dependency modeling, GCGTTE outperforms the models that build models on a grid structure data significantly. Besides, we also compared the distribution approximation performance with DeepGTT, a Variational Inference-based model which had the state-of-the-art performance on travel time estimation. The result shows that GCGTTE outperforms DeepGTT on metrics and the distribution generated by GCGTTE is much closer to the original distribution.

Using Generative Adversarial Networks (GAN) to translate images is a significant field in computer vision. There are partial distortion, artifacts and detail loss in the images generated by current image translation algorithms. In order to solve this problem, this paper adds attention-based residual neural network to the generator of GAN. Attention-based residual neural network can improve the representation ability of the generator by weighting the channels of the feature map. Experiment results on the Facades dataset show that Attention Residual GAN can translate images with excellent quality.

Image style transfer refers to the transformation of the style of image, so that the image details are retained to the maximum extent while the style is transferred. Aiming at the problem of low clarity of style transfer images generated by CycleGAN network, this paper improves the CycleGAN network. In this paper, the network model of auto-encoder and variational auto-encoder is added to the structure. The encoding part of the auto-encoder is used to extract image content features, and the variational auto-encoder is used to extract style features. At the same time, the generating network of the model in this paper uses first to adjust the image size and then perform the convolution operation to replace the traditional deconvolution operation. The discriminating network uses a multi-scale discriminator to force the samples generated by the generating network to be more realistic and approximate the target image, so as to improve the effect of image style transfer.

Text steganography aims to embed hidden messages in text information while the goal of text steganalysis is to identify the existence of hidden information or further uncover the embedded message from the text. Steganalysis has received significant attention recently for the security and privacy purpose. In this paper, we develop unsupervised learning approaches for text steganalysis. In particular, two detection models based on deep learning have been proposed to detect hidden information that may be embedded in text from a global and a local perspective. Extensive studies have been carried out on the Chinese poetry text steganography datasets. It is seen that the proposed models show strong empirical performance in steganographic text detection.

Intrusion detection is a traditional practice of security experts, however, there are several issues which still need to be tackled. Therefore, in this paper, after highlighting these issues, we present an architecture for a hybrid Intrusion Detection System (IDS) for an adaptive and incremental detection of both known and unknown attacks. The IDS is composed of supervised and unsupervised modules, namely, a Deep Neural Network (DNN) and the K-Nearest Neighbors (KNN) algorithm, respectively. The proposed system is near-autonomous since the intervention of the expert is minimized through the active learning (AL) approach. A query strategy for the labeling process is presented, it aims at teaching the supervised module to detect unknown attacks and improve the detection of the already-known attacks. This teaching is achieved through sliding windows (SW) in an incremental fashion where the DNN is retrained when the data is available over time, thus rendering the IDS adaptive to cope with the evolutionary aspect of the network traffic. A set of experiments was conducted on the CICIDS2017 dataset in order to evaluate the performance of the IDS, promising results were obtained.

The persistent growth in phishing and the rising volume of phishing websites has led to individuals and organizations worldwide becoming increasingly exposed to various cyber-attacks. Consequently, more effective phishing detection is required for improved cyber defence. Hence, in this paper we present a deep learning-based approach to enable high accuracy detection of phishing sites. The proposed approach utilizes convolutional neural networks (CNN) for high accuracy classification to distinguish genuine sites from phishing sites. We evaluate the models using a dataset obtained from 6,157 genuine and 4,898 phishing websites. Based on the results of extensive experiments, our CNN based models proved to be highly effective in detecting unknown phishing sites. Furthermore, the CNN based approach performed better than traditional machine learning classifiers evaluated on the same dataset, reaching 98.2% phishing detection rate with an F1-score of 0.976. The method presented in this paper compares favourably to the state-of-the art in deep learning based phishing website detection.

Users prefer to do e-banking and e-shopping now-a-days because of the exponential growth of the internet. Because of this paradigm shift, hackers are finding umpteen ways to steal our personal information and critical details like details of debit and credit cards, by disguising themselves as reputed websites, just by changing the spelling or making minor modifications to the URL. Identifying whether an URL is benign or malicious is a challenging job, because it makes use of the weakness of the user. While there are several works carried out to detect phishing websites, they only use heuristic methods and list based techniques and therefore couldn't avoid phishing effectively. In this paper an anti-phishing system was proposed to protect the users. It uses an ensemble model that uses both LSTM and CNN with a massive data set containing nearly 2,00,000 URLs, that is balanced. After analyzing the accuracy of different existing approaches, it has been found that the ensemble model that uses both LSTM and CNN performed better with an accuracy of 96% and the precision is 97% respectively which is far better than the existing solutions.

Today, the Internet covers worldwide. All over the world, people prefer an E-commerce platform to buy or sell their products. Therefore, cybercrime has become the center of attraction for cyber attackers in cyberspace. Phishing is one such technique where the unidentified structure of the Internet has been used by attackers/criminals that intend to deceive users with the use of the illusory website and emails for obtaining their credentials (like account numbers, passwords, and PINs). Consequently, the identification of a phishing or legitimate web page is a challenging issue due to its semantic structure. In this paper, a phishing detection system is implemented using deep learning techniques to prevent such attacks. The system works on URLs by applying a convolutional neural network (CNN) to detect the phishing webpage. In paper [19] the proposed model has achieved 97.98% accuracy whereas our proposed system achieved accuracy of 98.00% which is better than earlier model. This system doesn’t require any feature engineering as the CNN extract features from the URLs automatically through its hidden layers. This is other advantage of the proposed system over earlier reported in [19] as the feature engineering is a very time-consuming task.

Data mining technology is a very important technology in the current era of data explosion. With the informationization of society and the transparency and openness of information, network security issues have become the focus of concern of people all over the world. This paper wants to compare the accuracy of multiple machine learning methods and two deep learning frameworks when using lexical features to detect and classify malicious URLs. As a result, this paper shows that the Random Forest, which is an ensemble learning method for classification, is superior to 8 other machine learning methods in this paper. Furthermore, the Random Forest is even superior to some popular deep neural network models produced by famous frameworks such as TensorFlow and PyTorch when using lexical features to detect and classify malicious URLs.

Recently, the threats of phishing attacks have in-creased. As a countermeasure against phishing attacks, phishing detection systems using deep learning techniques have been considered. However, deep learning techniques are vulnerable to adversarial examples (AEs) that intentionally cause misclassification. When AEs are applied to a deep-learning-based phishing detection system, they pose a significant security risk. Therefore, in this paper, we assess the vulnerability of a phishing detection system by inputting AEs generated based on a dataset that consists of phishing sites’ URLs. Moreover, we consider countermeasures against AEs and clarify whether these defense methods can prevent misclassification.

Recently, DDoS attack has developed rapidly and become one of the most important threats to the Internet. Traditional machine learning and deep learning methods can-not train a satisfactory model based on the data of a single client. Moreover, in the real scenes, there are a large number of devices used for traffic collection, these devices often do not want to share data between each other depending on the research and analysis value of the attack traffic, which limits the accuracy of the model. Therefore, to solve these problems, we design a DDoS attack detection model based on federated learning named FLDDoS, so that the local model can learn the data of each client without sharing the data. In addition, considering that the distribution of attack detection datasets is extremely imbalanced and the proportion of attack samples is very small, we propose a hierarchical aggregation algorithm based on K-Means and a data resampling method based on SMOTEENN. The result shows that our model improves the accuracy by 4% compared with the traditional method, and reduces the number of communication rounds by 40%.

The protection of Intellectual Property (IP) has gradually increased in recent years. Traditional intellectual property management service has lower efficiency for such scale of data. Considering that the maturity of deep learning models has led to the development of knowledge graphs. Relevant researchers have investigated the application of knowledge graphs in different domains, such as medical services, social media, etc. However, few studies of knowledge graphs have been undertaken in the domain of intellectual property. In this paper, we introduce the process of building a domain knowledge graph and start from data preparation to conduct the research of named entity recognition.

With an increasing number of adversarial attacks against Industrial Control Systems (ICS) networks, enhancing the security of such systems is invaluable. Although attack prevention strategies are often in place, protecting against all attacks, especially zero-day attacks, is becoming impossible. Intrusion Detection Systems (IDS) are needed to detect such attacks promptly. Machine learning-based detection systems, especially deep learning algorithms, have shown promising results and outperformed other approaches. In this paper, we study the efficacy of a deep learning approach, namely, Multi Layer Perceptron (MLP), in detecting abnormal behaviors in ICS network traffic. We focus on very common reconnaissance attacks in ICS networks. In such attacks, the adversary focuses on gathering information about the targeted network. To evaluate our approach, we compare MLP with isolation Forest (i Forest), a statistical machine learning approach. Our proposed deep learning approach achieves an accuracy of more than 99% while i Forest achieves only 75%. This helps to reinforce the promise of using deep learning techniques for anomaly detection.

System logs record valuable information about the runtime status of IT systems. Therefore, system logs are a naturally excellent source of information for anomaly detection. Most of the existing studies on log-based anomaly detection construct a detection model to identify anomalous logs. Generally, the model treats historical logs as natural language sequences and learns the normal patterns from normal log sequences, and detects deviations from normal patterns as anomalies. However, the majority of existing methods focus on sequential and quantitative information and ignore semantic information hidden in log sequence so that they are inefficient in anomaly detection. In this paper, we propose a novel framework for automatically detecting log anomalies by utilizing an attention-based Bi-LSTM model. To demonstrate the effectiveness of our proposed model, we evaluate the performance on a public production log dataset. Extensive experimental results show that the proposed approach outperforms all comparison methods for anomaly detection.

Information systems and applications provide indispensable services at every stage of life, enabling us to carry out our activities more effectively and efficiently. Today, information technology systems produce many alarm and event records. These produced records often have a relationship with each other, and when this relationship is captured correctly, many interruptions that will harm institutions can be prevented before they occur. For example, an increase in the disk I/O speed of a server or a problem may cause the business software running on that server to slow down and cause different results in this slowness. Here, an institution’s accurate analysis and management of all event records, and rule-based analysis of the resulting records in certain time periods and depending on certain rules will ensure efficient and effective management of millions of alarms. In addition, it will be possible to prevent possible problems by removing the relationships between events. Events that occur in IT systems are a kind of footprint. It is also vital to keep a record of the events in question, and when necessary, these event records can be analyzed to analyze the efficiency of the systems, harmful interferences, system failure tendency, etc. By understanding the undesirable situations such as taking the necessary precautions, possible losses can be prevented. In this study, the model developed for fault prediction in systems by performing event log analysis in information systems is explained and the experimental results obtained are given.

The number of organisational cybersecurity threats continues to increase every year as technology advances. All too often, organisations assume that implementing systems security measures like firewalls and anti-virus software will eradicate cyber threats. However, even the most robust security systems are vulnerable to threats. As advanced as machine learning cybersecurity technology is becoming, it cannot be solely relied upon to solve cyber threats. There are other forces that contribute to these threats that are many-a-times out of an organisation's control i.e., human behaviour. This research article aims to create an understanding of the trends in key cybersecurity management issues that have developed in the past five years in relation to human behaviour and machine learning. The methodology adopted to guide the synthesis of this review was a systematic literature review. The guidelines for conducting the review are presented in the review approach. The key cybersecurity management issues highlighted by the research includes risky security behaviours demonstrated by employees, social engineering, the current limitations present in machine learning insider threat detection, machine learning enhanced cyber threats, and the underinvestment challenges faced in the cybersecurity domain.

The advent of IoT edge devices has enabled the collection of rich datasets, as part of Mobile Crowd Sensing (MCS), which has emerged as a key enabler for a wide gamut of safety-critical applications ranging from traffic control, environmental monitoring to assistive healthcare. Despite the clear advantages that such unprecedented quantity of data brings forth, it is also subject to inherent data trustworthiness challenges due to factors such as malevolent input and faulty sensors. Compounding this issue, there has been a plethora of proposed solutions, based on the use of traditional machine learning algorithms, towards assessing and sifting faulty data without any assumption on the trustworthiness of their source. However, there are still a number of open issues: how to cope with the presence of strong, colluding adversaries while at the same time efficiently managing this high influx of incoming user data. In this work, we meet these challenges by proposing the hybrid use of Deep Learning schemes (i.e., LSTMs) and conventional Machine Learning classifiers (i.e. One-Class Classifiers) for detecting and filtering out false data points. We provide a prototype implementation coupled with a detailed performance evaluation under various (attack) scenarios, employing both real and synthetic datasets. Our results showcase how the proposed solution outperforms various existing resilient aggregation and outlier detection schemes.

Fog computing extends cloud resources to the edge of the network, thus enabling network providers to support real-time applications at low latencies. These applications further demand high security against malicious attacks that target distributed fog servers. One effective defense mechanism here against cyber attacks is the use of honeypots. The latter acts as a potential target for attackers by diverting malicious traffic away from the servers that are dedicated to legitimate users. However, one main limitation of honeypots is the lack of real traffic and network activities. Therefore, it is important to implement a solution that simulates the behavior of the real system to lure attackers without the risk of being exposed. Hence this paper proposes a practical approach to generate network traffic by introducing decoy virtual network functions (VNF) embedded on fog servers, which make the network traffic on honeypots resemble a legitimate, vulnerable fog system to attract cyber attackers. The use of virtualization allows for robust scalability and modification of network functions based on incoming attacks, without the need for dedicated hardware. Moreover, deep learning is leveraged here to build fingerprints for each real VNF, which is subsequently used to support its decoy counterpart against active probes. The proposed framework is evaluated based on CPU utilization, memory usage, disk input/output access, and network latency.

A text is a meaningful source of information. Capturing the right patterns in written text gives metrics to measure and infer to what extent this text belongs or is relevant to a specific author. This research aims to introduce a new feature that goes more in deep in the language structure. The feature introduced is based on an attempt to differentiate stylistic changes among authors according to the different sentence structure each author uses. The study showed the effect of introducing this new feature to machine learning models to enhance their performance. It was found that the prediction of authors was enhanced by adding sentence structure as an additional feature as the f1\_scores increased by 0.3% and when normalizing the data and adding the feature it increased by 5%.

Code authorship identification can assist in identifying creators of malware, identifying plagiarism, and giving insights in copyright infringement cases. Taking inspiration from facial recognition work, we apply recent advances in metric learning to the problem of authorship identification and verification. The metric learning approach makes it possible to measure similarity in the learned embedding space. Access to a discriminative similarity measure allows for the estimation of probability distributions that facilitate open-set classification and verification. We extend our analysis to verification based on sets of files, a previously unexplored problem domain in large-scale author identification. On closed-set tasks we achieve competitive accuracies, but do not improve on the state of the art.

Identification of source code authorship is vital for attribution. In this work, a machine learning framework is described to identify source code authorship. The framework integrates the features extracted using natural language processing based approaches and abstract syntax tree of the code. We evaluate the methodology on Google Code Jam dataset. We present the performance measures of the logistic regression and deep learning on the dataset.

Previous research on frequency hopping (FH) signal recognition utilizing deep learning only focuses on single-label signal, but can not deal with overlapped FH signal which has multi-labels. To solve this problem, we propose a new FH signal recognition method based on fully convolutional networks (FCN). Firstly, we perform the short-time Fourier transform (STFT) on the collected FH signal to obtain a two-dimensional time-frequency pattern with time, frequency, and intensity information. Then, the pattern will be put into an improved FCN model, named FH-FCN, to make a pixel-level prediction. Finally, through the statistics of the output pixels, we can get the final classification results. We also design an algorithm that can automatically generate dataset for model training. The experimental results show that, for an overlapped FH signal, which contains up to four different types of signals, our method can recognize them correctly. In addition, the separation of multiple FH signals can be achieved by a slight improvement of our method.

Traffic Signal Control using Reinforcement Learning has been proved to have potential in alleviating traffic congestion in urban areas. Although research has been conducted in this field, it is still an open challenge to find an effective but low-cost solution to this problem. This paper presents multiple deep reinforcement learning-based traffic signal control systems that can help regulate the flow of traffic at intersections and then compares the results. The proposed systems are coupled with SUMO (Simulation of Urban MObility), an agent-based simulator that provides a realistic environment to explore the outcomes of the models.