Biblio

The existing network intrusion detection methods have less label samples in the training process, and the detection accuracy is not high. In order to solve this problem, this paper designs a network intrusion detection method based on the GAN model by using the adversarial idea contained in the GAN. The model enhances the original training set by continuously generating samples, which expanding the label sample set. In order to realize the multi-classification of samples, this paper transforms the previous binary classification model of the generated adversarial network into a supervised learning multi-classification model. The loss function of training is redefined, so that the corresponding training method and parameter setting are obtained. Under the same experimental conditions, several performance indicators are used to compare the detection ability of the proposed method, the original classification model and other models. The experimental results show that the method proposed in this paper is more stable, robust, accurate detection rate, has good generalization ability, and can effectively realize network intrusion detection.

This article discusses the problem of detecting cross-site scripting (XSS) using machine learning methods. XSS is an attack in which malicious code is embedded on a page to interact with an attacker’s web server. The XSS attack ranks third in the ranking of key web application risks according to Open Source Foundation for Application Security (OWASP). This attack has not been studied for a long time. It was considered harmless. However, this is fallacious: the page or HTTP Cookie may contain very vulnerable data, such as payment document numbers or the administrator session token. Machine learning is a tool that can be used to detect XSS attacks. This article describes an experiment. As a result the model for detecting XSS attacks was created. Following machine learning algorithms are considered: the support vector method, the decision tree, the Naive Bayes classifier, and Logistic Regression. The accuracy of the presented methods is made a comparison.

Malware refers to any software accessing or being installed in a system without the authorisation of administrators. Various malware has been widely used for cyber-criminals to accomplish their evil intentions and goals. To combat the increasing amount and reduce the threat of malicious programs, a novel deep learning framework, which uses NLP techniques for reference, combines CNN and LSTM neurones to capture the locally spatial correlations and learn from sequential longterm dependency is proposed. Hence, high-level abstractions and representations are automatically extracted for the malware classification task. The classification accuracy improves from 0.81 (best one by Random Forest) to approximately 1.0.

This paper investigates the effects of real time visual biofeedback for improving sports performance using a large scale immersive mixed reality system in which users are able to play a simulated game of curling. The users slide custom curling stones across the floor onto a projected target whose size is dictated by the user’s stress-related physiological measure; heart rate (HR). The higher HR the player has, the smaller the target will be, and vice-versa. In the experiment participants were asked to compete in three different conditions: baseline, with and without the proposed biofeedback. The results show that when providing a visual representation of the player’s HR or "choking" in competition, it helped the player understand their condition and improve competition performance (P-value of 0.0391).

Aiming at the problem that the traditional intrusion detection method can not effectively deal with the massive and high-dimensional network traffic data of industrial control system (ICS), an ICS intrusion detection strategy based on bidirectional generative adversarial network (BiGAN) is proposed in this paper. In order to improve the applicability of BiGAN model in ICS intrusion detection, the optimal model was obtained through the single variable principle and cross-validation. On this basis, the supervised control and data acquisition (SCADA) standard data set is used for comparative experiments to verify the performance of the optimized model on ICS intrusion detection. The results show that the ICS intrusion detection method based on optimized BiGAN has higher accuracy and shorter detection time than other methods.

Style transfer between Chinese fonts is challenging due to both the complexity of Chinese characters and the significant difference between fonts. Existing algorithms for this task typically learn a mapping between the reference and target fonts for each character. Subsequently, this mapping is used to generate the characters that do not exist in the target font. However, the characters available for training are unlikely to cover all fine-grained parts of the missing characters, leading to the overfitting problem. As a result, the generated characters of the target font may suffer problems of incomplete or even radicals and dirty dots. To address this problem, this paper presents a multi-task adversarial learning approach, termed MTfontGAN, to generate more vivid Chinese characters. MTfontGAN learns to transfer a reference font to multiple target ones simultaneously. An alignment is imposed on the encoders of different tasks to make them focus on the important parts of the characters in general style transfer. Such cross-task interactions at the feature level effectively improve the generalization capability of MTfontGAN. The performance of MTfontGAN is evaluated on three Chinese font datasets. Experimental results show that MTfontGAN outperforms the state-of-the-art algorithms in a single-task setting. More importantly, increasing the number of tasks leads to better performance in all of them.

Artificial neural networks in general and deep learning networks in particular established themselves as popular and powerful machine learning algorithms. While the often tremendous sizes of these networks are beneficial when solving complex tasks, the tremendous number of parameters also causes such networks to be vulnerable to malicious behavior such as adversarial perturbations. These perturbations can change a model's classification decision. Moreover, while single-step adversaries can easily be transferred from network to network, the transfer of more powerful multi-step adversaries has - usually - been rather difficult.In this work, we introduce a method for generating strong adversaries that can easily (and frequently) be transferred between different models. This method is then used to generate a large set of adversaries, based on which the effects of selected defense methods are experimentally assessed. At last, we introduce a novel, simple, yet effective approach to enhance the resilience of neural networks against adversaries and benchmark it against established defense methods. In contrast to the already existing methods, our proposed defense approach is much more efficient as it only requires a single additional forward-pass to achieve comparable performance results.

The Global Positioning System (GPS) can determine the position of any person or object on earth based on satellite signals. But when inside the building, the GPS cannot receive signals, the indoor positioning system will determine the precise position. How to achieve more precise positioning is the difficulty of an indoor positioning system now. In this paper, we proposed an ultra-wideband fingerprinting positioning method based on a convolutional neural network (CNN), and we collect the dataset in a room to test the model, then compare our method with the existing method. In the experiment, our method can reach an accuracy of 98.36%. Compared with other fingerprint positioning methods our method has a great improvement in robustness. That results show that our method has good practicality while achieves higher accuracy.

We are answering the question whenever systems with convolutional neural network classifier trained over plain and encrypted data keep the ordering according to accuracy. Our motivation is need for designing convolutional neural network classifiers when data in their plain form are not accessible because of private company policy or sensitive data gathered by police. We propose to use a combination of fully connected autoencoder together with a convolutional neural network classifier. The autoencoder transforms the data info form that allows the convolutional classifier to be trained. We present three experiments that show the ordering of systems over plain and encrypted data. The results show that the systems indeed keep the ordering, and thus a NN designer can select appropriate architecture over encrypted data and later let data owner train or fine-tune the system/CNN classifier on the plain data.

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.

Image forensics is an increasingly relevant problem, as it can potentially address online disinformation campaigns and mitigate problematic aspects of social media. Of particular interest, given its recent successes, is the detection of imagery produced by Generative Adversarial Networks (GANs), e.g. `deepfakes'. Leveraging large training sets and extensive computing resources, recent GANs can be trained to generate synthetic imagery which is (in some ways) indistinguishable from real imagery. We analyze the structure of the generating network of a popular GAN implementation [1], and show that the network's treatment of exposure is markedly different from a real camera. We further show that this cue can be used to distinguish GAN-generated imagery from camera imagery, including effective discrimination between GAN imagery and real camera images used to train the GAN.

"Deepfake" it is an incipiently emerging face video forgery technique predicated on AI technology which is used for creating the fake video. It takes images and video as source and it coalesces these to make a new video using the generative adversarial network and the output is very convincing. This technique is utilized for generating the unauthentic spurious video and it is capable of making it possible to generate an unauthentic spurious video of authentic people verbally expressing and doing things that they never did by swapping the face of the person in the video. Deepfake can create disputes in countries by influencing their election process by defaming the character of the politician. This technique is now being used for character defamation of celebrities and high-profile politician just by swapping the face with someone else. If it is utilized in unethical ways, this could lead to a serious problem. Someone can use this technique for taking revenge from the person by swapping face in video and then posting it to a social media platform. In this paper, working of Deepfake technique along with how it can swap faces with maximum precision in the video has been presented. Further explained are the different ways through which we can identify if the video is generated by Deepfake and its advantages and drawback have been listed.

Detecting DeepFake videos are one of the challenges in digital media forensics. This paper proposes a method to detect deepfake videos using Support Vector Machine (SVM) regression. The SVM classifier can be trained with feature points extracted using one of the different feature-point detectors such as HOG, ORB, BRISK, KAZE, SURF, and FAST algorithms. A comprehensive test of the proposed method is conducted using a dataset of original and fake videos from the literature. Different feature point detectors are tested. The result shows that the proposed method of using feature-detector-descriptors for training the SVM can be effectively used to detect false videos.

AI-synthesized face-swapping videos, commonly known as DeepFakes, is an emerging problem threatening the trustworthiness of online information. The need to develop and evaluate DeepFake detection algorithms calls for datasets of DeepFake videos. However, current DeepFake datasets suffer from low visual quality and do not resemble DeepFake videos circulated on the Internet. We present a new large-scale challenging DeepFake video dataset, Celeb-DF, which contains 5,639 high-quality DeepFake videos of celebrities generated using improved synthesis process. We conduct a comprehensive evaluation of DeepFake detection methods and datasets to demonstrate the escalated level of challenges posed by Celeb-DF.

An image forgery method called Deepfakes can cause security and privacy issues by changing the identity of a person in a photo through the replacement of his/her face with a computer-generated image or another person's face. Therefore, a new challenge of detecting Deepfakes arises to protect individuals from potential misuses. Many researchers have proposed various binary-classification based detection approaches to detect deepfakes. However, binary-classification based methods generally require a large amount of both real and fake face images for training, and it is challenging to collect sufficient fake images data in advance. Besides, when new deepfakes generation methods are introduced, little deepfakes data will be available, and the detection performance may be mediocre. To overcome these data scarcity limitations, we formulate deepfakes detection as a one-class anomaly detection problem. We propose OC-FakeDect, which uses a one-class Variational Autoencoder (VAE) to train only on real face images and detects non-real images such as deepfakes by treating them as anomalies. Our preliminary result shows that our one class-based approach can be promising when detecting Deepfakes, achieving a 97.5% accuracy on the NeuralTextures data of the well-known FaceForensics++ benchmark dataset without using any fake images for the training process.

This work uses adversarial perturbations to enhance deepfake images and fool common deepfake detectors. We created adversarial perturbations using the Fast Gradient Sign Method and the Carlini and Wagner L2 norm attack in both blackbox and whitebox settings. Detectors achieved over 95% accuracy on unperturbed deepfakes, but less than 27% accuracy on perturbed deepfakes. We also explore two improvements to deep-fake detectors: (i) Lipschitz regularization, and (ii) Deep Image Prior (DIP). Lipschitz regularization constrains the gradient of the detector with respect to the input in order to increase robustness to input perturbations. The DIP defense removes perturbations using generative convolutional neural networks in an unsupervised manner. Regularization improved the detection of perturbed deepfakes on average, including a 10% accuracy boost in the blackbox case. The DIP defense achieved 95% accuracy on perturbed deepfakes that fooled the original detector while retaining 98% accuracy in other cases on a 100 image subsample.

Photo- and video-realistic generation techniques have become a reality following the advent of deep neural networks. Consequently, there are immense concerns regarding the difficulty in differentiating what content is real from what is synthetic. An example of video-realistic generation techniques is the infamous Deepfakes, which exploit the main modality by which humans identify each other. Deepfakes are a category of synthetic face generation methods and are commonly based on generative adversarial networks. In this article, we propose a novel two-step synthetic face image detection method in which general-purpose features are extracted in a first step, trivializing the task of detecting synthetic images. The anomaly detector predicts the conditional probabilities for observing every individual pixel in the image and is trained on pristine data only. The extracted anomaly features demonstrate true generalization capacity across widely different unknown synthesis methods while showing a minimal loss in performance with regard to the detection of known synthetic samples.

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.

By the multi-layer nonlinear mapping and the semantic feature extraction of the deep learning, a deep learning network is proposed for video face detection to overcome the challenge of detecting faces rapidly and accurately in video with changeable background. Particularly, a pre-training procedure is used to initialize the network parameters to avoid falling into the local optimum, and the greedy layer-wise learning is introduced in the pre-training to avoid the training error transfer in layers. Key to the network is that the probability of neurons models the status of human brain neurons which is a continuous distribution from the most active to the least active and the hidden layer’s neuron number decreases layer-by-layer to reduce the redundant information of the input data. Moreover, the skin color detection is used to accelerate the detection speed by generating candidate regions. Experimental results show that, besides the faster detection speed and robustness against face rotation, the proposed method possesses lower false detection rate and lower missing detection rate than traditional algorithms.

In this paper, we apply machine learning to distributed private data owned by multiple data owners, entities with access to non-overlapping training datasets. We use noisy, differentially-private gradients to minimize the fitness cost of the machine learning model using stochastic gradient descent. We quantify the quality of the trained model, using the fitness cost, as a function of privacy budget and size of the distributed datasets to capture the trade-off between privacy and utility in machine learning. This way, we can predict the outcome of collaboration among privacy-aware data owners prior to executing potentially computationally-expensive machine learning algorithms. Particularly, we show that the difference between the fitness of the trained machine learning model using differentially-private gradient queries and the fitness of the trained machine model in the absence of any privacy concerns is inversely proportional to the size of the training datasets squared and the privacy budget squared. We successfully validate the performance prediction with the actual performance of the proposed privacy-aware learning algorithms, applied to: financial datasets for determining interest rates of loans using regression; and detecting credit card frauds using support vector machines.

Accurate traffic classification is fundamentally important for various network activities such as fine-grained network management and resource utilisation. Port-based approaches, deep packet inspection and machine learning are widely used techniques to classify and analyze network traffic flows. However, over the past several years, the growth of Internet traffic has been explosive due to the greatly increased number of Internet users. Therefore, both port-based and deep packet inspection approaches have become inefficient due to the exponential growth of the Internet applications that incurs high computational cost. The emerging paradigm of software-defined networking has reshaped the network architecture by detaching the control plane from the data plane to result in a centralised network controller that maintains a global view over the whole network on its domain. In this paper, we propose a new deep learning model for software-defined networks that can accurately identify a wide range of traffic applications in a short time, called Deep-SDN. The performance of the proposed model was compared against the state-of-the-art and better results were reported in terms of accuracy, precision, recall, and f-measure. It has been found that 96% as an overall accuracy can be achieved with the proposed model. Based on the obtained results, some further directions are suggested towards achieving further advances in this research area.

Steganalysis is an interesting classification problem in order to discriminate the images, including hidden messages from the clean ones. There are many methods, including deep CNN networks to extract fine features for this classification task. Nevertheless, a few researches have been conducted to improve the final classifier. Some state-of-the-art methods try to ensemble the networks by a voting strategy to achieve more stable performance. In this paper, a selection phase is proposed to filter improper networks before any voting. This filtering is done by a binary relevance multi-label classification approach. The Logistic Regression (LR) is chosen here as the last layer of network for classification. The large-margin Fisher’s linear discriminant (FLD) classifier is assigned to each one of the networks. It learns to discriminate the training instances which associated network is suitable for or not. Xu-Net, one of the most famous state-of-the-art Steganalysis models, is chosen as the base networks. The proposed method with different approaches is applied on the BOSSbase dataset and is compared with traditional voting and also some state-of-the-art related ensemble techniques. The results show significant accuracy improvement of the proposed method in comparison with others.

Robot Operating System (ROS) is becoming more and more important and is used widely by developers and researchers in various domains. One of the most important fields where it is being used is the self-driving cars industry. However, this framework is far from being totally secure, and the existing security breaches do not have robust solutions. In this paper we focus on the camera vulnerabilities, as it is often the most important source for the environment discovery and the decision-making process. We propose an unsupervised anomaly detection tool for detecting suspicious frames incoming from camera flows. Our solution is based on spatio-temporal autoencoders used to truthfully reconstruct the camera frames and detect abnormal ones by measuring the difference with the input. We test our approach on a real-word dataset, i.e. flows coming from embedded cameras of self-driving cars. Our solution outperforms the existing works on different scenarios.

Network behavior anomaly detection (NBAD) require fast mechanisms for learning from the large scale data. However, the training velocity of general machine learning approach is largely limited by the adopted training weights of all features in the NBAD. In this paper, we notice, however, that the related weights matching of NBAD features is sparse, which is not necessary for holding all weights. Hence, in this paper, we consider an efficient support vector machine (SVM) approach for NBAD by imposing 1 -norm. Essentially, we propose to use sparse SVM (S-SVM), where sparsity in model, i.e. in weights is used to interfere with special feature selection and that can achieve feature selection and classification efficiently.

Intrusion detection system based on representation learning is the main research direction in the field of anomaly detection. Malicious traffic detection system can distinguish normal and malicious traffic by learning representations between normal and malicious traffic. However, under the context of big data, there are many types of malicious traffic, and the features are also changing constantly. It is still a urgent problem to design a detection model that can effectively learn and summarize the feature of normal traffic and accurately identify the features of new kinds of malicious traffic.in this paper, a malicious traffic detection method based on Deep Support Vector Data Description is proposed, which is called Deep - SVDD. We combine convolutional neural network (CNN) with support vector data description, and train the model with normal traffic. The normal traffic features are mapped to high-dimensional space through neural networks, and a compact hypersphere is trained by unsupervised learning, which includes the normal features of the highdimensional space. Malicious traffic fall outside the hypersphere, thus distinguishing between normal and malicious traffic. Experiments show that the model has a high detection rate and a low false alarm rate, and it can effectively identify new malicious traffic.