Biblio

Nowadays deepfake videos are concern with social ethics, privacy and security. Deepfake videos are synthetically generated videos that are generated by modifying the facial features and audio features to impose one person’s facial data and audio to other videos. These videos can be used for defaming and fraud. So, counter these types of manipulations and threats, detection of deepfake video is needed. This paper proposes multilayer hybrid recurrent deep learning models for deepfake video detection. Proposed models exploit the noise-based temporal facial convolutional features and temporal learning of hybrid recurrent deep learning models. Experiment results of these models demonstrate its performance over stacked recurrent deep learning models.

Anomaly detection is a popular and vital task in various research contexts, which has been studied for several decades. To ensure the safety of people’s lives and assets, video surveillance has been widely deployed in various public spaces, such as crossroads, elevators, hospitals, banks, and even in private homes. Deep learning has shown its capacity in a number of domains, ranging from acoustics, images, to natural language processing. However, it is non-trivial to devise intelligent video anomaly detection systems cause anomalies significantly differ from each other in different application scenarios. There are numerous advantages if such intelligent systems could be realised in our daily lives, such as saving human resources in a large degree, reducing financial burden on the government, and identifying the anomalous behaviours timely and accurately. Recently, many studies on extending deep learning models for solving anomaly detection problems have emerged, resulting in beneficial advances in deep video anomaly detection techniques. In this paper, we present a comprehensive review of deep learning-based methods to detect the video anomalies from a new perspective. Specifically, we summarise the opportunities and challenges of deep learning models on video anomaly detection tasks, respectively. We put forth several potential future research directions of intelligent video anomaly detection system in various application domains. Moreover, we summarise the characteristics and technical problems in current deep learning methods for video anomaly detection.

Due to immense need for implementing security measures and control ongoing activities, intelligent video analytics is regarded as one of the outstanding and challenging research domains in Computer Vision. Assigning video operator to manually monitor the surveillance videos 24×7 to identify occurrence of interesting and anomalous events like robberies, wrong U-turns, violence, accidents is cumbersome and error- prone. Therefore, to address the issue of continuously monitoring surveillance videos and detect the anomalies from them, a deep learning approach based on pipelined sequence of convolutional autoencoder and sequence to sequence long short-term memory autoencoder has been proposed. Specifically, unsupervised learning approach encompassing one-class classification paradigm has been proposed for detection of anomalies in videos. The effectiveness of the propped model is demonstrated on benchmarked anomaly detection dataset and significant results in terms of equal error rate, area under curve and time required for detection have been achieved.

Digitization has pioneered to drive exceptional changes across all industries in the advancement of analytics, automation, and Artificial Intelligence (AI) and Machine Learning (ML). However, new business requirements associated with the efficiency benefits of digitalization are forcing increased connectivity between IT and OT networks, thereby increasing the attack surface and hence the cyber risk. Cyber threats are on the rise and securing industrial networks are challenging with the shortage of human resource in OT field, with more inclination to IT/OT convergence and the attackers deploy various hi-tech methods to intrude the control systems nowadays. We have developed an innovative real-time ICS cyber test kit to obtain the OT industrial network traffic data with various industrial attack vectors. In this paper, we have introduced the industrial datasets generated from ICS test kit, which incorporate the cyber-physical system of industrial operations. These datasets with a normal baseline along with different industrial hacking scenarios are analyzed for research purposes. Metadata is obtained from Deep packet inspection (DPI) of flow properties of network packets. DPI analysis provides more visibility into the contents of OT traffic based on communication protocols. The advancement in technology has led to the utilization of machine learning/artificial intelligence capability in IDS ICS SCADA. The industrial datasets are pre-processed, profiled and the abnormality is analyzed with DPI. The processed metadata is normalized for the easiness of algorithm analysis and modelled with machine learning-based latest deep learning ensemble LSTM algorithms for anomaly detection. The deep learning approach has been used nowadays for enhanced OT IDS performances.

Power quality gains more and more attentions because disturbances in power quality may damage equipment security, power availability and system reliability in power system. Detection and classification of the power quality disturbances is the first step before taking measures to lessen their harmful effects. Common methods to classify power quality disturbances includes signal processing methods, machine learning methods and deep learning methods. Signal processing methods are good at feature extraction, while machine learning methods and deep learning methods are expert in multi-classification tasks. Via combing their respective advantages, this paper proposes a combined method based on variational mode decomposition and convolutional neural networks, which needs a small quantity of samples but achieves high classification precision. The proposed method is proved to be a qualified and competitive scheme for the detection and classification of power quality disturbances.

Modern power systems depend on cyber-physical systems to link physical devices and control technologies. A major concern in the implementation of smart power networks is to minimize the risk of data privacy violation (e.g., by adversaries using data poisoning and inference attacks). In this article, we propose a privacy-preserving framework to achieve both privacy and security in smart power networks. The framework includes two main modules: a two-level privacy module and an anomaly detection module. In the two-level privacy module, an enhanced-proof-of-work-technique-based blockchain is designed to verify data integrity and mitigate data poisoning attacks, and a variational autoencoder is simultaneously applied for transforming data into an encoded format for preventing inference attacks. In the anomaly detection module, a long short-term memory deep learning technique is used for training and validating the outputs of the two-level privacy module using two public datasets. The results highlight that the proposed framework can efficiently protect data of smart power networks and discover abnormal behaviors, in comparison to several state-of-the-art techniques.

With the progress of science and technology and the development of Internet technology, Internet technology has penetrated into various industries in today’s society. But this explosive growth is also troubling information security. Among them, XSS (cross-site scripting vulnerability) is one of the most influential vulnerabilities in Internet applications in recent years. Traditional network security detection technology is becoming more and more weak in the new network environment, and deep learning methods such as CNN and RNN can only learn the spatial or timing characteristics of data samples in a single way. In this paper, a generalized self-regression pretraining model XLNet and GRU XSS attack detection method is proposed, the self-regression pretrained model XLNet is introduced and combined with GRU to learn the time series and spatial characteristics of the data, and the generalization capability of the model is improved by using dropout. Faced with the increasingly complex and ever-changing XSS payload, this paper refers to the character-level convolution to establish a dictionary to encode the data samples, thus preserving the characteristics of the original data and improving the overall efficiency, and then transforming it into a two-dimensional spatial matrix to meet XLNet’s input requirements. The experimental results on the Github data set show that the accuracy of this method is 99.92 percent, the false positive rate is 0.02 percent, the accuracy rate is 11.09 percent higher than that of the DNN method, the false positive rate is 3.95 percent lower, and other evaluation indicators are better than GRU, CNN and other comparative methods, which can improve the detection accuracy and system stability of the whole detection system. This multi-model fusion method can make full use of the advantages of each model to improve the accuracy of system detection, on the other hand, it can also enhance the stability of the system.

E-commerce, ticket booking, banking, and other web-based applications that deal with sensitive information, such as passwords, payment information, and financial information, are widespread. Some web developers may have different levels of understanding about securing an online application. The two vulnerabilities identified by the Open Web Application Security Project (OWASP) for its 2017 Top Ten List are SQL injection and Cross-site Scripting (XSS). Because of these two vulnerabilities, an attacker can take advantage of these flaws and launch harmful web-based actions. Many published articles concentrated on a binary classification for these attacks. This article developed a new approach for detecting SQL injection and XSS attacks using deep learning. SQL injection and XSS payloads datasets are combined into a single dataset. The word-embedding technique is utilized to convert the word’s text into a vector. Our model used BiLSTM to auto feature extraction, training, and testing the payloads dataset. BiLSTM classified the payloads into three classes: XSS, SQL injection attacks, and normal. The results showed great results in classifying payloads into three classes: XSS attacks, injection attacks, and non-malicious payloads. BiLSTM showed high performance reached 99.26% in terms of accuracy.

Most famous malware defense tools depend on a large number of detect rules, which are time consuming to develop and require lots of professional experience. Meanwhile, even commercial tools may show high false-negative for some new coming malware, whose patterns were not curved in the prepared rules. This paper proposed the Normalized CNN based Malicious binary Detection method on condition of String, Feature mining (NCMDSF) to address the above problems. Firstly, amount of string feature was extracted from thousands of windows binary applications. Secondly, a 3-layer normalized CNN model, with normalization layer other than down sampling layer, was fit to detect malware. Finally, the proposed method NCMDSF was evaluated to discover malware from more than 1,000 windows binary applications by K-fold cross validation. Experimental results showed that, NCMDSF was superior to some other learning-based methods, including classical CNN, LSTM, normalized LSTM, and won higher true positive rate on the condition of same false positive rate. Furthermore, it successfully avoids over-fitting that occurs in deep learning methods without using normalization.

Due to the continuous improvement of deep learning, saliency object detection based on deep learning has been a hot topic in computational vision. The Fully Convolutional Neural Network (FCNS) has become the mainstream method in salient target measurement. In this article, we propose a new end-to-end multi-level feature fusion module(MCFB), success-fully achieving the goal of extracting rich multi-scale global information by integrating semantic and detailed information. In our module, we obtain different levels of feature maps through convolution, and then cascade the different levels of feature maps, fully considering our global information, and get a rough saliency image. We also propose an optimization module upon our base module to further optimize the feature map. To obtain a clearer boundary, we use a self-defined loss function to optimize the learning process, which includes the Intersection-over-Union (IoU) losses, Binary Cross-Entropy (BCE), and Structural Similarity (SSIM). The module can extract global information to a greater extent while obtaining clearer boundaries. Compared with some existing representative methods, this method has achieved good results.

In this paper, we present a theoretical approach concerning the econometric modelling for the estimation of cyber-security risk, with the use of time-series analysis methods and alternatively with Machine Learning (ML) based, deep learning methodology. Also we present work performed in the framework of SAINT H2020 Project [1], concerning innovative data mining techniques, based on automated web scrapping, for the retrieving of the relevant time-series data. We conclude with a review of emerging challenges in cyber-risk assessment brought by the rapid development of adversarial AI.

Artificial intelligence (AI) technologies have given the cyber security industry a huge leverage with the possibility of having significantly autonomous models that can detect and prevent cyberattacks – even though there still exist some degree of human interventions. AI technologies have been utilized in gathering data which can then be processed into information that are valuable in the prevention of cyberattacks. These AI-based cybersecurity frameworks have commendable scalability about them and are able to detect malicious activities within the cyberspace in a prompter and more efficient manner than conventional security architectures. However, our one or two completed studies did not provide a complete and clear analyses to apply different machine learning algorithms on different media systems. Because of the existing methods of attack and the dynamic nature of malware or other unwanted software (adware etc.) it is important to automatically and systematically create, update and approve malicious packages that can be available to the public. Some of Complex tests have shown that DNN performs maybe can better than conventional machine learning classification. Finally, we present a multiple, large and hybrid DNN torrent structure called Scale-Hybrid-IDS-AlertNet, which can be used to effectively monitor to detect and review the impact of network traffic and host-level events to warn directly or indirectly about cyber-attacks. Besides this, they are also highly adaptable and flexible, with commensurate efficiency and accuracy when it comes to the detection and prevention of cyberattacks.There has been a multiplicity of AI-based cyber security architectures in recent years, and each of these has been found to show varying degree of effectiveness. Deep Neural Networks, which tend to be more complex and even more efficient, have been the major focus of research studies in recent times. In light of the foregoing, the objective of this paper is to discuss the use of AI methods in fighting cyberattacks like malware and DDoS attacks, with attention on DNN-based models.

Deep learning (DL) is an emerging technology that is being used in many areas due to its effectiveness. One of its major applications is attack detection and prevention of backdoor attacks. Sampling-based measurement approaches in the software-defined network of an Internet of Things (IoT) network often result in low accuracy, high overhead, higher memory consumption, and low attack detection. This study aims to review and analyse papers on DL-based network prediction techniques against the problem of Distributed Denial of service attack (DDoS) in a secure software network. Techniques and approaches have been studied, that can effectively predict network traffic and detect DDoS attacks. Based on this review, major components are identified in each work from which an overall system architecture is suggested showing the basic processes needed. Major findings are that the DL is effective against DDoS attacks more than other state of the art approaches.

In recent years, Deep Learning (DL) has been utilized for cyber-attack detection mechanisms as it offers highly accurate detection and is able to overcome the limitations of standard machine learning techniques. When applied in a Software-Defined Network (SDN) environment, a DL-based detection mechanism shows satisfying detection performance. However, in the case of adversarial attacks, the detection performance deteriorates. Therefore, in this paper, first, we outline a highly accurate flooding DDoS attack detection framework based on DL for SDN environments. Second, we investigate the performance degradation of our detection framework when being tested with two adversary traffic datasets. Finally, we evaluate three adversarial training procedures for improving the detection performance of our framework concerning adversarial attacks. It is shown that the application of one of the adversarial training procedures can avoid detection performance degradation and thus might be used in a real-time detection system based on continual learning.

Easy Data Augmentation is originally developed for text classification tasks. It consists of four basic methods: Synonym Replacement, Random Insertion, Random Deletion, and Random Swap. They yield accuracy improvements on several deep neural network models. In this study we apply these methods to a new domain. We augment Named Entity Recognition datasets from medical domain. Although the augmentation task is much more difficult due to the nature of named entities which consist of word or word groups in the sentences, we show that we can improve the named entity recognition performance.

Maintenance of Crop health is essential for the successful farming for both yield and product quality. Pest and disease in crops are serious problem to be monitored. pest and disease occur in different stages or phases of crop development. Due to introduction of genetically modified seeds the natural resistance of crops to prevent them from pest and disease is less. Major crop loss is due to pest and disease attack in crops. It damages the leaves, buds, flowers and fruits of the crops. Affected areas and damage levels of pest and diseases attacks are growing rapidly based on global climate change. Weather Conditions plays a major role in pest and disease attacks in crops. Naked eye inspection of pest and disease is complex and difficult for wide range of field. And at the same time taking lab samples to detect disease is also inefficient and time-consuming process. Early identification of diseases is important to take necessary actions for preventing crop loss and to avoid disease spreads. So, Timely and effective monitoring of crop health is important. Several technologies have been developed to detect pest and disease in crops. In this paper we discuss the various technologies implemented by using AI and Deep Learning for pest and disease detection. And also, briefly discusses their Advantages and limitations on using certain technology for monitoring of crops.

The continuous increase in sophistication of threat actors over the years has made the use of actionable threat intelligence a critical part of the defence against them. Such Cyber Threat Intelligence is published daily on several online sources, including vulnerability databases, CERT feeds, and social media, as well as on forums and web pages from the Surface and the Dark Web. Named Entity Recognition (NER) techniques can be used to extract the aforementioned information in an actionable form from such sources. In this paper we investigate how the latest advances in the NER domain, and in particular transformer-based models, can facilitate this process. To this end, the dataset for NER in Threat Intelligence (DNRTI) containing more than 300 pieces of threat intelligence reports from open source threat intelligence websites is used. Our experimental results demonstrate that transformer-based techniques are very effective in extracting cybersecurity-related named entities, by considerably outperforming the previous state- of-the-art approaches tested with DNRTI.

Network traffic detection is closely related to network security, and it is also a hot research topic now. With the development of encryption technology, traffic detection has become more and more difficult, and many crimes have occurred on the dark web, so how to detect dark web traffic is the subject of this study. In this paper, we proposed a dark web traffic(Tor traffic) detection scheme based on deep learning and conducted experiments on public data sets. By analyzing the results of the experiment, our detection precision rate reached 95.47%.

Software-defined networking (SDN) nowadays is extensively being used in a variety of practical settings, provides a new way to manage networks by separating the data plane from its control plane. However, SDN is particularly vulnerable to Distributed Denial of Service (DDoS) attacks because of its centralized control logic. Many studies have been proposed to tackle DDoS attacks in an SDN design using machine-learning-based schemes; however, these feature-based detection schemes are highly resource-intensive and they are unable to perform reliably in such a large-scale SDN network where a massive amount of traffic data is generated from both control and data planes. This can deplete computing resources, degrade network performance, or even shut down the network systems owing to being exhausting resources. To address the above challenges, this paper proposes a big data framework to overcome traditional data processing limitations and to exploit distributed resources effectively for the most compute-intensive tasks such as DDoS attack detection using machine learning techniques, etc. We demonstrate the robustness, scalability, and effectiveness of our framework through practical experiments.

Artificial intelligence technology is becoming more active in all areas of our lives day by day. This technology affects our daily life by more developing in areas such as industry 4.0, security and education. Deep reinforcement learning is one of the most developed algorithms in the field of artificial intelligence. In this study, it is aimed that three different robots in a limited area learn to move without hitting each other, fixed obstacles and the boundaries of the field. These robots have been trained using the deep reinforcement learning approach and Proximal policy optimization (PPO) policy. Instead of uses value-based methods with the discrete action space, PPO that can easily manipulate the continuous action field and successfully determine the action of the robots has been proposed. PPO policy achieves successful results in multi-agent problems, especially with the use of the Actor-Critic network. In addition, information is given about environment control and learning approaches for swarm behavior. We propose parameter sharing and behavior-based method for this study. Finally, trained model is recorded and tested in 9 different environments where the obstacles are located differently. With our method, robots can perform their tasks in closed environments in the real world without damaging anyone or anything.

The power grid is considered to be the most critical piece of infrastructure in the United States because each of the other fifteen critical infrastructures, as defined by the Cyberse-curity and Infrastructure Security Agency (CISA), require the energy sector to properly function. Due the critical nature of the power grid, the ability to detect anomalies in the power grid is of critical importance to prevent power outages, avoid damage to sensitive equipment and to maintain a working power grid. Over the past few decades, the modern power grid has evolved into a large Cyber Physical System (CPS) equipped with wide area monitoring systems (WAMS) and distributed control. As smart technology advances, the power grid continues to be upgraded with high fidelity sensors and measurement devices, such as phasor measurement units (PMUs), that can report the state of the system with a high temporal resolution. However, this influx of data can often become overwhelming to the legacy Supervisory Control and Data Acquisition (SCADA) system, as well as, the power system operator. In this paper, we propose using a deep learning (DL) convolutional neural network (CNN) as a module within the Automatic Network Guardian for ELectrical systems (ANGEL) Digital Twin environment to detect physical faults in a power system. The presented approach uses high fidelity measurement data from the IEEE 9-bus and IEEE 39-bus benchmark power systems to not only detect if there is a fault in the power system but also applies the algorithm to classify which bus contains the fault.

In federated learning, machine learning and deep learning models are trained globally on distributed devices. The state-of-the-art privacy-preserving technique in the context of federated learning is user-level differential privacy. However, such a mechanism is vulnerable to some specific model poisoning attacks such as Sybil attacks. A malicious adversary could create multiple fake clients or collude compromised devices in Sybil attacks to mount direct model updates manipulation. Recent works on novel defense against model poisoning attacks are difficult to detect Sybil attacks when differential privacy is utilized, as it masks clients' model updates with perturbation. In this work, we implement the first Sybil attacks on differential privacy based federated learning architectures and show their impacts on model convergence. We randomly compromise some clients by manipulating different noise levels reflected by the local privacy budget ε of differential privacy with Laplace mechanism on the local model updates of these Sybil clients. As a result, the global model convergence rates decrease or even leads to divergence. We apply our attacks to two recent aggregation defense mechanisms, called Krum and Trimmed Mean. Our evaluation results on the MNIST and CIFAR-10 datasets show that our attacks effectively slow down the convergence of the global models. We then propose a method to keep monitoring the average loss of all participants in each round for convergence anomaly detection and defend our Sybil attacks based on the training loss reported from randomly selected sets of clients as the judging panels. Our empirical study demonstrates that our defense effectively mitigates the impact of our Sybil attacks.

Mobile Social Networks have become one of the most convenient services for users to share information everywhere. This crowdsourced information is often meaningful and recommended to users, e.g., reviews on Yelp or high marks on Dianping, which poses the threat of Sybil attacks. To address the problem of Sybil attacks, previous solutions mostly use indirect/direct graph model or clickstream model to detect fake accounts. However, they are either dependent on strong connections or solely preserved by servers of social networks. In this paper, we propose a novel predictable approach by exploiting users' custom patterns to distinguish Sybil attackers from normal users for the application of recommendation in mobile social networks. First, we introduce the entropy of spatial-temporal features to profile the mobility traces of normal users, which is quite different from Sybil attackers. Second, we develop discriminative entropy-based features, i.e., users' preference features, to measure the uncertainty of users' behaviors. Third, we design a smart Sybil detection model based on a binary classification approach by combining our entropy-based features with traditional behavior-based features. Finally, we examine our model and carry out extensive experiments on a real-world dataset from Dianping. Our results have demonstrated that the model can significantly improve the detection accuracy of Sybil attacks.

The integration of IDS and Internet of Things (IoT) with deep learning plays a significant role in safety. Security has a strong role to play. Application of the IoT network decreases the time complexity and resources. In the traditional intrusion detection systems (IDS), this research work implements the cutting-edge methodologies in the IoT environment. This research is based on analysis, conception, testing and execution. Detection of intrusions can be performed by using the advanced deep learning system and multiagent. The NSL-KDD dataset is used to test the IoT system. The IoT system is used to test the IoT system. In order to detect attacks from intruders of transport layer, efficiency result rely on advanced deep learning idea. In order to increase the system performance, multi -agent algorithms could be employed to train communications agencies and to optimize the feedback training process. Advanced deep learning techniques such as CNN will be researched to boost system performance. The testing part an IoT includes data simulator which will be used to generate in continuous of research work finding with deep learning algorithms of suitable IDS in IoT network environment of current scenario without time complexity.

Social conflicts appearing in the media are increasing public awareness about security issues, resulting in a higher demand of more exhaustive environment monitoring methods. Automatic video surveillance systems are a powerful assistance to public and private security agents. Since the arrival of deep learning, object detection and classification systems have experienced a large improvement in both accuracy and versatility. However, deep learning-based object detection and classification systems often require expensive GPU-based hardware to work properly. This paper presents a novel deep learning-based foreground anomalous object detection system for video streams supplied by panoramic cameras, specially designed to build power efficient video surveillance systems. The system optimises the process of searching for anomalous objects through a new potential detection generator managed by three different multivariant homoscedastic distributions. Experimental results obtained after its deployment in a Jetson TX2 board attest the good performance of the system, postulating it as a solvent approach to power saving video surveillance systems.