With deep neural networks (DNNs) involved in more and more decision making processes, critical security problems can occur when DNNs give wrong predictions. This can be enforced with so-called adversarial attacks. These attacks modify the input in such a way that they are able to fool a neural network into a false classification, while the changes remain imperceptible to a human observer. Even for very specialized AI systems, adversarial attacks are still hardly detectable. The current state-of-the-art adversarial defenses can be classified into two categories: pro-active defense and passive defense, both unsuitable for quick rectifications: Pro-active defense methods aim to correct the input data to classify the adversarial samples correctly, while reducing the accuracy of ordinary samples. Passive defense methods, on the other hand, aim to filter out and discard the adversarial samples. Neither of the defense mechanisms is suitable for the setup of autonomous driving: when an input has to be classified, we can neither discard the input nor have the time to go for computationally expensive corrections. This motivates our method based on explainable artificial intelligence (XAI) for the correction of adversarial samples. We used two XAI interpretation methods to correct adversarial samples. We experimentally compared this approach with baseline methods. Our analysis shows that our proposed method outperforms the state-of-the-art approaches.

Information security construction is a social issue, and the most urgent task is to do an excellent job in information risk assessment. The bayesian neural network currently plays a vital role in enterprise information security risk assessment, which overcomes the subjective defects of traditional assessment results and operates efficiently. The risk quantification method based on fuzzy theory and Bayesian regularization BP neural network mainly uses fuzzy theory to process the original data and uses the processed data as the input value of the neural network, which can effectively reduce the ambiguity of language description. At the same time, special neural network training is carried out for the confusion that the neural network is easy to fall into the optimal local problem. Finally, the risk is verified and quantified through experimental simulation. This paper mainly discusses the problem of enterprise information security risk assessment based on a Bayesian neural network, hoping to provide strong technical support for enterprises and organizations to carry out risk rectification plans. Therefore, the above method provides a new information security risk assessment idea.

Cyber threats have been a major issue in the cyber security domain. Every hacker follows a series of cyber-attack stages known as cyber kill chain stages. Each stage has its norms and limitations to be deployed. For a decade, researchers have focused on detecting these attacks. Merely watcher tools are not optimal solutions anymore. Everything is becoming autonomous in the computer science field. This leads to the idea of an Autonomous Cyber Resilience Defense algorithm design in this work. Resilience has two aspects: Response and Recovery. Response requires some actions to be performed to mitigate attacks. Recovery is patching the flawed code or back door vulnerability. Both aspects were performed by human assistance in the cybersecurity defense field. This work aims to develop an algorithm based on Reinforcement Learning (RL) with a Convoluted Neural Network (CNN), far nearer to the human learning process for malware images. RL learns through a reward mechanism against every performed attack. Every action has some kind of output that can be classified into positive or negative rewards. To enhance its thinking process Markov Decision Process (MDP) will be mitigated with this RL approach. RL impact and induction measures for malware images were measured and performed to get optimal results. Based on the Malimg Image malware, dataset successful automation actions are received. The proposed work has shown 98\% accuracy in the classification, detection, and autonomous resilience actions deployment.

Frequency hopping (FH) technology is one of the most effective technologies in the field of radio countermeasures, meanwhile, the recognition of FH signal has become a research hotspot. FH signal is a typical non-stationary signal whose frequency varies nonlinearly with time and the time-frequency analysis technique provides a very effective method for processing this kind of signal. With the renaissance of deep learning, methods based on time-frequency analysis and deep learning are widely studied. Although these methods have achieved good results, the recognition accuracy still needs to be improved. Through the observation of the datasets, we found that there are still difficult samples that are difficult to identify. Through further analysis, we propose a horizontal spatial attention (HSA) block, which can generate spatial weight vector according to the signal distribution, and then readjust the feature map. The HSA block is a plug-and-play module that can be integrated into common convolutional neural network (CNN) to further improve their performance and these networks with HSA block are collectively called HANets. The HSA block also has the advantages of high recognition accuracy (especially under low SNRs), easy to implant, and almost no influence on the number of parameters. We verified our method on two datasets and a series of comparative experiments show that the proposed method achieves good results on FH datasets.

Advanced persistent threat (APT) attacks have caused severe damage to many core information infrastructures. To tackle this issue, the graph-based methods have been proposed due to their ability for learning complex interaction patterns of network entities with discrete graph snapshots. However, such methods are challenged by the computer networking model characterized by a natural continuous-time dynamic heterogeneous graph. In this paper, we propose a heterogeneous graph neural network based APT detection method in smart grid clouds. Our model is an encoderdecoder structure. The encoder uses heterogeneous temporal memory and attention embedding modules to capture contextual information of interactions of network entities from the time and spatial dimensions respectively. We implement a prototype and conduct extensive experiments on real-world cyber-security datasets with more than 10 million records. Experimental results show that our method can achieve superior detection performance than state-of-the-art methods.

Understanding dynamic human behavior based on online video has many applications in security control, crime surveillance, sports, and industrial IoT systems. This paper solves the problem of classifying video data recorded on surveillance cameras in order to identify fragments with instances of shoplifting. It is proposed to use a classifier that is a symbiosis of two neural networks: convolutional and recurrent. The convolutional neural network is used for extraction of features from each frame of the video fragment, and the recurrent network for processing the temporal sequence of processed frames and subsequent classification.

Wearables Security 2022 - Mobile devices such as smartphones are increasingly being used to record personal, delicate, and security information such as images, emails, and payment information due to the growth of wearable computing. It is becoming more vital to employ smartphone sensor-based identification to safeguard this kind of information from unwanted parties. In this study, we propose a sensor-based user identification approach based on individual walking patterns and use the sensors that are pervasively embedded into smartphones to accomplish this. Individuals were identified using a convolutional neural network (CNN). Four data augmentation methods were utilized to produce synthetically more data. These approaches included jittering, scaling, and time-warping. We evaluate the proposed identification model’s accuracy, precision, recall, F1-score, FAR, and FRR utilizing a publicly accessible dataset named the UIWADS dataset. As shown by the experiment findings, the CNN with the timewarping approach operates with very high accuracy in user identification, with the lowest false positive rate of 8.80\% and the most incredible accuracy of 92.7\%.

Wearables Security 2022 - One of the biggest new trends in artificial intelligence is the ability to recognise people s movements and take their actions into account. It can be used in a variety of ways, including for surveillance, security, human-computer interaction, and content-based video retrieval. There have been a number of researchers that have presented vision-based techniques to human activity recognition. Several challenges need to be addressed in the creation of a vision-based human activity recognition system, including illumination variations in human activity recognition, interclass similarity between scenes, the environment and recording setting, and temporal variation. To overcome the above mentioned problem, by capturing or sensing human actions with help of wearable sensors, wearable devices, or IoT devices. Sensor data, particularly one-dimensional time series data, are used in the work of human activity recognition. Using 1D-Convolutional Neural Network (CNN) models, this works aims to propose a new approach for identifying human activities. The Wireless Sensor Data Mining (WISDM) dataset is utilised to train and test the 1D-CNN model in this dissertation. The proposed HAR-CNN model has a 95.2\%of accuracy, which is far higher than that of conventional methods.

Privacy Policies - Companies and organizations inform users of how they handle personal data through privacy policies on their websites. Particular information, such as the purposes of collecting personal data and what data are provided to third parties is required to be disclosed by laws and regulations. An example of such a law is the Act on the Protection of Personal Information in Japan. In addition to privacy policies, an increasing number of companies are publishing security policies to express compliance and transparency of corporate behavior. However, it is challenging to update these policies against legal requirements due to the periodic law revisions and rapid business changes. In this study, we developed a method for analyzing privacy policies to check whether companies comply with legal requirements. In particular, the proposed method classifies policy contents using a convolutional neural network and evaluates privacy compliance by comparing the classification results with legal requirements. In addition, we analyzed security policies using the proposed method, to confirm whether the combination of privacy and security policies contributes to privacy compliance. In this study, we collected and evaluated 1,304 privacy policies and 140 security policies for Japanese companies. The results revealed that over 90\% of privacy policies sufficiently describe the handling of personal information by first parties, user rights, and security measures, and over 90\% insufficiently describe the data retention and specific audience. These differences in the number of descriptions are dependent on industry guidelines and business characteristics. Moreover, security policies were found to improve the compliance rates of 46 out of 140 companies by describing security practices not included in privacy policies.

Neural Style Transfer - With the development of economical society, the problem of product piracy security is becoming more and more serious. In order to protect the copyright of brands, based on the image neural style transfer, this paper proposes an automatic generation algorithm of anti-counterfeiting logo with security shading, which increases the difficulty of illegal copying and packaging production. VGG19 deep neural network is used to extract image features and calculate content response loss and style response loss. Based on the original neural style transfer algorithm, the content loss is added, and the generated security shading is fused with the original binary logo image to generate the anti-counterfeiting logo image with higher recognition rate. In this paper, the global loss function is composed of content loss, content response loss and style response loss. The L-BFGS optimization algorithm is used to iteratively reduce the global loss function, and the relationship between the weight adjustment, the number of iterations and the generated anti-counterfeiting logo among the three losses is studied. The secret keeping of shading style image used in this method increases the anti-attack ability of the algorithm. The experimental results show that, compared with the original logo, this method can generate the distinguishable logo content, complex security shading, and has convergence and withstand the attacks.

Neural Network Security - Trust is an essential concept in ad hoc network security. Creating and maintaining trusted relationships between nodes is a challenging task. This paper proposes a decentralized method for evaluating trust in ad hoc networks. The method uses neural networks and local information to predict the trust of neighboring nodes. The method was compared with the original centralized version, showing that even without global information knowledge, the method has, on average, 97\% accuracy in classification and 94\% in regression problem. An important contribution of this paper is overcoming the main limitation of the original method, which is the centralized evaluation of trust. Moreover, the decentralized method output is a perfect fit to use as an input to enhance routing in ad hoc networks.

Neural Network Security - Software-Defined Network (SDN) is a new networking paradigm that adopts centralized control logic and provides more control to the network operators over the network infrastructure to meet future network requirements. SDN controller known as operation system, which is responsible for running network applications and maintaining the different network services and functionalities. Despite all its great capabilities, SDN is facing different security threats due to its various architectural entities and centralized nature. Distributed Denial of Service (DDoS) is a promptly growing attack and becomes a major threat for the SDN. To date, most of the studies focus on detecting high-rate DDoS attacks at the control layer of SDN and low-rate DDoS attacks are high concealed because they are difficult to detect. Furthermore, the existing methods are useful for the detection of high-rate DDoS, so need to focus on low-rate DDoS attacks separately. Hence, the use of machine learning algorithms is growing for the detection of low-rate DDoS attacks in the SDN, but they achieved low accuracy against this attack. To improve the detection accuracy, this paper first describes the attack s mechanism and then proposes a Recurrent Neural Network (RNN) based method. The extracted features from the flow rules are used by the RNN for the detection of low-rate attacks. The experimental results show that the proposed method intelligently detects the attack, and its detection accuracy reaches 98.59\%. The proposed method achieves good detection accuracy as compared to existing studies.

Neural Network Security - Aiming at the network security problem caused by the rapid development of network, this paper uses a network traffic anomaly detection method of industrial control system based on convolutional neural network. In the traditional machine learning algorithm, the processing of features has a high impact on the performance of the model, and the model is highly dependent on features. This method uses the characteristics of convolutional neural network to autonomously learn features, which avoids this problem. In order to verify the superiority of the model, this paper takes accuracy as the evaluation index, and compares it with the traditional machine learning algorithm. The results show that the overall accuracy of the method is 99.88 \%, which has higher accuracy than traditional machine learning algorithms such as decision tree algorithm (ID3), adaptive boosting tree (Adboost) and naive Bayesian model. Therefore, this method can be better applied to the anomaly detection of network traffic in industrial control system, and has practical application value.

Neural Network Security - With the development of computer and network technology, industrial control systems are connecting with the Internet and other public networks in various ways, viruses, trojans and other threats are spreading to industrial control systems, industrial control system information security issues are becoming increasingly prominent. Under this background, it is necessary to construct the network security evaluation model of industrial control system based on the safety evaluation criteria and methods, and complete the safety evaluation of the industrial control system network according to the design scheme. Based on back propagation (BP) neural network’s evaluation of the network security status of industrial control system, this paper determines the number of neurons in BP neural network input layer, hidden layer and output layer by analyzing the actual demand, empirical equation calculation and experimental comparison, and designs the network security evaluation index system of industrial control system according to factors affecting industrial control safety, and constructs a safety rating table. Finally, by comparing the performance of BP neural network and multilinear regression to the evaluation of the network security status of industrial control system through experimental simulation, it can be found that BP neural network has higher accuracy for the evaluation of network security status of industrial control system.

Neural Network Security - With the continuous development of network technology and the continuous expansion of network scale, the security of the network has suffered more threats, and the attacks faced are becoming more and more extensive. The frequent occurrence of network security incidents has caused huge losses, facing more and more severe situation, it is necessary to adopt various network security technologies to solve the problem. In network security, the most commonly used technology is firewall. The firewall has a certain blocking effect on attacks from outside the network, but it has a weak defense effect on the attacks in the internal network, and it is easy to be bypassed. Intrusion detection technology can detect both internal and external network attacks. Responses are generated before the intrusion behavior occurs, and alarm information is issued for timely and effective processing. In recent years, China s campus security incidents are still happening, seriously threatening the lives of students and disrupting the normal teaching order of schools. At present, there are still many loopholes in campus security operations. Campus security management system has become an important task in campus security construction. On this basis, relevant personnel are required to analyze the existing problems of campus safety and the needs of the safety management system, and find the main technology of a more advanced intelligent safety management system.

Neural Network Security - With the rapid development of computer networks and information technology today, people are more inclined to use network systems to achieve various data exchanges. Alibaba, Tencent and other companies virtual payment has become the mainstream payment method. Due to the globalization and openness of the network, anyone can freely enter and exit, which brings huge hidden dangers to NS(network security). NS has become an important issue that we have to face. Once important information is stolen, it is likely to cause very large losses to individuals and even the society. This article mainly studies the computer NS encryption technology of neural network. First of all, the current situation of computer NS is comprehensively reflected from the two aspects of domestic Internet users and NS penetration rate in recent years. By 2020, the number of Chinese residents using the Internet has reached 1.034 billion, and 77.3\% of Internet users are generally aware of NS. Secondly, it analyzes the effect of NN(neural network) on computer NS encryption technology. The results show that the use of NN in computer encryption technology not only helps to improve security and convenience, but also prevents the secondary transmission of data and prevents related information leakage.

Neural Network Security - With the development of computing technology, data security and privacy protection have also become the focus of researchers; along with this comes the issue of network link security and reliability, and these issues have become the focus of discussion when studying network security. Intrusion detection is an effective means to assist in network malicious traffic detection and maintain network stability; to meet the ever-changing demand for network traffic identification, intrusion detection models have undergone a transformation from traditional intrusion detection models to machine learning intrusion detection models to deep intrusion detection models. The efficiency and superiority of deep learning have been proven in fields such as image processing, but there are still some problems in the field of network security intrusion detection: the models are not targeted when processing data, the models have poor generalization ability, etc. The combinatorial neural network proposed in this paper can effectively propose a solution to the problems of existing models, and the CL-IDS model proposed in this paper has a better performance on the KDDCUP99 dataset as demonstrated by relevant experiments.

Neural Network Resiliency - Over the past few years, deep neural networks (DNNs) have been used to solve a wide range of real-life problems. However, DNNs are vulnerable to adversarial attacks where carefully crafted input perturbations can mislead a well-trained DNN to produce false results. As DNNs are being deployed into security-sensitive applications such as autonomous driving, adversarial attacks may lead to catastrophic consequences.

Neural Network Resiliency - The globalization of the Integrated Circuit (IC) market is attracting an ever-growing number of partners, while remarkably lengthening the supply chain. Thereby, security concerns, such as those imposed by functional Reverse Engineering (RE), have become quintessential. RE leads to disclosure of confidential information to competitors, potentially enabling the theft of intellectual property. Traditional functional RE methods analyze a given gate-level netlist through employing pattern matching towards reconstructing the underlying basic blocks, and hence, reverse engineer the circuit’s function.

Network Security Architecture - Software-Defined Networking or SDN (Software-Defined Networking) is a technology for software control and management of the network in order to improve its properties. Unlike classic network management technologies, which are complex and decentralized, SDN technology is a much more flexible and simple system. The new architecture may be vulnerable to several attacks leading to resource depletion and preventing the SDN controller from providing support to legitimate users. One such attack is the Distributed Denial of Service (DDoS), which is on the rise today. We suggest Modified-DDoSNet, a system for detecting DDoS attacks in the SDN environment. A model based on Deep Learning (DL) techniques will be implemented, combining a Recurrent Neural Network (RNN) with an Autoencoder. The proposed model, which was first trained to detect attacks, was implemented in the security architecture of the SDN network, as a new component. The security architecture of the SDN network contains a total of 13 components, each of which represents an individual part of the architecture, where the first component is the RNN - autoencoder. The model itself, which is the first component, was trained in the CICDDoS2019 dataset. It has high reliability for attack detection, which increases the security of the SDN network architecture.

Network Control Systems Security - The huge advantages of cloud computing technology and the bottlenecks in the development of traditional network control systems have prompted the birth of cloud control systems to address the shortcomings of traditional network control systems in terms of bandwidth and performance. However, the information security issues faced by cloud control systems are more complex, and distributed denial-of-service (DDoS) attacks are a typical class of attacks that may lead to problems such as latency in cloud control systems and seriously affect the performance of cloud control systems. In this paper, we build a single-capacity water tank cloud control semi-physical simulation system with heterogeneous controllers and propose a DDoS attack detection method for cloud control systems based on bidirectional long short-term memory neural network (BiLSTM), study the impact of DDoS attacks on cloud control systems. The experimental results show that the BiLSTM algorithm can effectively detect the DDoS attack on the cloud control system.

Network Control Systems Security - With the development of computer and network technology, industrial control systems are connecting with the Internet and other public networks in various ways, viruses, trojans and other threats are spreading to industrial control systems, industrial control system information security issues are becoming increasingly prominent. Under this background, it is necessary to construct the network security evaluation model of industrial control system based on the safety evaluation criteria and methods, and complete the safety evaluation of the industrial control system network according to the design scheme. Based on back propagation (BP) neural network’s evaluation of the network security status of industrial control system, this paper determines the number of neurons in BP neural network input layer, hidden layer and output layer by analyzing the actual demand, empirical equation calculation and experimental comparison, and designs the network security evaluation index system of industrial control system according to factors affecting industrial control safety, and constructs a safety rating table. Finally, by comparing the performance of BP neural network and multilinear regression to the evaluation of the network security status of industrial control system through experimental simulation, it can be found that BP neural network has higher accuracy for the evaluation of network security status of industrial control system.

Network Intrusion Detection - Network intrusion detection technology has been a popular application technology for current network security, but the existing network intrusion detection technology in the application process, there are problems such as low detection efficiency, low detection accuracy and other poor detection performance. To solve the above problems, a new treatment combining artificial intelligence with network intrusion detection is proposed. Artificial intelligence-based network intrusion detection technology refers to the application of artificial intelligence techniques, such as: neural networks, neural algorithms, etc., to network intrusion detection, and the application of these artificial intelligence techniques makes the automatic detection of network intrusion detection models possible.

Network Intrusion Detection - With the development of computing technology, data security and privacy protection have also become the focus of researchers; along with this comes the issue of network link security and reliability, and these issues have become the focus of discussion when studying network security. Intrusion detection is an effective means to assist in network malicious traffic detection and maintain network stability; to meet the ever-changing demand for network traffic identification, intrusion detection models have undergone a transformation from traditional intrusion detection models to machine learning intrusion detection models to deep intrusion detection models. The efficiency and superiority of deep learning have been proven in fields such as image processing, but there are still some problems in the field of network security intrusion detection: the models are not targeted when processing data, the models have poor generalization ability, etc. The combinatorial neural network proposed in this paper can effectively propose a solution to the problems of existing models, and the CL-IDS model proposed in this paper has a better performance on the KDDCUP99 dataset as demonstrated by relevant experiments.

Network Coding - Precise binary code vulnerability detection is a significant research topic in software security. Currently, the majority of software is released in binary form, and the corresponding vulnerability detection approaches for binary code are desired. Existing deep learning-based detection techniques can only detect binary code vulnerabilities but cannot precisely identify the types of vulnerabilities. This paper proposes a Binary code-based Hybrid neural network for Multiclass Vulnerability Detection, dubbed BHMVD. BHMVD generates binary slices according to the control dependence and data dependence of library/API function calls, and then extracts syntax features from binary slices to generate type slices, which can help identify vulnerability types. This paper uses a hybrid neural network of CNN-BLSTM to extract vulnerability features from binary and type slices. The former extracts local features, while the latter extracts global features. Experiment results on 19 types of vulnerabilities show that BHMVD is effective for binary code-based multiclass vulnerability detection, and using a hybrid neural network can improve detection ability.