Biblio

Rational and smart decision making by means of strategic interaction and mathematical modelling is the key aspect of Game theory. Security games based on game theory are used extensively in cyberspace for various levels of security. The contemporary security issues can be modelled and analyzed using game theory as a robust mathematical framework. The attackers, defenders and the adversarial as well as defensive interactions can be captured using game theory. The security games equilibrium evaluation can help understand the attackers' strategies and potential threats at a deeper level for efficient defense. Wireless sensor network (WSN) designs are greatly benefitted by game theory. A deep learning adversarial network algorithm is used in combination with game theory enabling energy efficiency, optimal data delivery and security in a WSN. The trade-off between energy resource utilization and security is balanced using this technique.

The success of deep learning based steganography has shifted focus of researchers from traditional steganography approaches to deep learning based steganography. Various deep steganographic models have been developed for improved security, capacity and invisibility. In this work a multi-data deep learning steganography model has been developed using a well known deep learning model called Generative Adversarial Networks (GAN) more specifically using deep convolutional Generative Adversarial Networks (DCGAN). The model is capable of hiding two different messages, meant for two different receivers, inside a single cover image. The proposed model consists of four networks namely Generator, Steganalyzer Extractor1 and Extractor2 network. The Generator hides two secret messages inside one cover image which are extracted using two different extractors. The Steganalyzer network differentiates between the cover and stego images generated by the generator network. The experiment has been carried out on CelebA dataset. Two commonly used distortion metrics Peak signal-to-Noise ratio (PSNR) and Structural Similarity Index Metric (SSIM) are used for measuring the distortion in the stego image The results of experimentation show that the stego images generated have good imperceptibility and high extraction rates.

Existing anomaly detection models show success in detecting abnormal images with generative adversarial networks on the insufficient annotation of anomalous samples. However, existing models cannot accurately identify the anomaly samples which are close to the normal samples. We assume that the main reason is that these methods ignore the diversity of patterns in normal samples. To alleviate the above issue, this paper proposes a novel anomaly detection framework based on generative adversarial network, called ADe-GAN. More concretely, we construct a self-supervised learning task to fully explore the pattern information and latent representations of input images. In model inferring stage, we design a new abnormality score approach by jointly considering the pattern information and reconstruction errors to improve the performance of anomaly detection. Extensive experiments show that the ADe-GAN outperforms the state-of-the-art methods over several real-world datasets.

Generative adversarial networks (GANs) have been increasingly popular among deep learning methods. With many GANs-based models developed since its emergence, among which are conditional generative adversarial networks, progressive growing of generative adversarial networks, Wasserstein generative adversarial networks and so on. These frameworks are currently widely applied in areas such as remote sensing cybersecurity, medical, and architecture. Especially, they have solved problems of cloud removal, semantic segmentation, image-to-image translation and data argumentation in remote sensing. For example, WGANs and ProGANs can be applied in data argumentation, and cGANs can be applied in semantic argumentation and image-to-image translation. This article provides an overview of structures of multiple GANs-based models and what areas they can be applied in remote sensing.

Fuzzing is a popular technique for finding soft-ware vulnerabilities. Despite their success, the state-of-art fuzzers will inevitably produce a large number of low-quality inputs. In recent years, Machine Learning (ML) based selection strategies have reported promising results. However, the existing ML-based fuzzers are limited by the lack of training data. Because the mutation strategy of fuzzing can not effectively generate useful input, it is prohibitively expensive to collect enough inputs to train models. In this paper, propose a generative adversarial networks based solution to generate a large number of inputs to solve the problem of insufficient data. We implement the proposal in the American Fuzzy Lop (AFL), and the experimental results show that it can find more crashes at the same time compared with the original AFL.

Deep learning has been widely applied for jobs involving face inpainting, however, there are usually some problems, such as incoherent inpainting edges, lack of diversity of generated images and other problems. In order to get more feature information and improve the inpainting effect, we therefore propose a Generative Adversarial Network of Skip Connection (SCGAN), which connects the encoder layers and the decoder layers by skip connection in the generator. The coherence and consistency of the image inpainting edges are improved, and the finer features of the image inpainting are refined, simultaneously using the discriminator's local and global double discriminators model. We also employ WGAN-GP loss to enhance model stability during training, prevent model collapse, and increase the variety of inpainting face images. Finally, experiments on the CelebA dataset and the LFW dataset are performed, and the model's performance is assessed using the PSNR and SSIM indices. Our model's face image inpainting is more realistic and coherent than that of other models, and the model training is more reliable.

Speech enhancement is often applied for speech-based systems due to the proneness of speech signals to additive background noise. While speech processing-based methods are traditionally used for speech enhancement, with advancements in deep learning technologies, many efforts have been made to implement them for speech enhancement. Using deep learning, the networks learn mapping functions from noisy data to clean ones and then learn to reconstruct the clean speech signals. As a consequence, deep learning methods can reduce what is so-called musical noise that is often found in traditional speech enhancement methods. Currently, one popular deep learning architecture for speech enhancement is generative adversarial networks (GAN). However, the cross-entropy loss that is employed in GAN often causes the training to be unstable. So, in many implementations of GAN, the cross-entropy loss is replaced with the least-square loss. In this paper, to improve the training stability of GAN using cross-entropy loss, we propose to use deep regret analytic generative adversarial networks (Dragan) for speech enhancements. It is based on applying a gradient penalty on cross-entropy loss. We also employ relativistic rules to stabilize the training of GAN. Then, we applied it to the least square and Dragan losses. Our experiments suggest that the proposed method improve the quality of speech better than the least-square loss on several objective quality metrics.

In recent years, generative adversarial networks (GAN) have become a research hotspot in the field of deep learning. Researchers apply them to the field of anomaly detection and are committed to effectively and accurately identifying abnormal images in practical applications. In anomaly detection, traditional supervised learning algorithms have limitations in training with a large number of known labeled samples. Therefore, the anomaly detection model of unsupervised learning GAN is the research object for discussion and research. Firstly, the basic principles of GAN are introduced. Secondly, several typical GAN-based anomaly detection models are sorted out in detail. Then by comparing the similarities and differences of each derivative model, discuss and summarize their respective advantages, limitations and application scenarios. Finally, the problems and challenges faced by GAN in anomaly detection are discussed, and future research directions are prospected.

As one of the defensive solutions against cyberattacks, an Intrusion Detection System (IDS) plays an important role in observing the network state and alerting suspicious actions that can break down the system. There are many attempts of adopting Machine Learning (ML) in IDS to achieve high performance in intrusion detection. However, all of them necessitate a large amount of labeled data. In addition, labeling attack data is a time-consuming and expensive human-labor operation, it makes existing ML methods difficult to deploy in a new system or yields lower results due to a lack of labels on pre-trained data. To address these issues, we propose a semi-supervised IDS model that leverages Generative Adversarial Networks (GANs) and Adversarial AutoEncoder (AAE), called a semi-supervised adversarial autoencoder (SAAE). Our SAAE experimental results on two public datasets for benchmarking ML-based IDS, including NF-CSE-CIC-IDS2018 and NF-UNSW-NB15, demonstrate the effectiveness of AAE and GAN in case of using only a small number of labeled data. In particular, our approach outperforms other ML methods with the highest detection rates in spite of the scarcity of labeled data for model training, even with only 1% labeled data.

Imbalanced class distribution can cause information loss and missed/false alarms for deep learning and machine-learning algorithms. The detection performance of traditional intrusion detection systems tend to degenerate due to skewed class distribution caused by the uneven allocation of observations in different kinds of attacks. To combat class imbalance and improve network intrusion detection performance, we adopt the conditional generative adversarial network (CTGAN) that enables the generation of samples of specific classes of interest. CTGAN builds on the generative adversarial networks (GAN) architecture to model tabular data and generate high quality synthetic data by conditionally sampling rows from the generated model. Oversampling using CTGAN adds instances to the minority class such that both data in the majority and the minority class are of equal distribution. The generated security alerts are used for training classifiers that realize critical alert detection. The proposed scheme is evaluated on a real-world dataset collected from security operation center of a large enterprise. The experiment results show that detection accuracy can be substantially improved when CTGAN is adopted to produce a balanced security-alert dataset. We believe the proposed CTGAN-based approach can cast new light on building effective systems for critical alert detection with reduced missed/false alarms.

With the progress of cryptography computer science, designing cryptographic algorithms using deep learning is a very innovative research direction. Google Brain designed a communication model using generation adversarial network and explored the encrypted communication algorithm based on machine learning. However, the encrypted communication model it designed lacks quantitative evaluation. When some plaintexts and keys are leaked at the same time, the security of communication cannot be guaranteed. This model is optimized to enhance the security by adjusting the optimizer, modifying the activation function, and increasing batch normalization to improve communication speed of optimization. Experiments were performed on 16 bits and 64 bits plaintexts communication. With plaintext and key leak rate of 0.75, the decryption error rate of the decryptor is 0.01 and the attacker can't guess any valid information about the communication.

In order to better cultivate engineering and application-oriented cryptographic talents, it is urgent to establish a joint school enterprise cryptographic laboratory. However, there is a core problem in the existing school enterprise joint laboratory construction scheme: the enterprise is not specialized and has insufficient cooperation ability, which can not effectively realize the effective integration of resources and mutual benefit and win-win results. To solve this problem, we propose a comprehensive evaluation model of cooperative enterprises based on entropy weight method and grey correlation analysis. Firstly, the multi-level evaluation index system of the enterprise is established, and the entropy weight method is used to objectively weight the index. After that, the grey weighted correlation degree between each enterprise and the virtual optimal enterprise is calculated by grey correlation analysis to compare the advantages and disadvantages of enterprises. Through the example analysis, it is proved that our method is effective and reliable, eliminating subjective factors, and providing a certain reference value for the construction of school enterprise joint cryptographic laboratory.

Transmitting data through the internet may have severe security risks due to illegal access done by attackers. Some methods have been introduced to overcome this issue, such as cryptography and steganography. Nevertheless, some problems still arise, such as the quality of the stego data. Specifically, it happens if the stego is shared with some users. In this research, a shared-secret mechanism is combined with steganography. For this purpose, the fuzzy logic edge detection and Prediction Error (PE) methods are utilized to hide private data. The secret sharing process is carried out after data embedding in the cover image. This sharing mechanism is performed on image pixels that have been converted to PE values. Various Peak Signal to Noise Ratio (PSNR) values are obtained from the experiment. It is found that the number of participants and the threshold do not significantly affect the image quality of the shares.

Cloud computing is preferred because of its numerous improvements, such as data security, low maintenance cost, unlimited storage capacity and consistent backups. However, legitimate users take advantage of cloud storage services for storing a considerable amount of sensitive data. After storing data on the cloud, data users pass on control over data to cloud administrators. Although for assuring data security, sensitive information needs to be encrypted before deploying it on the cloud server. In traditional searchable encryption, encrypted data can be searched using keywords on a cloud server without knowing data details, and users can retrieve certain specific files of interest after authentication. However, the results are only related to the exact matching keyword searches. This drawback affects system usability and efficiency, due to which existing encryption methods are unsuitable in cloud computing. To avoid the above problems, this study includes as follows: Firstly, we analyze all fuzzy keyword search techniques that are wildcard based, gram based and trie-traverse. Secondly, we briefly describe certificateless cryptography and suggest a certificateless searchable encryption scheme. Finally, this study gives easy access to developing a fuzzy keyword searchable system for a new researcher to combine the above two points. It provides easy access and efficient search results.

This article is about a criterion based on credibility complex fuzzy set (CCFS) to study the prevailing substitution boxes (S-box) and learn their properties to find out their suitability in image encryption applications. Also these criterion has its own properties which is discussed in detailed and on the basis of these properties we have to find the best optimal results and decide the suitability of an S-box to image encryption applications. S-box is the only components which produces the confusion in the every block cipher in the formation of image encryption. So, for this first we have to convert the matrix having color image using the nonlinear components and also using the proposed algebraic structure of credibility complex fuzzy set to find the best S-box for image encryption based on its criterion. The analyses show that the readings of GRAY S-box is very good for image data.

The WSN nodes are arranged uniformly or randomly on the area of need for gathering the required data. The admin utilizes wireless broadband networks to connect to the Internet and acquire the required data from the base station (BS). However, these sensor nodes play a significant role in a variety of professional and industrial domains, but some of the concerns stop the growth of WSN, such as memory, transmission, battery power and processing power. The most significant issue with these restrictions is to increase the energy efficiency for WSN with rapid and trustworthy data transfer. In this designed model, the sensor nodes are clustered using the FCM (Fuzzy C-Means) clustering algorithm with the Reptile Search Optimization (RSO) for finding the centre of the cluster. The cluster head is determined by using African vulture optimization (AVO). For selecting the path of data transmission from the cluster head to the base station, the adaptive relay nodes are selected using the Fuzzy rule. These data from the base station are given to the server with a DNA cryptography encryption algorithm for secure data transmission. The performance of the designed model is evaluated with specific parameters such as average residual energy, throughput, end-to-end delay, information loss and execution time for a secure and energy-efficient routing protocol. These evaluated values for the proposed model are 0.91 %, 1.17Mbps, 1.76 ms, 0.14 % and 0.225 s respectively. Thus, the resultant values of the proposed model show that the designed clustering-based routing protocol using FCM-RSOA and DNA cryptography for smart building performs better compared to the existing techniques.

With the development of the digital development transformation of the power grid, the classification of power unstructured encrypted data is an important basis for data security protection. However, most studies focus on exact match classification or single-keyword fuzzy match classification. This paper proposes a fuzzy matching classification method for power unstructured encrypted data. The data owner generates an index vector based on the power unstructured file, and the data user generates a query vector by querying the file through the same process. The index and query vector are uploaded to the cloud server in encrypted form, and the cloud server calculates the relevance score and sorts it, and returns the classification result with the highest score to the user. This method realizes the multi-keyword fuzzy matching classification of unstructured encrypted data of electric power, and through the experimental simulation of a large number of data sets, the effect and feasibility of the method are proved.

Identity based digital signature is an important research topic of public key cryptography, which can effectively guarantee the authentication, integrity and unforgeability of data. In this paper, a new fuzzy multi-identity based signature scheme is proposed. It is proved that the scheme is existentially unforgeable against adaptively chosen message attack, and the security of the signature scheme can be reduced to CDH assumption. The storage cost and the communication overhead are small, therefore the new fuzzy multi-identity based signature (FMIBS) scheme can be implemented efficiently.

The security and privacy of the network in Internet of Things (IoT) systems are becoming more critical as we are more dependent on smart systems. Considering that packets are exchanged between the end user and the sensing devices, it is then important to ensure the security, privacy, and integrity of the transmitted data by designing a secure and a lightweight authentication protocol for IoT systems. In this article, in order to improve the authentication and the encryption in IoT systems, we present a novel method of authentication and encryption based on elliptic curve cryptography (ECC) using random numbers generated by fuzzy logic. We evaluate our novel key generation method by using standard randomness tests, such as: frequency test, frequency test with mono block, run test, discrete Fourier transform (DFT) test, and advanced DFT test. Our results show superior performance compared to existing ECC based on shift registers. In addition, we apply some attack algorithms, such as Pollard’s \textbackslashrho and Baby-step Giant-step, to evaluate the vulnerability of the proposed scheme.

In recent years, data security incidents caused by insider threats in distributed file systems have attracted the attention of academia and industry. The most common way to detect insider threats is based on user profiles. Through analysis, we realize that based on existing user profiles are not efficient enough, and there are many false positives when a stable user profile has not yet been formed. In this work, we propose personalized user profiles and design an insider threat detection framework, which can intelligently detect insider threats for securing distributed file systems in real-time. To generate personalized user profiles, we come up with a time window-based clustering algorithm and a weighted kernel density estimation algorithm. Compared with non-personalized user profiles, both the Recall and Precision of insider threat detection based on personalized user profiles have been improved, resulting in their harmonic mean F1 increased to 96.52%. Meanwhile, to reduce the false positives of insider threat detection, we put forward operation recommendations based on user similarity to predict new operations that users will produce in the future, which can reduce the false positive rate (FPR). The FPR is reduced to 1.54% and the false positive identification rate (FPIR) is as high as 92.62%. Furthermore, to mitigate the risks caused by inaccurate authorization for users, we present user tags based on operation content and permission. The experimental results show that our proposed framework can detect insider threats more effectively and precisely, with lower FPR and high FPIR.

Password-based authentication system has been praised for its user-friendly, cost-effective, and easily deployable features. It is arguably the most commonly used security mechanism for various resources, services, and applications. On the other hand, it has well-known security flaws, including vulnerability to guessing attacks. Present state-of-the-art approaches have high overheads, as well as difficulties and unreliability during training, resulting in a poor user experience and a high false positive rate. As a result, a lightweight authentication compromise detection model that can make accurate detection with a low false positive rate is required.In this paper we propose – LOG-OFF – a behavior-based authentication compromise detection model. LOG-OFF is a lightweight model that can be deployed efficiently in practice because it does not include a labeled dataset. Based on the assumption that the behavioral pattern of a specific user does not suddenly change, we study the real-world authentication traffic data. The dataset contains more than 4 million records. We use two features to model the user behaviors, i.e., consecutive failures and login time, and develop a novel approach. LOG-OFF learns from the historical user behaviors to construct user profiles and makes probabilistic predictions of future login attempts for authentication compromise detection. LOG-OFF has a low false positive rate and latency, making it suitable for real-world deployment. In addition, it can also evolve with time and make more accurate detection as more data is being collected.

Recently, internet services have increased rapidly due to the Covid-19 epidemic. As a result, cloud computing applications, which serve end-users as subscriptions, are rising. Cloud computing provides various possibilities like cost savings, time and access to online resources via the internet for end-users. But as the number of cloud users increases, so does the potential for attacks. The availability and efficiency of cloud computing resources may be affected by a Distributed Denial of Service (DDoS) attack that could disrupt services' availability and processing power. DDoS attacks pose a serious threat to the integrity and confidentiality of computer networks and systems that remain important assets in the world today. Since there is no effective way to detect DDoS attacks, it is a reliable weapon for cyber attackers. However, the existing methods have limitations, such as relatively low accuracy detection and high false rate performance. To tackle these issues, this paper proposes a Deep Generative Radial Neural Network (DGRNN) with a sigmoid activation function and Mutual Information Gain based Feature Selection (MIGFS) techniques for detecting DDoS attacks for the cloud environment. Specifically, the proposed first pre-processing step uses data preparation using the (Network Security Lab) NSL-KDD dataset. The MIGFS algorithm detects the most efficient relevant features for DDoS attacks from the pre-processed dataset. The features are calculated by trust evaluation for detecting the attack based on relative features. After that, the proposed DGRNN algorithm is utilized for classification to detect DDoS attacks. The sigmoid activation function is to find accurate results for prediction in the cloud environment. So thus, the proposed experiment provides effective classification accuracy, performance, and time complexity.

Over the years, false news has polluted the online media landscape across the world. In this “post-truth” era, the narratives created by false news have now come into fruition through dismantled democracies, disbelief in science, and hyper-polarized societies. Despite increased efforts in fact-checking & labeling, strengthening detection systems, de-platforming powerful users, promoting media literacy and awareness of the issue, false news continues to be spread exponentially. This study models the behaviors of both the victims of false news and the platform in which it is spread— through the system dynamics methodology. The model was used to develop a policy design by evaluating existing and proposed solutions. The results recommended actively countering confirmation bias, restructuring social networking sites’ recommendation algorithms, and increasing public trust in news organizations.

New malware increasingly adopts novel fileless techniques to evade detection from antivirus programs. Process injection is one of the most popular fileless attack techniques. This technique makes malware more stealthy by writing malicious code into memory space and reusing the name and port of the host process. It is difficult for traditional security software to detect and intercept process injections due to the stealthiness of its behavior. We propose a novel framework called ProcGuard for detecting process injection behaviors. This framework collects sensitive function call information of typical process injection. Then we perform a fine-grained analysis of process injection behavior based on the function call chain characteristics of the program, and we also use the improved RCNN network to enhance API analysis on the tampered memory segments. We combine API analysis with deep learning to determine whether a process injection attack has been executed. We collect a large number of malicious samples with process injection behavior and construct a dataset for evaluating the effectiveness of ProcGuard. The experimental results demonstrate that it achieves an accuracy of 81.58% with a lower false-positive rate compared to other systems. In addition, we also evaluate the detection time and runtime performance loss metrics of ProcGuard, both of which are improved compared to previous detection tools.

Unmanned aerial vehicles (UAVs) and various network entities deployed on the ground can communicate with each other over the Internet of Drones (IoD), a network architecture designed expressly to allow communications between heterogenous entities. Drone technology has a wide range of uses, including on-demand package delivery, traffic and wild life surveillance, inspection of infrastructure and search, rescue and agriculture. However, IoD systems are vulnerable to numerous attacks, The main goal is to develop an all-encompassing security model that can be used to analyze security concerns in various UAV-based systems. With exceptional flexibility and increasing efficiency, trust management is a promising alternative to traditional detection methods. In a heterogeneous environment, it is also compatible with other security mechanisms. In this article, we present a fuzzy logic as an Insider Detection technique which calculate sensor data trust and assessing node behavior. To build confidence throughout the entire IoD, our proposal divides trust into two parts: Data trust and Node trust. This is in contrast to earlier models. Experimental results show that our solution is effective in terms of False positive ratio and Average of end-to-end delay.