Biblio

With the advent of the era of big data, information technology has been rapidly developed and the application of computers has been popularized. However, network technology is a double-edged sword. While providing convenience, it also faces many problems, among which there are many hidden dangers of network information security. Based on this, based on the era background of big data, the network information security analysis, explore the main network security problems, and elaborate computer information network security matters needing attention, to strengthen the network security management, and put forward countermeasures, so as to improve the level of network security.

The widespread application of industrial Internet identifiers has increased the security risks of industrial Internet and identifier resolution system. In order to improve the security capabilities of identifier resolution system, this paper analyzes the security challenges faced by identifier resolution system at this stage, and in line with the concept of layered security defense in depth, divides the security domains of identifier resolution system and proposes a multi-level security strategy based on security domains by deploying appropriate protective measures in each security domain.

The importance of the security of industrial control network has become increasingly prominent. Aiming at the defects of main security protection system in the intelligent manufacturing industrial control network, we propose a security attack risk detection and defense, and emergency processing capability synchronization technology system suitable for the intelligent manufacturing industrial control system. Integrating system control and network security theories, a flexible and reconfigurable system-wide security architecture method is proposed. On the basis of considering the high availability and strong real-time of the system, our research centers on key technologies supporting system-wide security analysis, defense strategy deployment and synchronization, including weak supervision system reinforcement and pattern matching, etc.. Our research is helpful to solve the problem of industrial control network of “old but full of loopholes” caused by the long-term closed development of the production network of important parts, and alleviate the contradiction between the high availability of the production system and the relatively backward security defense measures.

The risk of detecting incidents in the field of computer technology in Maritime transport is considered. The structure of the computer incident investigation system and its functions are given. The system of conducting investigations of computer incidents on sea transport is considered. A possible algorithm for investigating the incident using the tools of forensic science and an algorithm for transmitting the received data for further processing are presented.

With the development of openness, sharing and interconnection of computer network, the architecture of enterprise network becomes more and more complex, and various network security problems appear. Threat Intelligence(TI) Analysis and situation awareness(SA) are the prediction and analysis technology of enterprise security risk, while intrusion detection technology belongs to active defense technology. In order to ensure the safe operation of computer network system, we must establish a multi-level and comprehensive security system. This paper analyzes many security risks faced by enterprise computer network, and integrates threat intelligence analysis, security situation assessment, intrusion detection and other technologies to build a comprehensive enterprise security system to ensure the security of large enterprise network.

Automatic Static Analysis Tools (ASATs) detect coding rule violations, including mistakes and bad practices that frequently occur during programming. While ASATs are widely used in both OSS and industry, the developers do not resolve more than 80% of the detected violations. As one of the reasons, most ASATs users do not customize their ASATs to their projects after installation; the ASATs with the default configuration report many rule violations that confuse developers. To reduce the ratio of such uninteresting warning messages, we propose a method to customize ASATs according to the product source code automatically. Our fundamental hypothesis is: A software project has interesting ASAT rules that are consistent over time. Our method takes source code as input and generates an ASAT configuration. In particular, the method enables optional (i.e., disabled by default) rules that detected no violations on the version because developers are likely to follow the rules in future development. Our method also disables violated rules because developers were unlikely to follow them. To evaluate the method, we applied our method to 643 versions of four JavaScript projects. The generated configurations for all four projects increased the ASAT precision. They also increased recall for two projects. The result shows that our method helps developers to focus on their attractive rule violations. Our implementation of the proposed method is available at https://github.com/devreplay/linter-maintainer

To increase and maintain web application security, developers could use some different methods, one of them is static code analysis. This method could find security vulnerabilities inside a source code without the need of running the program. It could also be automated by using tools, which considered more efficient than manual reviews. One specific method which is commonly used in static code analysis is taint analysis. Taint analysis usually utilizes source code modeling to prepare the code for analysis process to detect any untrusted data flows into security sensitives computations. While this kind of analysis could be very helpful, static code analysis tool for Laravel-based web application is still quite rare, despite its popularity. Therefore, in this research, we want to know how static code (taint) analysis could be utilized to detect security vulnerabilities and how the projects (Laravel-based) should be modeled in order to facilitate this analysis. We then developed a static analysis tool, which models the application’s source code using AST and dictionary to be used as the base of the taint analysis. The tool first parsed the route file of Laravel project to get a list of controller files. Each file in that list would be parsed in order to build the source code representation, before actually being analyzed using taint analysis method. The experiments was done using this tool shows that the tools (with taint analysis) could detect 13 security vulnerabilities from 6 Laravel-based projects with one False Negative. An ineffective sanitizer was the suspected cause of this False Negative. This also shows that proposed modeling technique could be helpful in facilitating taint analysis in Laravel-based projects. For future development and studies, this tool should be tested with more Laravel and even other framework based web application with a wider range of security vulnerabilities.

Business process mining of a large-scale project has many benefits such as finding vulnerabilities, improving processes, collecting data for data science, generating more clear and simple representation, etc. The general way of process mining is to turn event data such as application logs into insights and actions. Observing logs broad enough to depict the whole business logic scenario of a large project can become very costly due to difficult environment setup, unavailability of users, presence of not reachable or hardly reachable log statements, etc. Using static source code analysis to extract logs and arranging them perfect runtime execution order is a potential way to solve the problem and reduce the business process mining operation cost.

We propose an approach for filtering and prioritizing static code analysis alerts while these alerts are being reviewed by the developer. We construct a Prolog knowledge base that captures the data flow information in the source code as well as the reported alerts, their properties and associations with the data flow. The knowledge base is updated as the developer reviews the listed alerts and decides whether they point at an actual fault or not. These updates provide useful information since some of the alerts of the same type can be related in terms of their root cause. Hence, dynamically updated knowledge base can be queried to eliminate or prioritize the remaining alerts in the review list. We present a motivating example to illustrate the approach and its automation by integrating a set of tools.

A source code static analysis became an industrial standard for program source code issues early detection. As one of requirements to such kind of analysis is high performance to provide response of automatic code checking tool as early as possible as far as such kind of tools integrates to Continuous testing and Integration systems. In this paper we propose a source code static analysis algorithm for solving performance issue of source code static analysis tool in general way.

The technique of concealing a confidential information in a carrier information is known as steganography. When we use digital images as carriers, it is termed as image steganography. The advancements in digital technology and the need for information security have given great significance for image steganographic methods in the area of secured communication. An efficient steganographic system is characterized by a good trade-off between its features such as imperceptibility and capacity. The proposed scheme implements an edge-detection based adaptive steganography with transform domain embedding, offering high imperceptibility and capacity. The scheme employs an adaptive embedding technique to select optimal data-hiding regions in carrier image, using Canny edge detection and a Convolutional Neural Network (CNN). Then, the secret image is embedded in the Dual-Tree Complex Wavelet Transform (DTCWT) coefficients of the selected carrier image blocks, with the help of Singular Value Decomposition (SVD). The analysis of the scheme is performed using metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Normalized Cross Correlation (NCC).

Image steganography is the operation of hiding a message into a cover image. the message can be text, codes, or image. Hiding an image into another is the proposed approach in this paper. Based on LSB coding, a k-LSB-based method is proposed using k least bits to hide the image. For decoding the hidden image, a region detection operation is used to know the blocks contains the hidden image. The resolution of stego image can be affected, for that, an image quality enhancement method is used to enhance the image resolution. To demonstrate the effectiveness of the proposed approach, we compare it with some of the state-of-the-art methods.

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

In order to improve the concealment of image steganography, a new method is proposed. The algorithm firstly adopted GM (1, 1) model to detect texture and edge points of carrier image, then embedded secret information in them. GM (1, 1) model of optimized parameters can make full use of pixels information. These pixels are the nearest to the detected point, so it improves the detection accuracy. The method is a kind of steganography based on human visual system. By testing the stegano images with different embedding capacities, the result indicates concealment and image quality of the proposed algorithm are better than BPCS (Bit-plane Complexity Segmentation) and PVD (Pixel-value Differencing), which are also based on visual characteristics.

Digital signal processor (DSP) intellectual property (IP) cores are the underlying hardware responsible for high performance data intensive applications. However an unauthorized IP vendor may counterfeit the DSP IPs and infuse them into the design-chain. Thus fake IPs or integrated circuits (ICs) are unknowingly integrated into consumer electronics (CE) systems, leading to reliability and safety issues for users. The latent solution to this threat is hardware steganography wherein vendor's secret information is covertly inserted into the design to enable detection of counterfeiting. A key-regulated hash-modules chaining based IP steganography is presented in our paper to secure against counterfeiting threat. The proposed approach yielded a robust steganography achieving very high security with regard to stego-key length than previous approaches.

Due to the difficulty of obtaining a database of original images that are required in the classification process to detect tampering, this paper presents a technique for detecting image tampering such as image steganography in the spatial domain. The system depends on deriving the auto-correlation function of the image histogram, then applying a high-pass filter with a threshold. This technique can be used to decide which image is cover or a stego image, without adopting the original image. The results have eventually revealed the validity of this system. Although this study has focused on least-significant-bit (LSB) steganography, we expect that it could be extended to other types of image tapering.

Although the Pixel-Value Differencing (PVD) steganography can avoid being detected by the RS steganalysis, the histogram of the pixel-value differences poses an abnormal distribution. Based on this hiding characteristic, this paper proposes a PVD steganalysis based on chi-Square statistics. The degrees of freedom were adopted to be tested for obtaining various detection accuracies (ACs). Experimental results demonstrate the detection accuracies are all above 80%. When the degrees of freedom are set as 10 while the accuracy is the best (AC = 83%). It means that the proposed Chi-Square based method is an efficient detection for PVD steganography.

Many portable imaging devices use the operation of "trunc" (rounding towards zero) instead of rounding as the final quantizer for computing DCT coefficients during JPEG compression. We show that this has rather profound consequences for steganography and its detection. In particular, side-informed steganography needs to be redesigned due to the different nature of the rounding error. The steganographic algorithm J-UNIWARD becomes vulnerable to steganalysis with the JPEG rich model and needs to be adjusted for this source. Steganalysis detectors need to be retrained since a steganalyst unaware of the existence of the trunc quantizer will experience 100% false alarm.

The Internet of Things (IoT) technology is being utilized in endless applications nowadays and the security of these applications is of great importance. Image based IoT applications serve a wide variety of fields such as medical application and smart cities. Steganography is a great threat to these applications where adversaries can use the images in these applications to hide malicious messages. Therefore, this paper presents an image steganalysis technique that employs Convolutional Neural Networks (CNN) to detect the infamous JPEG steganography technique: JPEG universal wavelet relative distortion (J-UNIWARD). Several experiments were conducted to determine the breaking point of J-UNIWARD, whether the hiding technique relies on correlation of the images, and the effect of utilizing Discrete Cosine Transform (DCT) on the performance of the CNN. The results of the CNN display that the breaking point of J-UNIWARD is 1.5 (bpnzAC), the correlation of the database affects the detection accuracy, and DCT increases the detection accuracy by 13%.

Information sharing through internet has becoming challenge due to high-risk factor of attacks to the information being transferred. In this paper, a novel image-encryption edge based Image steganography technique is proposed. The proposed algorithm uses logistic map for encrypting the information prior to transmission. Laplacian of Gaussian (LoG) edge operator is used to find edge areas of the colored-cover-image. Simulation analysis demonstrates that the proposed algorithm has a good amount of payload along with better results of security analysis. The proposed scheme is compared with the existing-methods.

Steganography is the concept to conceal information and the data by embedding it as secret data into various digital medium in order to achieve higher security. To achieve this, many steganographic algorithms are already proposed. The ability of human eyes as well as invisibility remain the most important and prominent factor for the security and protection. The most commonly used security measure of data hiding within imagesYet it is ineffective against Steganalysis and lacks proper verifications. Thus the proposed system of Image Steganography using PVD (Pixel Value Differentiating) proves to be a better choice. It compresses and embeds data in images at the pixel value difference calculated between two consecutive pixels. To increase the security, another technique called Edge Detection is used along with PVD to embed data at the edges. Edge Detection techniques like Canny algorithm are used to find the edges in an image horizontally as well as vertically. The edge pixels in an image can be used to handle more bits of messages, because more pixel value shifts can be handled by the image edge area.

The recent focus of cyber security on automated detection of malware for Android apps has omitted the study of some apps used for “legitimate” purposes, such as steganography apps. Mobile steganography apps can be used for delivering harmful messages, and while current research on steganalysis targets the detection of stego images using academic algorithms and well-built benchmarking image data sets, the community has overlooked uncovering a mobile app itself for its ability to perform steganographic embedding. Developing automatic tools for identifying the code in a suspect app as a stego app can be very challenging: steganography algorithms can be represented in a variety of ways, and there exists many image editing algorithms which appear similar to steganography algorithms.This paper proposes the first automated approach to detect Android steganography apps. We use symbolic execution to summarize an app’s image operation behavior into expression trees, and match the extracted expression trees with reference trees that represents the expected behavior of a steganography embedding process. We use a structural feature based similarity measure to calculate the similarity between expression trees. Our experiments show that, the propose approach can detect real world Android stego apps that implement common spatial domain and frequency domain embedding algorithms with a high degree of accuracy. Furthermore, our procedure describes a general framework that has the potential to be applied to other similar questions when studying program behaviors.

This paper proposes 2 multiple layer message security schemes. Information security is carried out through the implementation of cryptography, steganography and image processing techniques. In both schemes, the sensitive data is first encrypted by employing a chaotic function. In the first proposed scheme, LSB steganography is then applied to 2D slices of a 3D image. In the second proposed scheme, a corner detection filter is first applied to the 2D slices of a 3D image, then LSB embedding is carried out in those corner-detected pixels. The number of neighboring pixels used for corner detection is varied and its effect is noted. Performance of the proposed schemes is numerically evaluated using a number of metrics, including the mean squared error (MSE), the peak signal to noise ratio (PSNR), the structure similarity index measure (SSIM), the normalized cross-correlation (NCC), the image fidelity (IF), as well as the image difference (ID). The proposed schemes exhibit superior payload capacity and security in comparison to their counterparts from the literature.

Existing search-based coverless text steganography algorithms according to the characteristics of the text, do not need to modify the carrier, and have good resistance to detection, but they rely on a large text data set and have a limited hiding capacity. For this reason, this paper proposes a coverless steganography method based on the source XML file organization of the OOXML documents from a new perspective. It analyzes the organization of OOXML documents, and uses the differences of organization to construct the mapping between documents and secret information, so as to realize the coverless information hiding. To achieve the efficiency of information hiding, a compound tree model is designed and introduced to construct the OOXML document category library. Compared with the existing coverless information hiding methods, the text set size that this method relies on is significantly reduced, and the flexibility of the mapping is higher under the similar hiding capacity.

Steganography security is the main concern in today’s informative world. The fact is that communication takes place to hide information secretly. Steganography is the technique of hiding secret data within an ordinary, non-secret, file, text message and images. This technique avoids detection of the secret data then extracted at its destination. The main reason for using steganography is, we can hide any secret message behind its ordinary file. This work presents a unique technique for image steganography based on a 512-bit algorithm. The secure stego image is a very challenging task to give protection. Therefore we used the least significant bit (LSB) techniques for implementing stego and cover image. However, data encryption and decryption are used to embedded text and replace data into the least significant bit (LSB) for better approaches. Android-based interface used in encryption-decryption techniques that evaluated in this process.Contribution—this research work with 512-bit data simultaneously in a block cipher to reduce the time complexity of a system, android platform used for data encryption decryption process. Steganography model works with stego image that interacts with LSB techniques for data hiding.