Symmetry-Adapted Machine Learning for Information Security

Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis

Logistic Map-Based Fragile Watermarking for Pixel Level Tamper Detection and Resistance

An efficient fragile image watermarking technique for pixel level tamper detection and resistance is proposed. It uses five most significant bits of the pixels to generate watermark bits and embeds them in the three least significant bits. The proposed technique uses a logistic map and takes advantage of its sensitivity property to a small change in the initial condition. At the same time, it incorporates the confusion/diffusion and hashing techniques used in many cryptographic systems to resist tampering at pixel level as well as at block level. This paper also presents two new approaches called nonaggressive and aggressive tamper detection algorithms. Simulations show that the proposed technique can provide more than 99.39% tamper detection capability with less than 2.31% false-positive detection and less than 0.61% false-negative detection responses

A Feature-Based Fragile Watermarking of Color Image for Secure E-Government Restoration

this research developed a method using fragile watermarking technique for color images to achieve secure e-government tamper detection with recovery capability. Before performing the watermark insertion process, the RGB image is converted first into YCbCr image. The watermark component is selected from the image feature that approximates the original image, in which the chrominance value features as a watermark component. For a better detection process, 3-tuple watermark, check bits, parity bits, and recovery bits are selected. The average block in each 2 x 2 pixels is selected as 8 restoration bits of each component, the embedding process work on the pixels by modifying the pixels value of three Least Significant Bit (LSB) . The secret key for secure tamper detection and recovery, transmitted along with the watermarked image, and the algorithm mixture is used to extract information at the receiving end. The results show remarkably effective to restore tampered image

Content Fragile Watermarking for H.264/AVC Video Authentication

Discrete Cosine transform (DCT) to generate the authentication data that are treated as a fragile watermark. This watermark is embedded in the motion vectors (MVs) The advances in multimedia technologies and digital processing tools have brought with them new challenges for the source and content authentication. To ensure the integrity of the H.264/AVC video stream, we introduce an approach based on a content fragile video watermarking method using an independent authentication of each Group of Pictures (GOPs) within the video. This technique uses robust visual features extracted from the video pertaining to the set of selected macroblocs (MBs) which hold the best partition mode in a tree-structured motion compensation process. An additional security degree is offered by the proposed method through using a more secured keyed function HMAC-SHA-256 and randomly choosing candidates from already selected MBs. In here, the watermark detection and verification processes are blind, whereas the tampered frames detection is not since it needs the original frames within the tampered GOPs. The proposed scheme achieves an accurate authentication technique with a high fragility and fidelity whilst maintaining the original bitrate and the perceptual quality. Furthermore, its ability to detect the tampered frames in case of spatial, temporal and colour manipulations, is confirmed