Biblio
Chinese Remainder Theorem (CRT) is one of the spatial domain methods that is more implemented in the data hiding method watermarking. CRT is used to improve security and imperceptibility in the watermarking method. CRT is rarely studied in studies that discuss steganographic images. Steganography research focuses more on increasing imperceptibility, embedded payload, and message security, so methods like LSB are still popular to be developed to date. CRT and LSB have some similarities such as default payload capacity and both are methods in the spatial domain which can produce good imperceptibility quality of stego image. But CRT is very superior in terms of security, so CRT is also widely used in cryptographic algorithms. Some ways to increase imperceptibility in image steganography are edge detection and spread spectrum embedding. This research proposes a combination of edge detection techniques and spread-spectrum embedding based on the CRT method to produce imperceptibility and safe image steganography method. Based on the test results it is proven that the combination of the proposed methods can increase imperceptibility of CRT-based steganography based on SSIM metric.
The progressed computational abilities of numerous asset compelled gadgets mobile phones have empowered different research zones including picture recovery from enormous information stores for various IoT applications. The real difficulties for picture recovery utilizing cell phones in an IoT situation are the computational intricacy and capacity. To manage enormous information in IoT condition for picture recovery a light-weighted profound learning base framework for vitality obliged gadgets. The framework initially recognizes and crop face areas from a picture utilizing Viola-Jones calculation with extra face classifier to take out the identification issue. Besides, the utilizes convolutional framework layers of a financially savvy pre-prepared CNN demonstrate with characterized highlights to speak to faces. Next, highlights of the huge information vault are listed to accomplish a quicker coordinating procedure for constant recovery. At long last, Euclidean separation is utilized to discover comparability among question and archive pictures. For exploratory assessment, we made a nearby facial pictures dataset it including equally single and gathering face pictures. In the dataset can be utilized by different specialists as a scale for examination with other ongoing facial picture recovery frameworks. The trial results demonstrate that our planned framework beats other cutting edge highlight extraction strategies as far as proficiency and recovery for IoT-helped vitality obliged stages.
We consider the problem of attack detection for IoT networks based only on passively collected network parameters. For the first time in the literature, we develop a blind attack detection method based on data conformity evaluation. Network parameters collected passively, are converted to their conformity values through iterative projections on refined L1-norm tensor subspaces. We demonstrate our algorithmic development in a case study for a simulated star topology network. Type of attack, affected devices, as well as, attack time frame can be easily identified.
Anomaly detection generally involves the extraction of features from entities' or users' properties, and the design of anomaly detection models using machine learning or deep learning algorithms. However, only considering entities' property information could lead to high false positives. We posit the importance of also considering connections or relationships between entities in the detecting of anomalous behaviors and associated threat groups. Therefore, in this paper, we design a GCN (graph convolutional networks) based anomaly detection model to detect anomalous behaviors of users and malicious threat groups. The GCN model could characterize entities' properties and structural information between them into graphs. This allows the GCN based anomaly detection model to detect both anomalous behaviors of individuals and associated anomalous groups. We then evaluate the proposed model using a real-world insider threat data set. The results show that the proposed model outperforms several state-of-art baseline methods (i.e., random forest, logistic regression, SVM, and CNN). Moreover, the proposed model can also be applied to other anomaly detection applications.
The amount of connected devices in the industrial environment is growing continuously, due to the ongoing demands of new features like predictive maintenance. New business models require more data, collected by IIoT edge node sensors based on inexpensive and low performance Microcontroller Units (MCUs). A negative side effect of this rise of interconnections is the increased attack surface, enabled by a larger network with more network services. Attaching badly documented and cheap devices to industrial networks often without permission of the administrator even further increases the security risk. A decent method to monitor the network and detect “unwanted” devices is network scanning. Typically, this scanning procedure is executed by a computer or server in each sub-network. In this paper, we introduce network scanning and mapping as a building block to scan directly from the Industrial Internet of Things (IIoT) edge node devices. This module scans the network in a pseudo-random periodic manner to discover devices and detect changes in the network structure. Furthermore, we validate our approach in an industrial testbed to show the feasibility of this approach.
With the rapid development of the Internet, preserving the security of confidential data has become a challenging issue. An effective method to this end is to apply steganography techniques. In this paper, we propose an efficient steganography algorithm which applies edge detection and MPC algorithm for data concealment in digital images. The proposed edge detection scheme partitions the given image, namely cover image, into blocks. Next, it identifies the edge blocks based on the variance of their corner pixels. Embedding the confidential data in sharp edges causes less distortion in comparison to the smooth areas. To diminish the imposed distortion by data embedding in edge blocks, we employ LSB and MPC algorithms. In the proposed scheme, the blocks are split into some groups firstly. Next, a full tree is constructed per group using the LSBs of its pixels. This tree is converted into another full tree in some rounds. The resultant tree is used to modify the considered LSBs. After the accomplishment of the data embedding process, the final image, which is called stego image, is derived. According to the experimental results, the proposed algorithm improves PSNR with at least 5.4 compared to the previous schemes.
In the current society, people pay more and more attention to identity security, especially in the case of some highly confidential or personal privacy, one-to-one identification is particularly important. The iris recognition just has the characteristics of high efficiency, not easy to be counterfeited, etc., which has been promoted as an identity technology. This paper has carried out research on daugman algorithm and iris edge detection.
Security challenges present in Machine-to-Machine Communication (M2M-C) and big data paradigm are fundamentally different from conventional network security challenges. In M2M-C paradigms, “Trust” is a vital constituent of security solutions that address security threats and for such solutions,it is important to quantify and evaluate the amount of trust in the information and its source. In this work, we focus on Machine Learning (ML) Based Trust (MLBT) evaluation model for detecting malicious activities in a vehicular Based M2M-C (VBM2M-C) network. In particular, we present an Entropy Based Feature Engineering (EBFE) coupled Extreme Gradient Boosting (XGBoost) model which is optimized with Binary Particle Swarm optimization technique. Based on three performance metrics, i.e., Accuracy Rate (AR), True Positive Rate (TPR), False Positive Rate (FPR), the effectiveness of the proposed method is evaluated in comparison to the state-of-the-art ensemble models, such as XGBoost and Random Forest. The simulation results demonstrates the superiority of the proposed model with approximately 10% improvement in accuracy, TPR and FPR, with reference to the attacker density of 30% compared with the start-of-the-art algorithms.
We consider distributed Kalman filter for dynamic state estimation over wireless sensor networks. It is promising but challenging when network is under cyber attacks. Since the information exchange between nodes, the malicious attacks quickly spread across the entire network, which causing large measurement errors and even to the collapse of sensor networks. Aiming at the malicious network attack, a trust-based distributed processing frame is proposed. Which allows neighbor nodes to exchange information, and a series of trusted nodes are found using truth discovery. As a demonstration, distributed Cooperative Localization is considered, and numerical results are provided to evaluate the performance of the proposed approach by considering random, false data injection and replay attacks.
The paper introduces a method of efficient partial firmware update with several advantages compared to common methods. The amount of data to transfer for an update is reduced, the energetic efficiency is increased and as the method is designed for over the air update, the radio spectrum occupancy is decreased. Herein described approach uses Lua scripting interface to introduce updatable fragments of invokable native code. This requires a dedicated memory layout, which is herein introduced. This method allows not only to distribute patches for deployed systems, but also on demand add-ons. At the end, the security aspects of proposed firmware update system is discussed and its limitations are presented.
Cyber-physical systems (CPS) are state-of-the-art communication environments that offer various applications with distinct requirements. However, security in CPS is a nonnegotiable concept, since without a proper security mechanism the applications of CPS may risk human lives, the privacy of individuals, and system operations. In this paper, we focus on PHY-layer security approaches in CPS to prevent passive eavesdropping attacks, and we propose an integration of physical layer operations to enhance security. Thanks to the McEliece cryptosystem, error injection is firstly applied to information bits, which are encoded with the forward error correction (FEC) schemes. Golay and Hamming codes are selected as FEC schemes to satisfy power and computational efficiency. Then obtained codewords are transmitted across reliable intermediate relays to the legitimate receiver. As a performance metric, the decoding frame error rate of the eavesdropper is analytically obtained for the fragmentary existence of significant noise between relays and Eve. The simulation results validate the analytical calculations, and the obtained results show that the number of low-quality channels and the selected FEC scheme affects the performance of the proposed model.
Memory corruption vulnerabilities have been around for decades and rank among the most prevalent vulnerabilities in embedded systems. Yet this constrained environment poses unique design and implementation challenges that significantly complicate the adoption of common hardening techniques. Combined with the irregular and involved nature of embedded patch management, this results in prolonged vulnerability exposure windows and vulnerabilities that are relatively easy to exploit. Considering the sensitive and critical nature of many embedded systems, this situation merits significant improvement. In this work, we present the first quantitative study of exploit mitigation adoption in 42 embedded operating systems, showing the embedded world to significantly lag behind the general-purpose world. To improve the security of deeply embedded systems, we subsequently present μArmor, an approach to address some of the key gaps identified in our quantitative analysis. μArmor raises the bar for exploitation of embedded memory corruption vulnerabilities, while being adoptable on the short term without incurring prohibitive extra performance or storage costs.