Adaptive watermark scheme with RBF neural networks

Purpose The authors argue that the current studies about malicious insiders confuse the fact that malicious attacks belong to two different categories, namely, those that launch instrumental attacks and expressive attacks. The authors collect malicious insider data from publicly available sources and use text-mining techniques to analyze the association between malicious insiders’ characteristics and the different types of attack. Design/methodology/approach The authors investigated the relationship between personality characteristics and different types of malicious attacks. For the personality characteristics, the authors use the same method as Liang et al. (2016), which extracted these characteristics based on a keyword-characteristic dictionary. For different types of malicious attacks, two raters rated each case based on criteria modified from criminology research to determine the degree of expressiveness and instrumentality. Findings The results show that malicious insiders who are manipulative or seeking personal gain tend to carry out instrumental attacks. Malicious insiders who are arrogant tend to conduct expressive attacks. Research limitations/implications This study uses third party articles to identify the personality characteristics of known malicious insiders. As such, not all personality characteristics may have been reported. Data availability was an issue. Practical implications Understanding if different personality characteristics lead different types of attacks can help managers identify employees who exhibit them and mitigate an attack before it occurs. Social implications Malicious insider attacks can have devastating results on businesses and employees. Help to identify potential malicious insiders before they act, may prevent undue harm. Originality/value This study used 132 cases of none malicious insiders to examine their attack objectives. No other study that the authors know of used that many cases.

There are different types of adversarial attacks and defences for machine learning algorithms which makes assessing the robustness of an algorithm a daunting task. Moreover, there is an intrinsic bias in these adversarial attacks and defences to make matters worse. Here, we organise the problems faced: a) Model Dependence, b) Insufficient Evaluation, c) False Adversarial Samples, and d) Perturbation Dependent Results. Based on this, we propose a model agnostic adversarial robustness assessment method based on L0 and L∞ distance-based norms and the concept of robustness levels to tackle the problems. We validate our robustness assessment on several neural network architectures (WideResNet, ResNet, AllConv, DenseNet, NIN, LeNet and CapsNet) and adversarial defences for image classification problem. The proposed robustness assessment reveals that the robustness may vary significantly depending on the metric used (i.e., L0 or L∞). Hence, the duality should be taken into account for a correct evaluation. Moreover, a mathematical derivation and a counter-example suggest that L1 and L2 metrics alone are not sufficient to avoid spurious adversarial samples. Interestingly, the threshold attack of the proposed assessment is a novel L∞ black-box adversarial method which requires even more minor perturbation than the One-Pixel Attack (only 12% of One-Pixel Attack’s amount of perturbation) to achieve similar results. We further show that all current networks and defences are vulnerable at all levels of robustness, suggesting that current networks and defences are only effective against a few attacks keeping the models vulnerable to different types of attacks.

Intrusion detection System forms a vital component of internet security. To keep pace with the growing trends, there is a critical need to replace single layer detection technology with multi layer detection. Different types of Denial of Service (DoS) attacks thwart authorized users from gaining access to the networks and we tried to detect as well as alleviate some of those attacks. In this paper, we have proposed a novel cross layer intrusion detection architecture to discover the malicious nodes and different types of DoS attacks by exploiting the information available across different layers of protocol stack in order to improve the accuracy of detection. We have used cooperative anomaly intrusion detection with data mining technique to enhance the proposed architecture. We have implemented fixed width clustering algorithm for efficient detection of the anomalies in the MANET traffic and also generated different types of attacks in the network. The simulation of the proposed architecture is performed in OPNET simulator and we got the result as we expected.

This paper proposes a signal adaptive, spread spectrum based inaudible speech watermarking scheme. The local signal energy information is used to compute the energy of the watermark to be embedded making the algorithm a signal adaptive one. The designed watermarking scheme, being spread spectrum based, is robust against attacks such as watermark removal and impairment attacks. The detection algorithm uses a correlation detector and does not require the knowledge of the original unwatermarked signal. The watermark embedding and detection algorithms were implemented on a field programmable gate array (FPGA) to evaluate the hardware complexity of the proposed algorithm. The timing constraints and gate count were studied to draw conclusions about implementing the circuit in real time. The scheme was tested for various 13-bit speech samples of global system for mobile (GSM) cellular communications.

In a present-day world there are different types of attacks being launched on computing devices. World is experiencing more and more cyber-attacks and types of attacks are also increasing. For example, an IoT device in a home network can act as a botnet attacking other devices or there could be man in the middle attack. As time goes by more and more devices are being connected within any given network. All these devices will be vulnerable to attacks if any one of the devices is compromised within the network. This complicates Intrusion Detection in any given network. Manual detection and intervention is nearly impossible. Hence it is quintessential to detect different types of attacks with more confidence with less computation complexity and time. A lot of research work has already been done in this area where the attacks have been studied separately. In this paper we focus on detecting intrusions including IoT botnet attacks and other types of network attacks. To achieve this, we build a multiclass classification using supervised learning models along with the dimensionality reduction technique. Numerous studies on ML-based IDS have been using KDD or the upgraded versions of KDD dataset. In this study we used a new dataset, IoT network Intrusion Detection dataset.

Prediction error expansion based on sorting is an important technique in reversible watermarking since it yields large embedding capacity and low distortion. In this paper, an efficient and adaptive reversible watermarking scheme is proposed based on trimmed prediction and pixel selection sorting. The trimmed prediction excludes one singular pixel from the neighboring region. A more efficient sorting method is used to achieve lower distortion. Then, a further sorting that considers context complexity is proposed to ensure better visual quality. The smooth pixels located in rough areas are assigned high priorities for carrying bits by using the prediction error expansion method. With these improvements, our method shows better performances in terms of capacity and distortion.

We propose a watermark embedding and extracting method for blind watermarking. It uses the characteristics of a scalar quantizer to comply with the recommendation in JPEG, MPEG series, or JPEG2000. Our method performs embedding of a watermark bit by shifting the corresponding frequency transform coefficient (the watermark position) to a quantization index according to the value of the watermark bit, which prevents from losing the watermark information during the data compression process. The watermark can be embedded simultaneously to the quantization process without an additional process for watermarking, which means it can be performed at the same speed to the compression process. In the embedding process, a Linear Feedback Shift Register (LFSR) is used to hide the watermark informations and the watermark positions. The experimental results showed that the proposed method satisfies enough robustness and imperceptibility that are the major requirements for watermarking.

Digital watermark technology has been proposed as an effective method to protect the copyright of multimedia data. An adaptive image watermark scheme based on fuzzy inference system that in discrete wavelet transform domain is proposed. By exploiting the brightness and texture characteristics of human visual system and considering properties of the original image wavelet coefficient, a fuzzy inference system is designed whose inputs are parameters of brightness and texture of the original image and output is the strength of embedded watermarks. It ensures that the watermark embedding strength is determined adaptively. The experimental results show that the watermarks embed by the proposed scheme are robust against attacks commonly used image processing methods such as JPEG compression, Gaussian noise, cropping, mean filtering, median filtering, rotation, and rescaling etc. Proposed scheme are shown to provide very good results in term of image imperceptibility, too.

The relation between the digital watermarking and spread-spectrum is analyzed in this paper at the beginning. Then, a spread-spectrum adaptive digital watermarking scheme based on the wavelet transform is proposed and the system for this digital watermarking algorithm based on DSP is realized. Finally, a testing on the image digital watermarking processing system by DSP is conducted. The result from the testing approves that the system is flexible and can accomplish watermark embedding and detecting.

We present a simulation-based study of the impacts of different types of attacks in mobile ad hoc networks (MANETs). We consider three common types of attacks: blackhole attack, jellyfish attack and neighbor attack. Specifically, we study how the number of attackers and their positions affect the performance metrics of a connection such as packet delivery ratio, throughput, end-to-end delay, and delay jitter. The results enable us to suggest measures to minimize the impacts of the above types of attacks in MANETs.

In recent years, digital watermarking techniques have been proposed to protect the copyright of multimedia data. Different watermarking schemes have been suggested for images. This paper proposes a watermarking algorithm based on image segmentation and Discrete Cosine Transform (DCT). The image is first segmented using Expectation Maximization (EM) algorithm. For each segment, the image segment is subdivided into pixels blocks of size 8 times 8 (64 pixels), and zigzag reordered. The DCT of the block is then computed. Then, a pseudorandom sequence of real numbers is embedded in the DCT domain of each image segment. Different experiments are conducted to show the performance of the scheme under different types of attacks. The results show that our proposed watermark scheme is robust to common signal distortions, including geometric manipulations.

This paper presents a watermarking algorithm to embed robust watermark and fragile authentication data into color satellite images. The aim of this watermarking algorithm is to protect the copyright and image originality. This algorithm doesn't require the original image in the extraction process and it is a blind algorithm. The algorithm contains two watermarks, one for the copyright and another one for the image authenticity. The first watermark is embedded in the frequency domain using the discrete cosine transform while the second watermark is embedded in the spatial domain which consists of hash functions using MD5 and Hex formats. The fragile watermark is inserted in one row of each one of the RGB channels. The distortion caused by the embedding is assessed using peak signal to noise ratio (PSNR) and similarity structure index measure (SSIM). Robustness against different types of attacks have been assessed using Stirmark and by introducing small modifications to the satellite images. The robust watermarking scheme survived many attacks such as JPEG compression and filtering. The fragile watermark is very sensitive and any small modification to the image can be detected. It was found that embedding in the green channel offers better performance compared to red and blue channels. In addition the embedding in the Y channel offers better robustness against attacks.

The 21st century is the digital world, the utilization of digital information like multimedia that contains image, audio and video is massively expanding because of the progression in innovation and web transformation. With this headway to achieve the one's proprietorship and copyrights for this advanced information is the greatest test. Digital watermarking is one of the procedures to accomplish one's proprietorship and copyrights with safely and securely. Digital watermarking is the strategy in which the proprietor's copyright data can be installed into the original media either as an image, audio or video. There are two fundamental components we have to consider for this digital audio watermarking to get the high robustness and also imperceptibility against the piracy, malevolent assaults, and different kind of transformation attacks. Despite the fact that there are numerous difficulties to accomplish these outcomes, in this paper, our proposed audio watermarking system is utilized to enhance the robustness, reliability and imperceptibility of the embedded data with security. For Security, in our proposed work we are utilizing DSSS encryption algorithm and some vital transformation techniques utilized that are DWT (Discreet Wavelet Transformation) up to 4-level to get most reduced frequency sub-band and after that DFT(Discrete Fourier Transform) is applied to get least frequency component from sub-band found by DWT in which the alterations are done and after that SVD (Singular esteem Decomposition)is used to it, so unique audio document does not have any effect of watermark bits to improve robustness and imperceptibility. This algorithm is implemented in MATLAB software that is used for numerical computation and data analysis. The GUI is presented in this paper for audio watermarking with different calculations.

Codes that aim to detect any error regardless of its multiplicity are referred to as security oriented codes. Most of these codes are designed to protect uniformly distributed codewords; there are few solutions which are used in protecting systems with non-uniformly distributed words. The paper introduces a new encoding method, termed “Level-Out encoding”, for cases in which some words are more likely to appear than others and compares the effectiveness of the different methods. The advantage of the suggested coding scheme is demonstrated by simulations with two different types of attackers — a Blind Attacker, limited to the knowledge of the a priori information word and codeword distribution, and a Memory Based Attacker with access to the conditional probabilities based on previous information words as well.

Security aspects in IPv6 networks – implementation and testing