Hybrid Cryptosystem with Computational Ghost Imaging Based on Integer Wavelet Transform and Chaotic Maps

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.

With the rapid development of computer network technology, information security has attracted increasing attention. Due to the characteristics of multi-dimensional operation and parallel processing capability, optical image encryption techniques have been receiving more and more attention. Since the well-known double random phase encoding technique was proposed, many other methods based on optical information processing means such as the use of optical transform, interference, and polarized light encoding, have been proposed for optical image encryption. However, recent researches have demonstrated that traditional optical encryption techniques are symmetric cryptosystems, in which decryption keys are identical to encryption keys and they have been found to be vulnerable to different types of attacks, such as known plaintext and chosen plaintext attacks. To overcome this shortcoming, asymmetric cryptosystems based on nonlinear phase-truncation techniques and phase retrieval algorithm have been proposed. Asymmetric cryptosystem is a cryptographic system in which encryption keys are different from decryption keys. The encryption keys are used as public keys which are disseminated widely, and the decryption keys are used as private keys which are known only to the authorized users. So, asymmetric cryptosystem can offer a higher-level security than symmetric cryptosystem. However, asymmetric cryptosystems based on phase retrieval algorithms require a lot of computational time, and asymmetric cryptosystems based on phase-truncated Fourier transforms have been found to be vulnerable to special attack. Therefore, in this paper, a novel asymmetric image encryption method is proposed by using the gyrator transform and vector operation. The original image is encrypted into two phase masks with vector operation. One is a random phase mask and the other is a phase mask related to the original image. In the encryption process, the random phase mask is used as a phase key and the other phase mask is transformed by gyrator transform. The transform result is performed by Fourier transform after being modulated by a phase distribution. The ciphertext is the amplitude of the above result. Compared with previous encryption schemes, the suggested method has two advantages. Firstly, we have proposed a new asymmetric encryption method based on the gyrator transform and vector operation. The decryption process is different from the encryption process. The gyrator transform and Fourier transform are used in the encryption process, while only the inverse operation of Fourier transform is employed in the decryption process. In addition, the decryption keys produced in the encryption process are different from the encryption keys. Therefore, the proposed scheme has high resistance against the conventional attacks. Secondly, the encrypted result is real-valued, which is convenient for display, transmission and storage.Numerical simulations illustrate the feasibility and effectiveness of the proposed encryption scheme.

Optimal algorithm for color medical encryption and compression images based on DNA coding and a hyperchaotic system in the moments

Internet is used as the main source of communication throughout the world. However due to public nature of internet data are always exposed to different types of attacks. To address this issue many researchers are working in this area and proposing data encryption techniques. Recently a new substitution box has been proposed for image encryption using many interesting properties like gingerbread-man chaotic map andS8permutation. But there are certain weaknesses in aforesaid technique which does not provide sufficient security. To resolve the security issue an enhanced version of existing technique is proposed in this paper. Lorenz chaotic map based confusion and diffusion processes in existing technique are employed. Lorenz map is used to remove strong correlation among the plain text image pixels. In diffusion stage a random matrix is generated through lorenz chaotic map and XORed with shuffled image. It the end, existing gingerbread-man chaotic map based S-box is applied to extract the final cipher text image. The proposed enhanced scheme is analysed by statistical analysis, key space analysis, information entropy analysis and differential analysis. In order to ensure the robustness and higher security of proposed scheme, results via Number of Pixel Rate Change (NPRC) and Unified Average Change Intensity (UACI) tests are also validated.

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.

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.

A robust and secure chaotic standard map based pseudorandom permutation-substitution scheme for image encryption

Exponentially increasing multimedia data over the internet raises concerns over its security. In the current work, we present a new technique for encrypting digital images based upon the pixel shuffling combined with changing pixel values using 128-bit secret key using henon chaotic map. Chaotic maps are characterized by their high sensitivity towards the initial parameters, which makes it a natural choice for developing a dynamic permutation matrix or as it is mostly called, permutation map. So we used chaotic Henon map in order to dynamically generate the permutation matrix. The initial parameters of the chaotic map and the secret key for changing the pixel values are derived from an external secret key. Horizontal and vertical permutations are used to perform pixel shuffling. Shuffling is used to destroy the correlation among the neighbour or adjacent image pixels, also it helps in increasing diffusion in the image. The proposed scheme is tested against a series of tests to measure its performance. Results of such tests indicate that the proposed algorithm is highly sensitive towards the encryption key and showed high resistance against various brute-force or statistical attacks.

In the last decade, the communication of images through the internet has increased. Due to the growing demands for data transfer through images, protection of data and safe communication is very important. For this purpose, many encryption techniques have been designed and developed. New and secured encryption schemes based on chaos theory have introduced methods for secure as well as fast communication. A modified image encryption process is proposed in this work with chaotic maps and orthogonal matrix in Hill cipher. Image encryption involves three phases. In the first phase, a chaotic Henon map is used for permuting the digital image. In the second phase, a Hill cipher is used whose encryption key is generated by an orthogonal matrix which further is produced from the equation of the plane. In the third phase, a sequence is generated by a chaotic tent map which is later XORed. Chaotic maps play an important role in the encryption process. To deal with the issues of fast and highly secured image processing, the prominent properties of non-periodical movement and non-convergence of chaotic theory play an important role. The proposed scheme is resistant to different attacks on the cipher image. Different tests have been applied to evaluate the proposed technique. The results of the tests such as key space analysis, key sensitivity analysis, and information entropy, histogram correlation of the adjacent pixels, number of pixel change rate (NPCR), peak signal to noise ratio (PSNR), and unified average changing intensity (UCAI) showed that our proposed scheme is an efficient encryption technique. The proposed approach is also compared with some state-of-the-art image encryption techniques. In the view of statistical analysis, we claim that our proposed encryption algorithm is secured.

The main purpose of the proposed image encryption (IE) method is to enhance various security characteristics to protect sensitive images on the internet. In the literature, chaotic maps are preferred for image encryption because of their high randomness and sensitivity to initial parameter values. Therefore, metaheuristic algorithms have been utilized to fine-tune the parameters. In this research, we have employed the metaheuristic Lyrebird optimization (LO) algorithm to fine-tune the chaotic map parameters. The LO algorithm is chosen over other algorithms due to its better exploration and exploitation rate to search for the best solution. The LO algorithm searches for the best parameter value based on the objective function. This research involves the formulation of a multi-objective function that incorporates numerous security parameters, namely, entropy and correlation coefficient (CC), to improve the security attributes of the IE model. The proposed IE model has two steps. In the first step, a random key is generated to transform the pixels of the secret image, whereas in the second step, the image pixels are shuffled based on the shuffling index values to reduce the correlation among the neighboring pixels. In both steps, we have used the chaotic logistic map. The proposed IE is evaluated against statistical and differential attacks on the standard dataset by considering the various images and their different resolutions. The uniform distribution of the histogram of the encrypted images is validated using the chi-square test. Besides that, entropy analysis shows that the average entropy value is 7.9973, 7.9993, and 7.9998 for 256x256, 512x512, and 1024x1024 image resolutions. The average CC and SSIM are near zero value. The average NPCR value (>0.9969) is near the 1 value. Finally, the comparative analysis based on entropy, CC, and NPCR parameters shows that the proposed IE model outperforms the existing models.

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 this paper, an image encryption scheme based on dynamic DNA sequence and two dimension logistic map is proposed. Firstly two different pseudo random sequences are generated using two dimension Sine-Henon alteration map. These sequences are used for altering the positions of each pixel of plain image row wise and column wise respectively. Secondly each pixels of distorted image and values of random sequences are converted into a DNA sequence dynamically using one dimension logistic map. Reversible DNA operations are applied between DNA converted pixel and random values. At last after decoding the results of DNA operations cipher image is obtained. Different theoretical analyses and experimental results proved the effectiveness of this algorithm. Large key space proved that it is possible to protect different types of attacks using our proposed encryption scheme.

A new blind color image watermarking based on Walsh Hadamard Transform (WHT) is proposed in this paper which uses Triangular Vertex Transform (TVT) along with WHT. This method introduces a Triangular Vertex Transform (TVT) and Inverse Triangular Vertex Transform (ITVT) as well as applies this transform for image watermarking. Initially, convert the R, G, B color image to TVT coefficients U, V and W using the proposed TVT transform. Then the W coefficient is subdivided into 4 × 4 non-overlapping blocks, which are then transformed using WHT. The watermark is encrypted with the key K using the 2D-Logistic Sine Coupling Map encryption to obtain the encrypted watermarked image. The bits of the encrypted image is embedded in the first row WHT coefficients since the embedding of watermark bit on the first row provides high robustness to attacks. After embedding the data, the inverse WHT and inverse TVT are applied to obtain the watermarked image. The extraction process is similar to that of the embedding process where TVT is applied on the Watermarked image to construct W′ coefficient. Subdivide the attained W′ coefficient into 4 × 4 sub-block and then the WHT is applied to the 4 × 4 sub-block for extracting the data from the first row of WHT coefficients. The performance of the proposed watermarking algorithm outperforms in terms of Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Normalized cross-correlation (NC). Experimental results reveal that the proposed watermarking method is highly resistant to different types of attacks.

Optimized Chaotic encrypted image based on continuous raster scan method