Common Attack Methods Research Articles

Image encryption algorithm based on LDCML and DNA coding sequence

This paper proposes a new C-shaped scrambling method and combines it with the Logistic-Dynamics Coupled Map Lattices (LDCML) and DNA coding, thus designing a new image encryption scheme. First, we use the chaotic sequence generated by the LDCML system to perform preliminary scrambling on the original image. Then convert it into a DNA matrix according to a coding rule. After that perform the second scrambling according to the “C” shape. Next, the scrambled DNA matrix is subjected to a diffusion operation using the corresponding DNA sequence of the chaotic sequence generated by the LDCML system and the addition, subtraction and XOR rules of the DNA. At last, the final encrypted image is obtained by performing a DNA decoding operation on the diffused DNA matrix. Simulation experiments and security analysis show that the algorithm is safe and can resist common attack methods.

A novel image encryption algorithm based on fractional order 5D cellular neural network and Fisher-Yates scrambling.

This paper proposes a new chaotic image encryption algorithm. Firstly, an original phased composite chaotic map is used. The comparative study shows that the map cryptographic characteristics are better than the Logistic map, and the map is used as the controller of Fisher-Yates scrambling. Secondly, with the higher complexity of the fractional-order five-dimensional cellular neural network system, it is used as a diffusion controller in the encryption process. And mix the secret key, mapping and plaintext, we can obtain the final ciphertext. Finally, the comparative experiments prove that the proposed algorithm improves the encryption efficiency, has good security performance, and can resist common attack methods.

Providing Email Privacy by Preventing Webmail from Loading Malicious XSS Payloads

With the development of internet technology, email has become the formal communication method in modern society. Email often contains a large amount of personal privacy information, possible business agreements, and sensitive attachments, which make emails a good target for hackers. One of the most common attack method used by hackers is email XSS (Cross-site scripting). Through exploiting XSS vulnerabilities, hackers can steal identities, logging into the victim’s mailbox and stealing content directly. Therefore, this paper proposes an email XSS detection model based on deep learning technology, which can identify whether the XSS payload is carried in the email or not. Firstly, the model could extract the Sender, Receiver, Subject, Content, Attachment field information from the original email. Secondly, the email XSS corpus is formed after data processing. The Word2Vec algorithm is introduced to train the corpus and extract features for each email sample. Finally, the model uses the Bidirectional-RNN algorithm and Attention mechanism to train the email XSS detection model. In the experiment, the AUC (area under curve) value of the Bidirectional-RNN model reached 0.9979. When the Attention mechanism was added, the accuracy upper limit of the Bidirectional-RNN model was raised to 0.9936, and the loss value was reduced to 0.03.

Chaotic Image Encryption Algorithm Based on Fractional Order Scrambling Wavelet Transform and 3D Cyclic Displacement Operation

In order to deal with the security of image information, this paper proposes a chaotic image encryption method based on fractional wavelet transform. First, fractional wavelet transform is applied to preprocess the image as a signal. After three-level fractional wavelet decomposition, high-frequency components are obtained with low frequency components. Second, the hash function is used to generate the initial value of the chaotic system for the plaintext operation, and the initial value is brought into the Lorenz system to obtain the chaotic sequence, the chaotic sequence is scrambled for the high frequency components, and the 3D cyclic displacement scrambling is performed for the low frequency components. The high-frequency components and low-frequency components are reconstructed by fractional wavelet transform and restored to the final scrambled image. Last, in order to further deepen the encryption effect, cyclic displacement diffusion is performed on the scrambled image to obtain the final encrypted image. Experimental and simulation results show that the algorithm proposed in this paper has excellent encryption effect and can resist common attack methods.

A privacy encryption algorithm based on an improved chaotic system

This paper proposes an encryption scheme based on an improved chaotic system that can hide key information. First, the CML chaotic map is improved, and the chaotic state can be entered from 0. The image of the person is divided into key information and non-key information and spread by different diffusion methods. The RSA algorithm is used to encrypt the size of the key information as part of the key to enhancing the resistance to the chosen plaintext attack. Scrambling is performed at the pixel level using a CML system with a sorting function. The experimental and security analysis results show that the new algorithm has a large key space, is sensitive to keys and plaintext images, has a good encryption effect, and can resist common attack methods.

An improved color image encryption scheme and cryptanalysis based on a hyperchaotic sequence

In this paper, a three-dimensional (3D) discrete hyperchaotic map is introduced on the basis of the Marotto theorem. On the basis of the 3D discrete hyperchaotic map, an improved color image encryption scheme is designed. In this algorithm, the chaotic sequence generated by the 3D discrete hyperchaotic map confuses each pixel position of the color image, and the pixel values are replaced using DNA sequence operation to diffuse them. Security is analyzed by several common attack methods. Cryptanalysis shows that the improved encryption algorithm has superior security characteristics: not only does it prevent common attacks, but it does so more effectively than others and is easy to realize.

Ransomware deployment methods and analysis: views from a predictive model and human responses

Ransomware incidents have increased dramatically in the past few years. The number of ransomware variants is also increasing, which means signature and heuristic-based detection techniques are becoming harder to achieve, due to the ever changing pattern of ransomware attack vectors. Therefore, in order to combat ransomware, we need a better understanding on how ransomware is being deployed, its characteristics, as well as how potential victims may react to ransomware incidents. This paper aims to address this challenge by carrying out an investigation on 18 families of ransomware, leading to a model for categorising ransomware behavioural characteristics, which can then be used to improve detection and handling of ransomware incidents. The categorisation was done in respect to the stages of ransomware deployment methods with a predictive model we developed called Randep. The stages are fingerprint, propagate, communicate, map, encrypt, lock, delete and threaten. Analysing the samples gathered for the predictive model provided an insight into the stages and timeline of ransomware execution. Furthermore, we carried out a study on how potential victims (individuals, as well as IT support staff at universities and SMEs) detect that ransomware was being deployed on their machine, what steps they took to investigate the incident, and how they responded to the attack. Both quantitative and qualitative data were collected through questionnaires and in-depth interviews. The results shed an interesting light into the most common attack methods, the most targeted operating systems and the infection symptoms, as well as recommended defence mechanisms. This information can be used in the future to create behavioural patterns for improved ransomware detection and response.

Chaotic Image Encryption Algorithm Based on Bit-Combination Scrambling in Decimal System and Dynamic Diffusion

In this paper, an image encryption algorithm based on bit-combination scrambling and dynamic diffusion is proposed based on multiple chaotic systems. First, the plaintext image is scrambled. On the basis of the decimal system, the pixel value of the gray-scale plaintext image is divided into three groups: units, tens, and hundreds' digit. Each group performs different rounds of Arnold mapping, and the parameters of the Arnold mapping and the number of mapping wheels are determined by the plaintext image. The scrambled image is generated by combination. This algorithm avoids the periodicity of Arnold mapping. Second, the dynamic diffusion operation is designed. This operation can start from any position of the plaintext pixel value and combine chaotic sequence to do XOR around this point. Therefore, it has N modes of diffusion, where N is the number of pixel values. Third, this method is extended to color image encryption. The comparison test results show that this method has better security and can resist common attack methods.

A color image encryption algorithm based on Hopfield chaotic neural network

In this paper, a new chaotic color image encryption algorithm is proposed. This algorithm uses a composite chaotic map combined with a staged Logistic map and Tent map for initial key processing and then obtains the parameters required for Arnold mapping through a function transformation needed to scramble the image. The diffusion phase uses Hopfield chaotic neural network to generate the self-diffusion chaotic matrix, and the key is generated by a function transformation; finally, the scrambled image is XORed with the key to obtain the final ciphertext image. Experimental results show that the proposed algorithm improves encryption efficiency, has good security performance, and can resist common attack methods.

Research on Plaintext Restoration of AES Based on Neural Network

Known plaintext attack is a common attack method in cryptographic attack. For ciphertext, only known part of the plaintext but unknown key, how to restore the rest of the plaintext is an important part of the known plaintext attack. This paper uses backpropagation neural networks to perform cryptanalysis on AES in an attempt to restore plaintext. The results show that the neural network can restore the entire byte with a probability of more than 40%, restoring more than half of the plaintext bytes with a probability of more than 63% and restoring more than half of the bytes above 89%.

Trust and Privacy Exploitation in Online Social Networks

Online social networks have been typically created for convenience--so they haven't been built from the ground up with security in mind. They often have confusing privacy settings and are susceptible to various kinds of attacks that exploit users' trust and privacy. In this article, the authors discuss security pitfalls in today's social networks, briefly introducing common attack methods. They implemented a proof-of-concept Facebook app, which is actually a harmless malware that uses common attack methods to demonstrate the vulnerability of online social networks. Although today's online social networks commonly offer users a variety of security settings, users tend to trust the information obtained from online social networks regardless of the settings. This kind of user mentality can be more crucial than technical aspects in determining the level of security in online social networks.

An Improved QIM Watermarking Algorithm

Put forward an improved QIM watermark embedding algorithm. The carrier image is transformed into YCbCr space, lifting wavelet transform the three components, then using the QIM quantization method embeds watermark into high frequency coefficients of Cb and Cr component, and Y component of low frequency coefficients. The experimental results show that embedded watermark which using improved algorithm has good invisibility. Anti attack test results show that after using common attack method attack the image embedded watermark, it can still extract the watermark completely. The anti-attack performance of improved QIM algorithm is much better than texture watermarking algorithm based on DCT.

Research on Security of Routing Protocols Against Wormhole Attack in the Ad hoc Networks

As known that Ad hoc networks are made of a group of portable terminals and has many wireless transmitter as multi-hops autonomy system. While Ad hoc networks are very easy to be attacked by various kinds of network attacks, because it has limited resources, dynamic topology and open communication channel, etc. The wormhole attack is the common attack method, and it is an internal attack against routing protocols in the Ad hoc networks, the research on security of routing protocols against wormhole attack in the Ad hoc networks is to avoid the wormhole attack. In the study, the security routing mechanisms against wormhole attack in the Ad hoc networks are presented, on the basis of deeply research on wormhole attack principles and models. The modelling method and related simulations are presented in detailed to verify the effectiveness of security routing mechanisms.

Cryptanalysis and improvement of a certificateless threshold signature secure in the standard model

In this paper, we focus on security analysis of certificateless signature (CLS) schemes and certificateless threshold signature (CLTHS) schemes. We first propose four common attack methods for analyzing security of CLS schemes and CLTHS schemes. Then we give seven existing schemes as examples for demonstrating how to use our common attack methods, and prove that these schemes are vulnerable against public key replacement attacks or malicious-but-passive key generation center (KGC) attacks. By comprehensively using the proposed attack ideas, we also present three attacks against a CLTHS scheme proposed by Xiong et al. (2010) [28]: two public key replacement attacks and a malicious-but-passive KGC attack. Furthermore, we point out the flaws in the security proofs of these insecure CLS or CLTHS schemes. Finally, to resist these attacks, we propose an improved CLTHS scheme.

A Continuous Re-Authentication Approach Using Ensemble Learning

Insider threat becomes a more and more serious problem to organizations. Identify theft is a common attack method among various attack methods from insider. And it is very hard to detection since the attacker acts as a legal identity. Existing researches focus on Human Computer Interaction (HCI) behavior such as keystroke dynamics or mouse dynamics to detection this kind of attack. Based on an obvious observable that different applications show different HCI behavioral patterns (rich-key-operations or rich-mice-operations), we demonstrate that single authentication method is not efficient always in full time work period. In this paper, we provide an ensemble re-authentication approach to detection identify theft in real time, which combine the classification of keystroke-classifier and mice-classifier to determine whether the current operations is produced by the real legitimate user or not. Our experiment proves this new approach is efficient and can provide consistent detection ability with high accuracy.