Gotowa bibliografia na temat „White-box attack”

This Research paper provides special white-box attacks and outputs from it. Wearable IoT units perform keep doubtlessly captured, then accessed within an unauthorized behavior due to the fact concerning their bodily nature. That case, they are between white-box attack systems, toughness the opponent may additionally have quantity,visibility about the implementation of the inbuilt crypto system including complete control over its solution platform. The white-box attacks on wearable devices is an assignment without a doubt. To serve as a countermeasure in opposition to these problems in such contexts, here analyzing encryption schemes in imitation of protecting the confidentiality concerning records against white-box attacks. The lightweight encryption, intention in accordance with protecting the confidentiality over data towards white-box attacks is the recently stability good algorithm. In that paper, we read the extraordinary algorithms too.

Adversarial attacks are among the biggest threats that affect the accuracy of classifiers in machine learning systems. This type of attacks tricks the classification model and make it perform false predictions by providing noised data that only human can detect that noise. The risk of attacks is high in natural language processing applications because most of the data collected in this case is taken from social networking sites that do not impose any restrictions on users when writing comments, which allows the attack to be created (either intentionally or unintentionally) easily and simply affecting the level of accuracy of the model. In this paper, The MLP model was used for the sentiment analysis of the texts taken from the tweets, the effect of applying a white-box adversarial attack on this classifier was studied and a technique was proposed to protect it from the attack. After applying the proposed methodology, we found that the adversarial attack decreases the accuracy of the classifier from 55.17% to 11.11%, and after applying the proposed defense technique, this contributed to an increase in the accuracy of the classifier up to 77.77%, and therefore the proposed plan can be adopted in the face of the adversarial attack. Attacker determines their targets strategically and deliberately depend on vulnerabilities they have ascertained. Organization and individuals mostly try to protect themselves from one occurrence or type on an attack. Still, they have to acknowledge that the attacker may easily move focus to advanced uncovered vulnerabilities. Even if someone successfully tackles several attacks, risks remain, and the need to face threats will happen for the predictable future.

Black-box attacks against deep neural network (DNN) classifiers are receiving increasing attention because they represent a more practical approach in the real world than white box attacks. In black-box environments, adversaries have limited knowledge regarding the target model. This makes it difficult to estimate gradients for crafting adversarial examples, such that powerful white-box algorithms cannot be directly applied to black-box attacks. Therefore, a well-known black-box attack strategy creates local DNNs, called substitute models, to emulate the target model. The adversaries then craft adversarial examples using the substitute models instead of the unknown target model. The substitute models repeat the query process and are trained by observing labels from the target model’s responses to queries. However, emulating a target model usually requires numerous queries because new DNNs are trained from the beginning. In this study, we propose a new training method for substitute models to minimize the number of queries. We consider the number of queries as an important factor for practical black-box attacks because real-world systems often restrict queries for security and financial purposes. To decrease the number of queries, the proposed method does not emulate the entire target model and only adjusts the partial classification boundary based on a current attack. Furthermore, it does not use queries in the pre-training phase and creates queries only in the retraining phase. The experimental results indicate that the proposed method is effective in terms of the number of queries and attack success ratio against MNIST, VGGFace2, and ImageNet classifiers in query-limited black-box environments. Further, we demonstrate a black-box attack against a commercial classifier, Google AutoML Vision.

White-box attack context assumes that the running environments of algorithms are visible and modifiable. Algorithms that can resist the white-box attack context are called white-box cryptography. The elliptic curve digital signature algorithm (ECDSA) is one of the most widely used digital signature algorithms which can provide integrity, authenticity, and nonrepudiation. Since the private key in the classical ECDSA is plaintext, it is easy for attackers to obtain the private key. To increase the security of the private key under the white-box attack context, this article presents an algorithm for the white-box implementation of ECDSA. It uses the lookup table technology and the “cloud plus side” mode to protect the private key. The residue number system (RNS) theory is used to reduce the size of storage. Moreover, the article analyzes the security of the proposed algorithm against an exhaustive search attack, a random number attack, a code lifting attack, and so on. The efficiency of the proposed scheme is compared with that of the classical ECDSA through experiments.