Cryptanalysis of CIKS-128 and CIKS-128H Suitable for Intelligent Multimedia and Ubiquitous Computing Systems

Recently, data-dependent permutations (DDP) that are very suitable for intelligent multimedia and ubiquitous computing systems have been introduced as a new cryptographic primitive for the design of fast encryption systems. The CIKS-128 and CIKS-128H block ciphers are the typical examples of DDP-based encryption algorithms. In this paper, we show that CIKS-128 and CIKS-128H are vulnerable to related-key differential attacks. We first describe how to construct their full-round related-key differential characteristics with high probabilities and then we exploit them to break the full-round CIKS-128 and CIKS-128H with 2^44, and 2^48 data/time complexities, respectively

Cryptanalysis of Block Ciphers

The block cipher is one of the most important primitives in modern cryptography, information and network security; one of the primary purposes of such ciphers is to provide confidentiality for data transmitted in insecure communication environments. To ensure that confidentiality is robustly provided, it is essential to investigate the security of a block cipher against a variety of cryptanalytic attacks. In this thesis, we propose a new extension of differential cryptanalysis, which we call the impossible boomerang attack. We describe the early abort technique for (related-key) impossible differential cryptanalysis and rectangle attacks. Finally, we analyse the security of a number of block ciphers that are currently being widely used or have recently been proposed for use in emerging cryptographic applications; our main cryptanalytic results are as follows. An impossible differential attack on 7-round AES when used with 128 or 192 key bits, and an impossible differential attack on 8-round AES when used with 256 key bits. An impossible boomerang attack on 6-round AES when used with 128 key bits, and an impossible boomerang attack on 7-round AES when used with 192 or 256 key bits. A related-key impossible boomerang attack on 8-round AES when used with 192 key bits, and a related-key impossible boomerang attack on 9-round AES when used with 256 key bits, both using two keys. An impossible differential attack on 11-round reduced Camellia when used with 128 key bits, an impossible differential attack on 12-round reduced Camellia when used with 192 key bits, and an impossible differential attack on 13-round reduced Camellia when used with 256 key bits. A related-key rectangle attack on the full Cobra-F64a, and a related-key differential attack on the full Cobra-F64b. A related-key rectangle attack on 44-round SHACAL-2. A related-key rectangle attack on 36-round XTEA. An impossible differential attack on 25-round reduced HIGHT, a related-key rectangle attack on 26-round reduced HIGHT, and a related-key impossible differential attack on 28-round reduced HIGHT. In terms of either the attack complexity or the numbers of attacked rounds, the attacks presented in the thesis are better than any previously published cryptanalytic results for the block ciphers concerned, except in the case of AES; for AES, the presented impossible differential attacks on 7-round AES used with 128 key bits and 8-round AES used with 256 key bits are the best currently published results on AES in a single key attack scenario, and the presented related-key impossible boomerang attacks on 8-round AES used with 192 key bits and 9-round AES used with 256 key bits are the best currently published results on AES in a related-key attack scenario involving two keys

Ultrafast cryptography with indefinitely switchable optical nanoantennas

Bistability is widely exploited to demonstrate all-optical signal processing and light-based computing. The standard paradigm of switching between two steady states corresponding to '0" and '1" bits is based on the rule that a transition occurs when the signal pulse intensity overcomes the bistability threshold, and otherwise, the system remains in the initial state. Here, we break with this concept by revealing the phenomenon of indefinite switching in which the eventual steady state of a resonant bistable system is transformed into a nontrivial function of signal pulse parameters for moderately intense signal pulses. The essential nonlinearity of the indefinite switching allows realization of well-protected cryptographic algorithms with a single bistable element in contrast to software-assisted cryptographic protocols that require thousands of logic gates. As a proof of concept, we demonstrate stream deciphering of the word 'enigma' by means of an indefinitely switchable optical nanoantenna. An extremely high bitrate ranging from ~0.1 to 1 terabits per second and a small size make such systems promising as basic elements for all-optical cryptographic architectures.Comment: Light: Science & Applications, to appea

Privacy-aware Security Applications in the Era of Internet of Things

In this dissertation, we introduce several novel privacy-aware security applications. We split these contributions into three main categories: First, to strengthen the current authentication mechanisms, we designed two novel privacy-aware alternative complementary authentication mechanisms, Continuous Authentication (CA) and Multi-factor Authentication (MFA). Our first system is Wearable-assisted Continuous Authentication (WACA), where we used the sensor data collected from a wrist-worn device to authenticate users continuously. Then, we improved WACA by integrating a noise-tolerant template matching technique called NTT-Sec to make it privacy-aware as the collected data can be sensitive. We also designed a novel, lightweight, Privacy-aware Continuous Authentication (PACA) protocol. PACA is easily applicable to other biometric authentication mechanisms when feature vectors are represented as fixed-length real-valued vectors. In addition to CA, we also introduced a privacy-aware multi-factor authentication method, called PINTA. In PINTA, we used fuzzy hashing and homomorphic encryption mechanisms to protect the users\u27 sensitive profiles while providing privacy-preserving authentication. For the second privacy-aware contribution, we designed a multi-stage privacy attack to smart home users using the wireless network traffic generated during the communication of the devices. The attack works even on the encrypted data as it is only using the metadata of the network traffic. Moreover, we also designed a novel solution based on the generation of spoofed traffic. Finally, we introduced two privacy-aware secure data exchange mechanisms, which allow sharing the data between multiple parties (e.g., companies, hospitals) while preserving the privacy of the individual in the dataset. These mechanisms were realized with the combination of Secure Multiparty Computation (SMC) and Differential Privacy (DP) techniques. In addition, we designed a policy language, called Curie Policy Language (CPL), to handle the conflicting relationships among parties. The novel methods, attacks, and countermeasures in this dissertation were verified with theoretical analysis and extensive experiments with real devices and users. We believe that the research in this dissertation has far-reaching implications on privacy-aware alternative complementary authentication methods, smart home user privacy research, as well as the privacy-aware and secure data exchange methods

Symmetry-Adapted Machine Learning for Information Security

Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis

Machine-Learning assisted Side-Channel Attacks on RNS-based Elliptic Curve Implementations using Hybrid Feature Engineering

Side-channel attacks based on machine learning have recently been introduced to recover the secret information from software and hardware implementations of mathematically secure algorithms. Convolutional Neural Networks (CNNs) have proven to outperform the template attacks due to their ability of handling misalignment in the symmetric algorithms leakage data traces. However, one of the limitations of deep learning algorithms is the requirement of huge datasets for model training. For evaluation scenarios, where limited leakage trace instances are available, simple machine learning with the selection of proper feature engineering, data splitting, and validation techniques, can be more effective. Moreover, limited analysis exists for public-key algorithms, especially on non-traditional implementations like those using Residue Number System (RNS). Template attacks are successful on RNS-based Elliptic Curve Cryptography (ECC), only if the aligned portion is used in templates. In this study, we present a systematic methodology for the evaluation of ECC cryptosystems with and without countermeasures against machine learning side-channel attacks using two attack models. RNS-based ECC datasets have been evaluated using four machine learning classifiers and comparison is provided with existing state-of-the-art template attacks. Moreover, we analyze the impact of raw features and advanced hybrid feature engineering techniques, along with the effect of splitting ratio. We discuss the metrics and procedures that can be used for accurate classification on the imbalance datasets. The experimental results demonstrate that, for ECC RNS datasets, the efficiency of simple machine learning algorithms is better than the complex deep learning techniques when such datasets are not so huge

Some aspects of a code division multiple access local area network

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