On the period of GSM's A5/1 stream cipher and its internal state transition structure

2011 ◽  
Author(s):  
Vahid Amin Ghaffari ◽  
Ali Vardasbi
Keyword(s):  
Stream Cipher ◽  
State Transition ◽  
Internal State ◽  
Transition Structure

scholarly journals Breaking Trivium Stream Cipher Implemented in ASIC Using Experimental Attacks and DFA

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6909
Author(s):  
Francisco Eugenio Potestad-Ordóñez ◽  
Manuel Valencia-Barrero ◽  
Carmen Baena-Oliva ◽  
Pilar Parra-Fernández ◽  
Carlos Jesús Jiménez-Fernández
Keyword(s):  
Stream Cipher ◽  
Internal State ◽  
Clock Cycle ◽  
Fault Analysis ◽  
Invasive Technique ◽  
Sensitive Information ◽  
Secret Key ◽  
Key Recovery ◽  
Differential Fault Analysis ◽  
Secret Keys

One of the best methods to improve the security of cryptographic systems used to exchange sensitive information is to attack them to find their vulnerabilities and to strengthen them in subsequent designs. Trivium stream cipher is one of the lightweight ciphers designed for security applications in the Internet of things (IoT). In this paper, we present a complete setup to attack ASIC implementations of Trivium which allows recovering the secret keys using the active non-invasive technique attack of clock manipulation, combined with Differential Fault Analysis (DFA) cryptanalysis. The attack system is able to inject effective transient faults into the Trivium in a clock cycle and sample the faulty output. Then, the internal state of the Trivium is recovered using the DFA cryptanalysis through the comparison between the correct and the faulty outputs. Finally, a backward version of Trivium was also designed to go back and get the secret keys from the initial internal states. The key recovery has been verified with numerous simulations data attacks and used with the experimental data obtained from the Application Specific Integrated Circuit (ASIC) Trivium. The secret key of the Trivium were recovered experimentally in 100% of the attempts, considering a real scenario and minimum assumptions.

Effective search strategy via internal state transition graphs on onboard planning for deep space probes

2018 ◽  
Vol 148 ◽  
pp. 235-245 ◽  
Author(s):  
Rui Xu ◽  
Hao Jin ◽  
Wenming Xu ◽  
Pingyuan Cui ◽  
Xiaodong Han
Keyword(s):  
Search Strategy ◽  
State Transition ◽  
Internal State ◽  
Deep Space ◽  
Transition Graphs

ON GUESS AND DETERMINE ANALYSIS OF RABBIT

2011 ◽  
Vol 22 (06) ◽  
pp. 1283-1296 ◽  
Author(s):  
XIUTAO FENG ◽  
ZHENQING SHI ◽  
CHUANKUN WU ◽  
DENGGUO FENG
Keyword(s):  
Stream Cipher ◽  
Internal State ◽  
Original Data ◽  
State Function ◽  
Brute Force ◽  
View Point ◽  
New Techniques ◽  
Extraction Scheme ◽  
Data Granularity ◽  
Future Work

Rabbit is a stream cipher proposed by M. Boesgaard et al., and has been selected into the final portfolio after three evaluation phases of the ECRYPT Stream Cipher Project (eSTREAM). So far only a few papers studied its security besides a series of white papers by the designers of Rabbit. Recently we presented a new idea to evaluate the security of a word-oriented stream cipher algorithm from a smaller data granularity instead of its original data granularity and applied it successfully to the stream cipher SOSEMANUK. In this work we apply the same idea to the Rabbit algorithm and analyze its security in resistance against the guess and determine attack from the view point of byte units. As a result, we present two new approaches of solving all xj,t+1' s and gj,t' s from the next-state function and the extraction scheme of Rabbit, whose complexities are 2166 and 2140.68 respectively, which are dramatically lower than those proposed by Lu et al. (2192 and 2174 resp.) at ISC 2008. Finally based on the above new results we propose a byte-based guess and determine attack on Rabbit, which only needs a small segment of known keystream to recover the whole internal state of Rabbit with time complexity 2242. Though the complexity of our attack is far higher than that of a brute force (2128), we believe that some new techniques adopted in this paper are of interest for future work on Rabbit.

scholarly journals Information Theory Based Evaluation of the RC4 Stream Cipher Outputs

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 896
Author(s):  
Evaristo José Madarro-Capó ◽  
Carlos Miguel Legón-Pérez ◽  
Omar Rojas ◽  
Guillermo Sosa-Gómez
Keyword(s):  
Information Theory ◽  
Stream Cipher ◽  
Internal State ◽  
Probability Distributions ◽  
Random Variables ◽  
Test Statistic ◽  
Output Sequence ◽  
Rc4 Stream Cipher ◽  
Average Information

This paper presents a criterion, based on information theory, to measure the amount of average information provided by the sequences of outputs of the RC4 on the internal state. The test statistic used is the sum of the maximum plausible estimates of the entropies H(jt|zt), corresponding to the probability distributions P(jt|zt) of the sequences of random variables (jt)t∈T and (zt)t∈T, independent, but not identically distributed, where zt are the known values of the outputs, while jt is one of the unknown elements of the internal state of the RC4. It is experimentally demonstrated that the test statistic allows for determining the most vulnerable RC4 outputs, and it is proposed to be used as a vulnerability metric for each RC4 output sequence concerning the iterative probabilistic attack.

scholarly journals Planning in Factored State and Action Spaces with Learned Binarized Neural Network Transition Models

2018 ◽  
Author(s):  
Buser Say ◽  
Scott Sanner
Keyword(s):  
State Transition ◽  
Boolean Satisfiability ◽  
Planning Problem ◽  
Transition Structure ◽  
Complex State ◽  
Constraint Generation ◽  
Transition Models ◽  
Network Transition ◽  
Action Spaces ◽  
Planning Problems

In this paper, we leverage the efficiency of Binarized Neural Networks (BNNs) to learn complex state transition models of planning domains with discretized factored state and action spaces. In order to directly exploit this transition structure for planning, we present two novel compilations of the learned factored planning problem with BNNs based on reductions to Boolean Satisfiability (FD-SAT-Plan) as well as Binary Linear Programming (FD-BLP-Plan). Experimentally, we show the effectiveness of learning complex transition models with BNNs, and test the runtime efficiency of both encodings on the learned factored planning problem. After this initial investigation, we present an incremental constraint generation algorithm based on generalized landmark constraints to improve the planning accuracy of our encodings. Finally, we show how to extend the best performing encoding (FD-BLP-Plan+) beyond goals to handle factored planning problems with rewards.

scholarly journals Anterior cingulate cortex represents action-state predictions and causally mediates model-based reinforcement learning in a two-step decision task

2017 ◽  
Author(s):  
Thomas Akam ◽  
Ines Rodrigues-Vaz ◽  
Ivo Marcelo ◽  
Xiangyu Zhang ◽  
Michael Pereira ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Anterior Cingulate Cortex ◽  
State Transition ◽  
Cingulate Cortex ◽  
Anterior Cingulate ◽  
State Transitions ◽  
Decision Task ◽  
Transition Structure ◽  
Model Based ◽  
Subsequent Choice

SummaryThe anterior cingulate cortex (ACC) is implicated in learning the value of actions, but it remains poorly understood whether and how it contributes to model-based mechanisms that use action-state predictions and afford behavioural flexibility. To isolate these mechanisms, we developed a multi-step decision task for mice in which both action-state transition probabilities and reward probabilities changed over time. Calcium imaging revealed ramps of choice-selective neuronal activity, followed by an evolving representation of the state reached and trial outcome, with different neuronal populations representing reward in different states. ACC neurons represented the current action-state transition structure, whether state transitions were expected or surprising, and the predicted state given chosen action. Optogenetic inhibition of ACC blocked the influence of action-state transitions on subsequent choice, without affecting the influence of rewards. These data support a role for ACC in model-based reinforcement learning, specifically in using action-state transitions to guide subsequent choice.HighlightsA novel two-step task disambiguates model-based and model-free RL in mice.ACC represents all trial events, reward representation is contextualised by state.ACC represents action-state transition structure, predicted states, and surprise.Inhibiting ACC impedes action-state transitions from influencing subsequent choice.

scholarly journals On the Statistical Analysis of ZUC, Espresso and Grain v1

2021 ◽  
pp. 384-390
Author(s):  
Saurabh Shrivastava ◽  
K. V. Lakshmy ◽  
Chungath Srinivasan
Keyword(s):  
Statistical Analysis ◽  
Stream Cipher ◽  
Internal State ◽  
Statistical Tests ◽  
Stream Ciphers ◽  
Hamming Weight ◽  
Secret Key ◽  
State Correlation ◽  
Very High

A stream cipher generates long keystream to be XORed with plaintext to produce ciphertext. A stream cipher is said to be secure if the keystream that it produces is consistently random. One of the ways by which we can analyze stream ciphers is by testing randomness of the keystream. The statistical tests mainly try to find if any output keystream leaks any information about the secret key or the cipher’s internal state and also check the randomness of the keystream. We have applied these tests to different keystreams generated by ZUC, Espresso and Grain v1 stream ciphers to check for any weaknesses. We have also proposed four new statistical tests to analyze the internal state when the hamming weight of key and IV used is very high or low. Out of these four tests, Grain v1 fails the last test i.e. internal state correlation using high hamming weight IV.

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