Cryptanalysis of Round-Reduced SIMON32 Based on Deep Learning

Deep learning has played an important role in many fields. It shows significant potential to cryptanalysis. Differential cryptanalysis is an important method in the field of block cipher cryptanalysis. The key point of differential cryptanalysis is to find a differential distinguisher with longer rounds or higher probability. Firstly, we describe how to construct the ciphertext pairs required for differential cryptanalysis based on deep learning. Based on this, we train 9-round and 8-round differential distinguisher of SIMON32 based on deep residual neural networks. Secondly, we explore the impact of the input difference patterns on the accuracy of the distinguisher based on deep learning. For the input difference with Hamming weight of 1, the accuracy of 9-round distinguisher is different between the first 16 bits and the last 16 bits for non-zero bit positions. This is mainly caused by that its nonlinear operation is mainly concentrated in the first 16 bits. We also find that the accuracy of the distinguisher is different even if the input differences come from the differential characteristics with the same probability. Finally, we construct a last subkey recovery attack on 11-Round SIMON32 with practical data and time complexities. Our attack only uses about 29 chosen plaintexts and only needs about 45s for an attack with a success rate of over 90% using our workstation, which does not exceed 2^18:5 11-round encryption. At the same time, we extend the neural 9-round distinguisher to a 11-round distinguisher based on SAT, and propose a last subkey recovery attack on 13-Round SIMON32 using 2^12:5 chosen plaintexts with a success rate of over 90%. Compared with traditional approach, the complexity of the method based on deep learning is lower, both in time complexity and data complexity

Improve Neural Distinguisher for Cryptanalysis

At CRYPTO\u2719, Gohr built a bridge between deep learning and cryptanalysis. Based on deep neural networks, he trained neural distinguishers of Speck32/64 using a plaintext difference and single ciphertext pair. Compared with purely differential distinguishers, neural distinguishers successfully use features of the ciphertext pairs. Besides, with the help of neural distinguishers, he attacked 11-round Speck32/64 using Bayesian optimization. At EUROCRYPTO\u2721, Benamira proposed a detailed analysis about the inherent workings of Gohr\u27s distinguishers. Although their work opened a new direction of machine learning aided cryptanalysis, there are still two research gaps that researchers are eager to fill in. (1) How to further improve neural distinguishers? (2) Can we conduct effective key recovery on large-size block ciphers adopting neural distinguishers? In this paper, we propose a new algorithm and model to improve neural distinguishers in terms of accuracy and the number of rounds and present effective neural aided attack on large-size block ciphers. First, we design an algorithm based on SAT to improve neural distinguishers. With the help of SAT/SMT solver, we obtain new effective neural distinguishers of SIMON using the input differences of high-probability differential characteristics. Second, we propose a new neural distinguisher model using multiple output differences. Inspired by Benamira\u27s work and data augmentation in deep learning, we use the output differences to exploit more derived features and train neural distinguishers, by splicing output differences into a matrix as a sample. Based on the new model, we construct neural distinguishers of SIMON and Speck with round and accuracy promotion. Utilizing our neural distinguishers, we can distinguish reduced-round NSA block ciphers from pseudo-random permutation better. Moreover, we perform practical key recovery attacks on different versions of SIMON. For SIMON32/64 and SIMON48/96, we append additional 2-round optimal characteristics searched by SAT/SMT solver to the beginning of our neural distinguishers and attack 13-round SIMON32/64, 14-round SIMON48/96 using Gohr\u27s key recovery frame. For SIMON64/128, it costs too much time in precomputation, especially in wrong key response profile, which is unbearable for most of researchers. However, we show with experiments that the distribution of the wrong key profile is pseudo-periodic. Based on this, we make use of partial wrong key profile to describe the whole wrong key response profile, and then propose a generic key recovery attack scheme which can attack large-size block ciphers. As an application, we perform a key recovery attack on 13-round SIMON64/128 using a 11-round neural distinguisher. All our results are confirmed with experiments (source code available online)

Security evaluation for parameters of SIMON-like cipher based on neural network distinguisher

The neural distinguisher is a new tool widely used in crypto analysis of some ciphers.For SIMON-like block ciphers, there are multiple choices for their parameters, but the reasons for designer’s selection remain unexplained.Using neural distinguishers, the security of the parameters （a,b,c） of the SIMON-like with a block size of 32 bits was researched, and good choices of parameters were given.Firstly, using the idea of affine equivalence class proposed by K?lbl et al.in CRYPTO2015, these parameters can be divided into 509 classes.And 240 classes which satisfied gcd（a-b,2）=1 were mainly researched.Then a SAT/SMT model was built to help searching differential characteristics for each equivalent class.From these models, the optimal differential characteristics of SIMON-like was obtained.Using these input differences of optimal differential characteristics, the neural distinguishers were trained for the representative of each equivalence class, and the accuracy of the distinguishers was saved.It was found that 20 optimal parameters given by K?lbl et al.cannot make the neural distinguishers the lowest accuracy.On the contrary, there were 4 parameters, whose accuracy exceeds 80%.Furthermore, the 4 parameters were bad while facing neural distinguishers.Finally, comprehensively considering the choice of K?lbl et al.and the accuracy of different neural distinguishers, three good parameters, namely （6,11,1）,（1,8,3）, and（6,7,5） were given

Conditional differential analysis on the KATAN ciphers based on deep learning

Abstract KATAN ciphers are block ciphers using non‐linear feedback shift registers. In this study, the authors improve the results of conditional differential analysis on KATAN by using deep learning. Multi‐differential neural distinguishers are built to improve the accuracy of the neural distinguishers and increase the number of its rounds. Moreover, a conditional differential analysis framework is proposed based on deep learning with the multi‐differential neural distinguishers, resulting in a significant improvement than the previous. We present a practical key recovery attack on the 97‐round KATAN32 with 215.5 data complexity and 220.5 time complexity. The attack of the 82‐round KATAN48 and 70‐round KATAN64 are also presented as the best known practical results

Effectiveness of Panax ginseng

Mechanisms for Panax ginseng’s cardioprotective effect against ischemia reperfusion injury involve the estrogen-mediated pathway, but little is known about the role of androgen. A standardized Panax ginseng extract (RSE) was orally given with or without flutamide in a left anterior descending coronary artery ligation rat model. Infarct size, CK and LDH activities were measured. Time-related changes of NO, PI3K/Akt/eNOS signaling, and testosterone concentration were also investigated. RSE (80 mg/kg) significantly inhibited myocardial infarction and CK and LDH activities, while coadministration of flutamide abolished this effect of RSE. NO was increased by RSE and reached a peak after 15 min of ischemia; however, flutamide cotreatment suppressed this elevation. Western blot analysis showed that RSE significantly reversed the decreases of expression and activation of PI3K, Akt, and eNOS evoked by ischemia, whereas flutamide attenuated the effects of these protective mechanisms induced by RSE. RSE completely reversed the dropping of endogenous testosterone level induced by I/R injury. Flutamide plus RSE treatment not only abolished RSE’s effect but also produced a dramatic change on endogenous testosterone level after pretreatment and ischemia. Our results for the first time indicate that blocking androgen receptor abolishes the ability of Panax ginseng to protect the heart from myocardial I/R injury

Copper-Phosphido Catalysis: Enantioselective Addition of Phosphines to Cyclopropenes

We describe a copper-catalyst that promotes the addition of phosphines to cyclopropenes at ambient temperature. A range of cyclopropylphosphines bearing different steric and electronic properties can now be accessed in high yields and enantioselectivities. A combined experimental and theoretical mechanistic study supports insertion of a Cu(I)-phosphido intermediate into the strained olefin. Density functional theory calculations reveal migratory insertion as the stereodetermining step of the pathway, with final product formation occurring via a syn-protodemetalation. Enrichment of phosphorus stereocenters is demonstrated via a DyKAT process

The Role of Acupuncture Improving Cognitive Deficits due to Alzheimer’s Disease or Vascular Diseases through Regulating Neuroplasticity

Dementia affects millions of elderly worldwide causing remarkable costs to society, but effective treatment is still lacking. Acupuncture is one of the complementary therapies that has been applied to cognitive deficits such as Alzheimer’s disease (AD) and vascular cognitive impairment (VCI), while the underlying mechanisms of its therapeutic efficiency remain elusive. Neuroplasticity is defined as the ability of the nervous system to adapt to internal and external environmental changes, which may support some data to clarify mechanisms how acupuncture improves cognitive impairments. This review summarizes the up-to-date and comprehensive information on the effectiveness of acupuncture treatment on neurogenesis and gliogenesis, synaptic plasticity, related regulatory factors, and signaling pathways, as well as brain network connectivity, to lay ground for fully elucidating the potential mechanism of acupuncture on the regulation of neuroplasticity and promoting its clinical application as a complementary therapy for AD and VCI

Gingerenone A Alleviates Ferroptosis in Secondary Liver Injury in Colitis Mice via Activating Nrf2–Gpx4 Signaling Pathway

Patients with ulcerative colitis (UC) have been found to be frequently associated with secondary liver injury (SLI). In this study, we investigated the protective effect of GA on dextran sodium sulfate (DSS)-induced SLI in mice and its mechanism. The SLI was established by adding 4% DSS in the drinking water of mice, and the effects of GA (5, 20 mg/kg, p.o., once a day for 7 days) in hepatic tissues were analyzed. HepG2 cells were induced by lipopolysaccharide (LPS) to detect the effect of GA on ferroptosis and the underlying mechanism. Pathological damage was determined by H&E. Liver parameters (AST and ALT), antioxidant enzyme activities (MDA and SOD), and the level of Fe2+ in the liver were detected by kits. Cytokine levels (TNF-α, IL-1β, and IL-6) and Gpx4 activity in the liver were detected by ELISA. Finally, the activation of nuclear factor erythroid 2-like 2 (Nrf2) was detected to explore the mechanism. The results indicated that GA significantly attenuated DSS-induced hepatic pathological damage, liver parameters, and cytokine levels and increased the antioxidant enzyme activities. Moreover, GA attenuated ferroptosis in DSS-induced liver injury and upregulated Gpx4 expression in DSS-induced mice. Mechanistic experiments revealed that GA activated Nrf2 in mice. Taken together, this study demonstrates that GA can alleviate ferroptosis in SLI in DSS-induced colitis mice, and its protective effects are associated with activating the Nrf2–Gpx4 signaling pathway