The RC4 stream cipher is widely used including WEP and WPA, which are the security protocols for IEEE 802.11 wireless standard. WPA improved a construction of the RC4 key setting known as TKIP to avoid the known WEP attacks. The first 3-byte RC4 keys generated by IV in WPA are known since IV can be obtained by observing packets. The weaknesses in TKIP using the known IV were reported by Sen Gupta et al. at FSE 2014 and by Ito and Miyaji at FSE 2015. Both showed that TKIP induces many RC4 key correlations including the keystream bytes or the unknown internal states. Ideally TKIP should be constructed in such a way that it can keep the security level of generic RC4. In the first part of this paper, we will provide newly theoretical proofs of 17 correlations remain unproven in our previous work theoretically. Our theoretical analysis can make clear how TKIP induces biases of internal states in generic RC4. In the second part of this paper, we will further provide a refined construction of the RC4 key setting. As a result, we can reduce the number of correlations in the refined construction by about 70% in comparison with that in the original setting.

WPA is the security protocol for IEEE 802.11 wireless networks standardized as a substitute for WEP in 2003, and uses RC4 stream cipher for encryption. It improved a 16-byte RC4 key generation procedure, which is known as TKIP, from that in WEP. One of the remarkable features in TKIP is that the first 3-byte RC4 key is derived from the public parameter IV, and an analysis using this feature has been reported by Sen Gupta et al. at FSE 2014. They focused on correlations between the keystream bytes and the known RC4 key bytes in WPA, which are called key correlations or linear correlations, and improved the existing plaintext recovery attack using their discovered correlations. No study, however, has focused on such correlations including the internal states in WPA. In this paper, we investigated new linear correlations including unknown internal state variables in both generic RC4 and WPA. From the result, we can successfully discover various new linear correlations, and prove some correlations theoretically.

The Virtual Machine Consolidation (VMC) algorithm is the core strategy of virtualization resource management software. In general, VMC efficiency dictates cloud datacenter efficiency to a great extent. However, all the current Virtual Machine (VM) consolidation strategies, including the Iterative Correlation Match Algorithm (ICMA), are not suitable for the dynamic VM consolidation of the level of physical servers in actual datacenter environments. In this paper, we propose two VM consolidation and placement strategies which are called standard Segmentation Iteration Correlation Combination (standard SICC) and Multi-level Segmentation Iteration Correlation Combination (multi-level SICC). The standard SICC is suitable for the single-size VM consolidation environment and is the cornerstone of multi-level SICC which is suitable for the multi-size VM consolidation environment. Numerical simulation results indicate that the numbers of remaining Consolidated VM (CVM), which are generated by standard SICC, are 20% less than the corresponding parameters of ICMA in the single-level VM environment with the given initial condition. The numbers of remaining CVMs of multi-level SICC are 14% less than the corresponding parameters of ICMA in the multi-level VM environment. Furthermore, the used physical servers of multi-level SICC are also 5% less than the used servers of ICMA under the given initial condition.

We propose a computing method for linear convolution and linear correlation between sequences using discrete cosine transform (DCT). Zero-padding is considered as well as linear convolution using discrete Fourier transform (DFT). Analyzing the circular convolution between symmetrically extended sequences, we derive the condition for zero-padding before and after the sequences. The proposed method can calculate linear convolution for any filter and also calculate linear correlation without reversing one of the input sequences. The computational complexity of the proposed method is lower than that of linear convolution using DFT.

In this study, an expression of the regression relationship with less information loss is concretely derived in the form suitable to the existence of amplitude constraint of the observed data and the prediction of response probability distribution. The effectiveness of the proposed method is confirmed experimentally by applying it to the actual acoustic data.