DoS (Denial of Service) attacks are becoming one of the most serious security threats to global networks. We analyze the existing DoS detection methods and defense mechanisms in depth. In recent years, K-Means and improved variants have been widely examined for security intrusion detection, but the detection accuracy to data is not satisfactory. In this paper we propose a multi-dimensional space feature vector expansion K-Means model to detect threats in the network environment. The model uses a genetic algorithm to optimize the weight of K-Means multi-dimensional space feature vector, which greatly improves the detection rate against 6 typical Dos attacks. Furthermore, in order to verify the correctness of the model, this paper conducts a simulation on the NSL-KDD data set. The results show that the algorithm of multi-dimensional space feature vectors expansion K-Means improves the recognition accuracy to 96.88%. Furthermore, 41 kinds of feature vectors in NSL-KDD are analyzed in detail according to a large number of experimental training. The feature vector of the probability positive return of security attack detection is accurately extracted, and a comparison chart is formed to support subsequent research. A theoretical analysis and experimental results show that the multi-dimensional space feature vector expansion K-Means algorithm has a good application in the detection of DDos attacks.

In order to reduce PtSi Schottky barrier height (SBH) for electron, we investigated modulation of PtSi work function by alloying with low work function metal, such as Hf (3.9 eV) and Yb (2.7 eV). Pt (10-20 nm)/Hf, Yb (0-10 nm)/n-Si(100) stacked structures were in-situ deposited at room temperature by RF magnetron sputtering method. In case of PtxHf1 - xSi formed at 400/60 min annealing in N2, SBH for electron was reduced from 0.85 eV to 0.53 eV with Hf thickness without increase of sheet resistance. Yb incorporation also affected the SBH modulation, however, the sheet resistance increased with increase of Yb thickness.

The current orthogonal cooperative system (OCS) achieves diversity through the use of relays and the consumption of an additional time slot (TS). To guarantee the orthogonality of the received signal and avoid the mutual interference at the destination, the source has to be mute in the second TS. Consequently, the spectral efficiency (SE) is halved. In this paper, linear constellation precoded orthogonal frequency division multiplexing with index modulation (LCP-OFDM-IM) based OCS is proposed, where the source activates the complementary subcarriers to convey the symbols over two TSs. Hence the source can consecutively transmit information to the destination without the mutual interference. Compared with the current OFDM based OCS, the LCP-OFDM-IM based OCS can achieve a higher SE, since the subcarrier activation patterns (SAPs) can be exploited to convey additional information. Furthermore, the optimal precoder, in the sense of maximizing the minimum Euclidean distance of the symbols conveyed on each subcarrier over two TSs, is provided. Simulation results show the superiority of the LCP-OFDM-IM based OCS over the current OFDM based OCS.

A low complexity channel estimation scheme using data-dependent superimposed training (DDST) is proposed in this paper, where the pilots are inserted in more than one block, rather than the single block of the original DDST. Comparing with the original DDST (which improves the performance of channel estimation at the cost of huge computational overheads), the proposed DDST scheme improves the performance of channel estimation with only a slight increase in the consumption of computation resources. The optimal precoder is designed to minimize the data distortion caused by the rank-deficient precoding. The optimal pilots and placement are also provided to improve the performance of channel estimation. In addition, the impact of power allocation between the data and pilots on symbol detection is analyzed, the optimal power allocation scheme is derived to maximize the effective signal-to-noise ratio at the receiver. Simulation results are presented to show the computational advantage of the proposed scheme, and the advantages of the optimal pilots and power allocation scheme.

This paper proposes a Reduced Explicitly Parallel Instruction Computing Processor (REPICP) which is an independently designed, 64-bit, general-purpose microprocessor. The REPICP based on EPIC architecture overcomes the disadvantages of hardware-based superscalar and software-based Very Long Instruction Word (VLIW) and utilizes the cooperation of compiler and hardware to enhance Instruction-Level Parallelism (ILP). In REPICP, we propose the Optimized Lock-Step execution Model (OLSM) and instruction control pipeline method. We also propose reduced innovative methods to optimize the design. The REPICP is fabricated in Artisan 0.13 µm Nominal 1P8M process with 57 M transistors. The die size of the REPICP is 100 mm2 (1010), and consumes only 12 W power when running at 300 MHz.

In recent years, with the continuous development of the Internet of Things, radio frequency identification (RFID) technology has also been widely concerned. The computing power of low cost tags is limited because of their high hardware requirements. Symmetric encryption algorithms and asymmetric encryption algorithms, such as RSA, DES, AES, etc., cannot be suitable for low cost RFID protocols. Therefore, research on RFID security authentication protocols with low cost and high security has become a focus. Recently, an ultralightweight RFID authentication protocol LP2UF was proposed to provide security and prevent all possible attacks. However, it is discovered that a type of desynchronization attack can successfully break the proposed scheme. To overcome the vulnerability against desynchronization attacks, we propose here a new ultra-lightweight RFID two-way authentication protocol based on stream cipher technology that uses only XOR. The stream cipher is employed to ensure security between readers and tags. Analysis shows that our protocol can effectively resist position tracking attacks, desynchronization attacks, and replay attacks.