Aiming at the plaintext recovery on plaintexts encrypted by RC4 (Rivest Cipher 4) algorithm with different lengths of seed key, a plaintext recovery attack on plaintexts encrypted by RC4 algorithm with different lengths of seed key (8 bytes, 16 bytes, 22 bytes) was proposed. Firstly, by using the statistical algorithm, the t-value distribution of each output byte of key stream of RC4 was calculated under the condition of 2 ³² different seed keys, and biases were found. Then the attack on the first 256 bytes of the plaintext encrypted by the RC4 was given by using single-byte biases and double-bytes biases. The experimental results show that with 2 ³¹ ciphertexts, the first 196 bytes of the plaintext can be recovered with the success probability of 100% except the 4th Byte. Besides, the first 256 bytes can be recovered with the success probability over 91%, 87% and 81% for 8-byte, 16-byte and 22-byte seed key, respectively. The proposed attack algorithm extends the scope of RC4 algorithm with seed key length of 16 bytes, and it can recover the plaintexts encrypted by RC4 algorithm in practice.

The properties of the measured objects in 3D profile using the grating projection are more and more complex, there are a large number of splits in the extracted refinement grating stripes, and the refinement stripe encoding is very difficult. An automatic coding algorithm based on color structure light was proposed. The paper designed a new model of color structure light, introduced its design principle and implemented a new automatic stripe coding algorithm. First, the algorithm extracted the refinement grating stripe with color information from the color structure grating. Then, orderly encoded the refined stripes of each color by judging the best connected domain. Finally, the article got the stripe coding of the total image through combined coding by using the periodicity of grating model. The simulation experiment results show that the model design of color structure light is simple, the automatic coding algorithm of stripe has high accuracy and the error is decreased to 10 percent. The ideal 3D points cloud data model can be reconstructed through the strip coded data.

The cluster number is not generally set by K-means clustering algorithm beforehand, and artificial initial clustering number easily leads to the problem of unstable clustering results. A high-efficient algorithm for determining the K-means optimal clustering number was presented. The algorithm got the upper bound of the number of clustering search range through stratified sample data and designed a new kind of effective clustering indicator to evaluate the clustering degree of similarity between and within class after clustering. Thus the optimal number of clusters was obtained in the search range of the clusters number. The simulation results show that the algorithm can obtain the optimal clustering number fast and accurately, and the dataset clustering effect is good.

Bad Mouthing and Self-Promoting (BS) collusion attack group and its detection mechanism, called BSCD, were proposed to resolve the security issues of the multiple malicious node collusion attack network nodes and affect their accurate positioning in the Wireless Sensor Network (WSN) reputation system. And the implementation method of the mechanism was given. It detected the abnormal recommended node, analyzed the evaluation behavior similarity between recommended nodes, and effectively detected the existence of collusion attack group, thereby reduced its damage and impact on the reputation of the system. The simulation results show that, BSCD has significant effect on the detection and resisting BS collusion attack group, effectively improves the malicious node detection rate in the reputation system and the capacity of the entire system to resist malicious node.

A new low-power localization algorithm based on the evaluation of distributed reputation was proposed to improve the security and energy consumption of the node positioning for wireless sensor network. The concepts of Trustworthy Node Table (TNT) and the backup cluster head node were introduced to find the reliable beacon nodes quickly, and the backup cluster head node could assist and monitor the cluster head node, reducing the workload of the cluster head and participating in the integration process of the beacon nodes reputation values. The proposed algorithm enhanced the reliability and integrity of the beacon nodes, improved the efficiency and security of the node localization, reduced the systems energy consumption and improved the detection rate of malicious nodes. The simulation results show that in malicious node environment, the algorithm can effectively improve the detection rate of malicious nodes, reduce the positioning error, weaken the malicious nodes damage and influence on the positioning system to achieve the safe positioning of the nodes.