A Metaheuristic Approach to Secure Multimedia Big Data for IoT‐Based Smart City Applications

Abstract

Media streaming falls into the category of Big Data. Regardless of the video duration, an enormous amount of information is encoded in accordance with standardized algorithms of videos. In the transmission of videos, the intended recipient is allowed to receive a copy of the broadcasted video; however, the adversary also has access to it which poses a serious concern to the data confidentiality and availability. In this paper, a cryptographic algorithm, Advanced Encryption Standard, is used to conceal the information from malicious intruders. However, in order to utilize fewer system resources, video information is compressed before its encryption. Various compression algorithms such as Discrete Cosine Transform, Integer Wavelet transforms, and Huffman coding are employed to reduce the enormous size of videos. moving picture expert group is a standard employed in video broadcasting, and it constitutes of different frame types, viz., I, B, and P frames. Later, two frame types carry similar information as of foremost type. Even I frame is to be processed and compressed with the abovementioned schemes to discard any redundant information from it. However, I frame embraces an abundance of new information; thus, encryption of this frame is sufficient enough to safeguard the whole video. The introduction of various compression algorithms can further increase the encryption time of one frame. The performance parameters such as PSNR and compression ratio are examined to further analyze the proposed model’s effectiveness. Therefore, the presented approach has superiority over the other schemes when the speed of encryption and processing of data are taken into consideration. After the reversal of the complete system, we have observed no major impact on the quality of the deciphered video. Simulation results ensure that the presented architecture is an efficient method for enciphering the video information.

Study on Library Management System Based on Data Mining and Clustering Algorithm

“…In order to realize the optimized, develop and design library management system [18][19][20][21][22][23][24][25], analyze with data mining technology flow characteristics of the library management, and the resource distribution characteristics of the library are analyzed in the cloud computing environment [26][27][28][29], which mainly includes the characteristics of associated knowledge base information [30], computing resources [31][32][33], physical resources and logical resources [34-38. The data flow resource distribution of library management see Figure 2.…”

Big Data Intelligent Collection and Network Failure Analysis Based on Artificial Intelligence

“…Select the set j S of output neurons j "adjacent neurons". Among them,   0 j S represents the "adjacent neuron" set of neuron j at time t=0,   t S j represents the set of "adjacent neurons" at time t. Area   t S j keeps shrinking with time [16] . ② Provide a new input mode X.…”

“…Lei, Y. will first introduce big data and artificial intelligence, after analyzing the application of artificial intelligence in computer networks in the current era of big data, in this way, it can be used as a reference for relevant people to communicate [13]. Xia, M. Can use the effective application of artificial intelligence virus detection and killing technology in enterprise information security situation awareness system can realize the effective identification and timely detection and killing of virus, and reduce the damage caused by virus to computer system [14][15][16][17][18].Because these studies have large loopholes, or the detection is not comprehensive enough, this paper proposes a method based on artificial intelligence on the basis of existing studies [19][20][21][22][23][24][25][26][27]. The design and deployment of the system, effective detection, threat perception, determination and threat risk tracking of information security threats can realize the comprehensive improvement of enterprises and their ability to detect security threats and security attacks [28][29][30][31][32][33][34][35].…”

Design and Study of Urban Rail Transit Security System Based on Face Recognition Technology

“…Although face recognition technology is in continuous development and improvement [13][14][15][16][17][18][19][20], the recognition rate, anti-counterfeiting, fingerprint, retina, and other has a large gap [21][22][23][24][25]. The large passenger flow and complex environmental characteristics of urban rail transit also affect the application of face recognition technology in urban rail transit.…”

“…Face tracking function was tested for different face conditions, including single face, multiple faces, and face in and out or interleaved [50]. The system automatically initializes a tracking window before the face tracking starts, and assigns a Camshift tracker to each face in the video surveillance to realize multiple face recognition.…”