# Attacks and Preventive Measures on Video Surveillance Systems: A Review
## 4 Review and Analysis of Existing VSS Frameworks
The author proposed a randomized capturing approach, which makes it impossible for attackers to extract original information data from the encoded frames. This denies information required by the attackers for building a crypto-analysis model. This algorithm is secure as an attacker obtains any meaningful information from the encoded image. The bootstrapping method reduces the bandwidth, transmission and storage cost as well as gives ample time for the security analysts to detect any suspicious activity. [PERSON] et al. [55] have introduced a deep learning model to measure the classification accuracy of inserted malicious data as well as the side-channel attacks performed in a video surveillance system. An autoencoder and image-specific convolutional neural network (CNN) classification models were used, which are not limited to image datasets. NSL-KDD and an image dataset are first constructed in an adversarial sample with MNIST with a dataset that is first constructed. The samples were created using the \"Jacobian-based saliency map attack (JSMA) method and the Fast Gradient Sign Method (FGSM)\", in which autoencoder validation experiments and CNN-based classification models were created using the Tensor Flow and PyTorch libraries, to assess the risks of adversarial attacks. A predefined set of images is trained using the CNN algorithm and JSMA/FGSM is applied to extract the image dataset to determine side-channel attacks and image content alteration. [PERSON] et al. [56] proposed a secure surveillance framework based on IoT systems by video streaming integration and the encryption of image algorithms. A highly efficient video encoding scheme is used to extract information from the frames using visual sensors to reduce redundant video processing. When an informative event is identified from key frames, a warning should be issued to concerned authorities autonomously. Event identification-based decision depends on the encoded key frames, but if modification occurs due to attacks during transmission, it can result in severe data losses. To overcome this issue, the author proposed a high speed and lightweight crypto algorithm for key frame encryption before transmission, which modifies the originality of key frames to break attacks, which in turn makes this highly suitable IoT-based systems. Table 4 summarizes the existing methods optimizing the video stream with multiple nodes in the network architecture and methods used for securing VSS. Details such as methodology, issues addressed, advantages and disadvantages of each method are tabulated. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author(s)** & **Methodology** & **Issues Addressed** & **Advantages** & **Disadvantages** \\ \hline \multirow{6}{*}{[PERSON] et al. [54]} & The background & & Proposed a randomized & \multirow{2}{*}{\
\begin{tabular}{c} Deals with large \\ amount of surveillance \\ data to decide about \\ abnormal and \\ suspicious \\ activity detection. \\ \end{tabular} } \\  & bootstrapping method is & & & \\ applied, which eliminates & & & \\ background motion and the & & & \\ need to precisely estimate & & & \\ the salient motion. & & & \\ \hline \multirow{6}{*}{[PERSON] et al. [55]} & The adversarial attack & & The proposed experimental & \multirow{2}{*}{
\begin{tabular}{c} Both the models used \\ have a significant \\ reduction in \\ classification accuracy \\ by using \\ adversarial samples. \\ \end{tabular} } \\  & method is used for finding & & & \\ vulnerability by employing a & & & \\ deep learning library for & & & \\ multimedia VSS. & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of literature review on VSS security and optimization methods.