Biblio

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2021-05-13
Jain, Harsh, Vikram, Aditya, Mohana, Kashyap, Ankit, Jain, Ayush.  2020.  Weapon Detection using Artificial Intelligence and Deep Learning for Security Applications. 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC). :193—198.
Security is always a main concern in every domain, due to a rise in crime rate in a crowded event or suspicious lonely areas. Abnormal detection and monitoring have major applications of computer vision to tackle various problems. Due to growing demand in the protection of safety, security and personal properties, needs and deployment of video surveillance systems can recognize and interpret the scene and anomaly events play a vital role in intelligence monitoring. This paper implements automatic gun (or) weapon detection using a convolution neural network (CNN) based SSD and Faster RCNN algorithms. Proposed implementation uses two types of datasets. One dataset, which had pre-labelled images and the other one is a set of images, which were labelled manually. Results are tabulated, both algorithms achieve good accuracy, but their application in real situations can be based on the trade-off between speed and accuracy.
2020-04-13
Nalamati, Mrunalini, Kapoor, Ankit, Saqib, Muhammed, Sharma, Nabin, Blumenstein, Michael.  2019.  Drone Detection in Long-Range Surveillance Videos. 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). :1–6.

The usage of small drones/UAVs has significantly increased recently. Consequently, there is a rising potential of small drones being misused for illegal activities such as terrorism, smuggling of drugs, etc. posing high-security risks. Hence, tracking and surveillance of drones are essential to prevent security breaches. The similarity in the appearance of small drone and birds in complex background makes it challenging to detect drones in surveillance videos. This paper addresses the challenge of detecting small drones in surveillance videos using popular and advanced deep learning-based object detection methods. Different CNN-based architectures such as ResNet-101 and Inception with Faster-RCNN, as well as Single Shot Detector (SSD) model was used for experiments. Due to sparse data available for experiments, pre-trained models were used while training the CNNs using transfer learning. Best results were obtained from experiments using Faster-RCNN with the base architecture of ResNet-101. Experimental analysis on different CNN architectures is presented in the paper, along with the visual analysis of the test dataset.

2020-07-10
Xiao, Tianran, Tong, Wei, Lei, Xia, Liu, Jingning, Liu, Bo.  2019.  Per-File Secure Deletion for Flash-Based Solid State Drives. 2019 IEEE International Conference on Networking, Architecture and Storage (NAS). :1—8.

File update operations generate many invalid flash pages in Solid State Drives (SSDs) because of the-of-place update feature. If these invalid flash pages are not securely deleted, they will be left in the “missing” state, resulting in leakage of sensitive information. However, deleting these invalid pages in real time greatly reduces the performance of SSD. In this paper, we propose a Per-File Secure Deletion (PSD) scheme for SSD to achieve non-real-time secure deletion. PSD assigns a globally unique identifier (GUID) to each file to quickly locate the invalid data blocks and uses Security-TRIM command to securely delete these invalid data blocks. Moreover, we propose a PSD-MLC scheme for Multi-Level Cell (MLC) flash memory. PSD-MLC distributes the data blocks of a file in pairs of pages to avoid the influence of programming crosstalk between paired pages. We evaluate our schemes on different hardware platforms of flash media, and the results prove that PSD and PSD-MLC only have little impact on the performance of SSD. When the cache is disabled and enabled, compared with the system without the secure deletion, PSD decreases SSD throughput by 1.3% and 1.8%, respectively. PSD-MLC decreases SSD throughput by 9.5% and 10.0%, respectively.

2020-02-17
Meijer, Carlo, van Gastel, Bernard.  2019.  Self-Encrypting Deception: Weaknesses in the Encryption of Solid State Drives. 2019 IEEE Symposium on Security and Privacy (SP). :72–87.
We have analyzed the hardware full-disk encryption of several solid state drives (SSDs) by reverse engineering their firmware. These drives were produced by three manufacturers between 2014 and 2018, and are both internal models using the SATA and NVMe interfaces (in a M.2 or 2.5" traditional form factor) and external models using the USB interface. In theory, the security guarantees offered by hardware encryption are similar to or better than software implementations. In reality, we found that many models using hardware encryption have critical security weaknesses due to specification, design, and implementation issues. For many models, these security weaknesses allow for complete recovery of the data without knowledge of any secret (such as the password). BitLocker, the encryption software built into Microsoft Windows will rely exclusively on hardware full-disk encryption if the SSD advertises support for it. Thus, for these drives, data protected by BitLocker is also compromised. We conclude that, given the state of affairs affecting roughly 60% of the market, currently one should not rely solely on hardware encryption offered by SSDs and users should take additional measures to protect their data.
2017-08-02
Nguyen, Trong-Dat, Lee, Sang-Won.  2016.  I/O Characteristics of MongoDB and Trim-based Optimization in Flash SSDs. Proceedings of the Sixth International Conference on Emerging Databases: Technologies, Applications, and Theory. :139–144.

NoSQL solutions become emerging for large scaled, high performance, schema-flexible applications. WiredTiger is cost effective, non-locking, no-overwrite storage used as default storage engine in MongoDB. Understanding I/O characteristics of storage engine is important not only for choosing suitable solution with an application but also opening opportunities for researchers optimizing current working system, especially building more flash-awareness NoSQL DBMS. This paper explores background of MongoDB internals then analyze I/O characteristics of WiredTiger storage engine in detail. We also exploit space management mechanism in WiredTiger by using TRIM command.