Biblio

Live Linux distribution devices can hold Linux operating system for portability. Using such devices and distributions, one can access system or critical files, which otherwise cannot be accessed by guest or any unauthorized user. Events like file leakage before the official announcement. These announcements can vary from mobile companies to software industries. Damages caused by such vulnerabilities can be data theft, data tampering, or permanent deletion of certain records. This study uncovers the security flaws of operating system against live device attacks. For this study, we used live devices with different Linux distributions. Target operating systems are exposed to live device attacks and their behavior is recorded against different Linux distribution. This study also compares the robustness level of different operating system against such attacks.

Data can be stored securely in various storage servers. But in the case of a server failure, or data theft from a certain number of servers, the remaining data becomes inadequate for use. Data is stored securely using secret sharing schemes, so that data can be reconstructed even if some of the servers fail. But not much work has been carried out in the direction of updation of this data. This leads to the problem of updation when two or more concurrent requests arrive and thus, it results in inconsistency. Our work proposes a novel method to store data securely with concurrent update requests using Petri Nets, under the assumption that the number of nodes is very large and the requests for updates are very frequent.

Fully connected autonomous vehicles are more vulnerable than ever to hacking and data theft. The controller area network (CAN) protocol is used for communication between in-vehicle control networks (IVN). The absence of basic security features of this protocol, like message authentication, makes it quite vulnerable to a wide range of attacks including spoofing attacks. As traditional cybersecurity methods impose limitations in ensuring confidentiality and integrity of transmitted messages via CAN, a new technique has emerged among others to approve its reliability in fully authenticating the CAN messages. At the physical layer of the communication system, the method of fingerprinting the messages is implemented to link the received signal to the transmitting electronic control unit (ECU). This paper introduces a new method to implement the security of modern electric vehicles. The lumped element model is used to characterize the channel-specific step response. ECU and channel imperfections lead to a unique transfer function for each transmitter. Due to the unique transfer function, the step response for each transmitter is unique. In this paper, we use control system parameters as a feature-set, afterward, a neural network is used transmitting node identification for message authentication. A dataset collected from a CAN network with eight-channel lengths and eight ECUs to evaluate the performance of the suggested method. Detection results show that the proposed method achieves an accuracy of 97.4% of transmitter detection.

According to recent reports, the most common insider threats to systems are unauthorized access to or use of corporate information and exposure of sensitive data. While anomaly detection techniques have proved to be effective in the detection of early signs of data theft, these techniques are not able to detect sophisticated data misuse scenarios in which malicious insiders seek to aggregate knowledge by executing and combining the results of several queries. We thus need techniques that are able to track users' actions across time to detect correlated ones that collectively flag anomalies. In this paper, we propose such techniques for the detection of anomalous accesses to relational databases. Our approach is to monitor users' queries, sequences of queries and sessions of database connection to detect queries that retrieve amounts of data larger than the normal. Our evaluation of the proposed techniques indicates that they are very effective in the detection of anomalies.

Organizations not only need to defend their IT systems against external cyber attackers, but also from malicious insiders, that is, agents who have infiltrated an organization or malicious members stealing information for their own profit. In particular, malicious insiders can leak a document by simply opening it and taking pictures of the document displayed on the computer screen with a digital camera. Using a digital camera allows a perpetrator to easily avoid a log trail that results from using traditional communication channels, such as sending the document via email. This makes it difficult to identify and prove the identity of the perpetrator. Even a policy prohibiting the use of any device containing a camera cannot eliminate this threat since tiny cameras can be hidden almost everywhere. To address this leakage vector, we propose a novel screen watermarking technique that embeds hidden information on computer screens displaying text documents. The watermark is imperceptible during regular use, but can be extracted from pictures of documents shown on the screen, which allows an organization to reconstruct the place and time of the data leak from recovered leaked pictures. Our approach takes advantage of the fact that the human eye is less sensitive to small luminance changes than digital cameras. We devise a symbol shape that is invisible to the human eye, but still robust to the image artifacts introduced when taking pictures. We complement this symbol shape with an error correction coding scheme that can handle very high bit error rates and retrieve watermarks from cropped and compressed pictures. We show in an experimental user study that our screen watermarks are not perceivable by humans and analyze the robustness of our watermarks against image modifications.

Due to the growing advancement of crime ware services, the computer and network security becomes a crucial issue. Detecting sensitive data exfiltration is a principal component of each information protection strategy. In this research, a Multi-Level Data Exfiltration Detection (MLDED) system that can handle different types of insider data leakage threats with staircase difficulty levels and their implications for the organization environment has been proposed, implemented and tested. The proposed system detects exfiltration of data outside an organization information system, where the main goal is to use the detection results of a MLDED system for digital forensic purposes. MLDED system consists of three major levels Hashing, Keywords Extraction and Labeling. However, it is considered only for certain type of documents such as plain ASCII text and PDF files. In response to the challenging issue of identifying insider threats, a forensic readiness data exfiltration system is designed that is capable of detecting and identifying sensitive information leaks. The results show that the proposed system has an overall detection accuracy of 98.93%.

Efficient and secure search on encrypted data is an important problem in computer science. Users having large amount of data or information in multiple documents face problems with their storage and security. Cloud services have also become popular due to reduction in cost of storage and flexibility of use. But there is risk of data loss, misuse and theft. Reliability and security of data stored in the cloud is a matter of concern, specifically for critical applications and ones for which security and privacy of the data is important. Cryptographic techniques provide solutions for preserving the confidentiality of data but make the data unusable for many applications. In this paper we report a novel approach to securely store the data on a remote location and perform search in constant time without the need for decryption of documents. We use bloom filters to perform simple as well advanced search operations like case sensitive search, sentence search and approximate search.