Biblio

The application of line current differential relays (LCDRs) to protect transmission lines has recently proliferated. However, the reliance of LCDRs on digital communication channels has raised growing cyber-security concerns. This paper investigates the impacts of false data injection attacks (FDIAs) on the performance of LCDRs. It also develops coordinated attacks that involve multiple components, including LCDRs, and can cause false line tripping. Additionally, this paper proposes a technique for detecting FDIAs against LCDRs and differentiating them from actual faults in two-terminal lines. In this method, when an LCDR detects a fault, instead of immediately tripping the line, it calculates and measures the superimposed voltage at its local terminal, using the proposed positive-sequence (PS) and negative-sequence (NS) submodules. To calculate this voltage, the LCDR models the protected line in detail and replaces the rest of the system with a Thevenin equivalent that produces accurate responses at the line terminals. Afterwards, remote current measurement is utilized by the PS and NS submodules to compute each sequence's superimposed voltage. A difference between the calculated and the measured superimposed voltages in any sequence reveals that the remote current measurements are not authentic. Thus, the LCDR's trip command is blocked. The effectiveness of the proposed method is corroborated using simulation results for the IEEE 39-bus test system. The performance of the proposed method is also tested using an OPAL real-time simulator.

Cybersecurity plays a critical role in protecting sensitive information and the structural integrity of networked systems. As networked systems continue to expand in numbers as well as in complexity, so does the threat of malicious activity and the necessity for advanced cybersecurity solutions. Furthermore, both the quantity and quality of available data on malicious content as well as the fact that malicious activity continuously evolves makes automated protection systems for this type of environment particularly challenging. Not only is the data quality a concern, but the volume of the data can be quite small for some of the classes. This creates a class imbalance in the data used to train a classifier; however, many classifiers are not well equipped to deal with class imbalance. One such example is detecting malicious HMTL files from static features. Unfortunately, collecting malicious HMTL files is extremely difficult and can be quite noisy from HTML files being mislabeled. This paper evaluates a specific application that is afflicted by these modern cybersecurity challenges: detection of malicious HTML files. Previous work presented a general framework for malicious HTML file classification that we modify in this work to use a $\chi$2 feature selection technique and synthetic minority oversampling technique (SMOTE). We experiment with different classifiers (i.e., AdaBoost, Gentle-Boost, RobustBoost, RusBoost, and Random Forest) and a pure detection model (i.e., Isolation Forest). We benchmark the different classifiers using SMOTE on a real dataset that contains a limited number of malicious files (40) with respect to the normal files (7,263). It was found that the modified framework performed better than the previous framework's results. However, additional evidence was found to imply that algorithms which train on both the normal and malicious samples are likely overtraining to the malicious distribution. We demonstrate the likely overtraining by determining that a subset of the malicious files, while suspicious, did not come from a malicious source.

The dynamic nature of the Web 2.0 and the heavy obfuscation of web-based attacks complicate the job of the traditional protection systems such as Firewalls, Anti-virus solutions, and IDS systems. It has been witnessed that using ready-made toolkits, cyber-criminals can launch sophisticated attacks such as cross-site scripting (XSS), cross-site request forgery (CSRF) and botnets to name a few. In recent years, cyber-criminals have targeted legitimate websites and social networks to inject malicious scripts that compromise the security of the visitors of such websites. This involves performing actions using the victim browser without his/her permission. This poses the need to develop effective mechanisms for protecting against Web 2.0 attacks that mainly target the end-user. In this paper, we address the above challenges from information flow control perspective by developing a framework that restricts the flow of information on the client-side to legitimate channels. The proposed model tracks sensitive information flow and prevents information leakage from happening. The proposed model when applied to the context of client-side web-based attacks is expected to provide a more secure browsing environment for the end-user.

The dynamic nature of the Web 2.0 and the heavy obfuscation of web-based attacks complicate the job of the traditional protection systems such as Firewalls, Anti-virus solutions, and IDS systems. It has been witnessed that using ready-made toolkits, cyber-criminals can launch sophisticated attacks such as cross-site scripting (XSS), cross-site request forgery (CSRF) and botnets to name a few. In recent years, cyber-criminals have targeted legitimate websites and social networks to inject malicious scripts that compromise the security of the visitors of such websites. This involves performing actions using the victim browser without his/her permission. This poses the need to develop effective mechanisms for protecting against Web 2.0 attacks that mainly target the end-user. In this paper, we address the above challenges from information flow control perspective by developing a framework that restricts the flow of information on the client-side to legitimate channels. The proposed model tracks sensitive information flow and prevents information leakage from happening. The proposed model when applied to the context of client-side web-based attacks is expected to provide a more secure browsing environment for the end-user.

The dynamic nature of the Web 2.0 and the heavy obfuscation of web-based attacks complicate the job of the traditional protection systems such as Firewalls, Anti-virus solutions, and IDS systems. It has been witnessed that using ready-made toolkits, cyber-criminals can launch sophisticated attacks such as cross-site scripting (XSS), cross-site request forgery (CSRF) and botnets to name a few. In recent years, cyber-criminals have targeted legitimate websites and social networks to inject malicious scripts that compromise the security of the visitors of such websites. This involves performing actions using the victim browser without his/her permission. This poses the need to develop effective mechanisms for protecting against Web 2.0 attacks that mainly target the end-user. In this paper, we address the above challenges from information flow control perspective by developing a framework that restricts the flow of information on the client-side to legitimate channels. The proposed model tracks sensitive information flow and prevents information leakage from happening. The proposed model when applied to the context of client-side web-based attacks is expected to provide a more secure browsing environment for the end-user.