Biblio

The integration of advanced information, communication and data analytic technologies has transformed the traditional grid into an intelligent bidirectional system that can automatically adapt its services for utilities or consumers' needs. However, this change raises new privacy-related challenges. Privacy leakage has become a severe issue in the grid paradigm as adversaries run malicious analytics to identify the system's internal insight or use it to interrupt grids' operation by identifying real-time demand-based supply patterns. As a result, current grid authorities require an integrated mechanism to improve the system's sensitive data's privacy preservation. To this end, we present a multilayered smart grid architecture by characterizing the privacy issues that occur during data sharing, aggregation, and publishing by individual grid end nodes. Based on it, we quantify the nodes preferred privacy requirements. We further introduce personalized differential privacy (PDP) scheme based on trust distance in our proposed framework to provide the system with the added benefit of a user-specific privacy guarantee to eliminate differential privacy's limitation that allows the same level of privacy for all data providers. Lastly, we conduct extensive experimental analysis on a real-world grid dataset to illustrate that our proposed method is efficient enough to provide privacy preservation on sensitive smart grid data.

Today's global network is filled with attackers both live and automated seeking to identify and compromise vulnerable devices, with initial scanning and attack activity occurring within minutes or even seconds of being connected to the Internet. To better understand these events, honeypots can be deployed to monitor and log activity by simulating actual Internet facing services such as SSH, Telnet, HTTP, or FTP, and malicious activity can be logged as attempts are made to compromise them. In this study six multi-service honeypots are deployed in locations around the globe to collect and catalog traffic over a period of several months between March and December, 2020. Analysis is performed on various characteristics including source and destination IP addresses and port numbers, usernames and passwords utilized, commands executed, and types of files downloaded. In addition, Cowrie log data is restructured to observe individual attacker sessions, study command sequences, and monitor tunneling activity. This data is then correlated across honeypots to compare attack and traffic patterns with the goal of learning more about the tactics being employed. By gathering data gathered from geographically separate zones over a long period of time a greater understanding can be developed regarding attacker intent and methodology, can aid in the development of effective approaches to identifying malicious behavior and attack sources, and can serve as a cyber-threat intelligence feed.

Signature-based malware detection is not always effective at detecting polymorphic variants of known malware. Malware signatures are devised to counter known threats, which also limits efficacy against new forms of malware. However, existing signatures do present the ability to classify malware based upon known malicious behavior which occurs on a victim computer. In this paper we present a method of classifying malware by family type through behavioral analysis, where the frequency of system function calls is used to fingerprint the actions of specific malware families. This in turn allows us to demonstrate a machine learning classifier which is capable of distinguishing malware by family affiliation with high accuracy.

The most common defenses against malware threats involves the use of signatures derived from instances of known malware. However, the constant evolution of the malware threat landscape necessitates defense against unknown malware, making a signature catalog of known threats insufficient to prevent zero-day vulnerabilities from being exploited. Recent research has applied machine learning approaches to identify malware through artifacts of malicious activity as observed through dynamic behavioral analysis. We have seen that these approaches mimic common malware defenses by simply offering a method of detecting known malware. We contribute a new method of identifying software as malicious or benign through analysis of the frequency of Windows API system function calls. We show that this is a powerful technique for malware detection because it generates learning models which understand the difference between malicious and benign software, rather than producing a malware signature classifier. We contribute a method of systematically comparing machine learning models against different datasets to determine their efficacy in accurately distinguishing the difference between malicious and benign software.

Rapid evolution of cyber threats and recent trends in the increasing number of cyber-attacks call for adopting robust and agile cybersecurity techniques. Cybersecurity information sharing is expected to play an effective role in detecting and defending against new attacks. However, reservations and or-ganizational policies centering the privacy of shared data have become major setbacks in large-scale collaboration in cyber defense. The situation is worsened by the fact that the benefits of cyber-information exchange are not realized unless many actors participate. In this paper, we argue that privacy preservation of shared threat data will motivate entities to share threat data. Accordingly, we propose a framework called CYBersecurity information EXchange with Privacy (CYBEX-P) to achieve this. CYBEX-P is a structured information sharing platform with integrating privacy-preserving mechanisms. We propose a complete system architecture for CYBEX-P that guarantees maximum security and privacy of data. CYBEX-P outlines the details of a cybersecurity information sharing platform. The adoption of blind processing, privacy preservation, and trusted computing paradigms make CYBEX-P a versatile and secure information exchange platform.

The goal of this work was to identify and try to solve some of the vulnerabilities present in the AR Drone 2.0 by Parrot. The approach was to identify how the system worked, find and analyze vulnerabilities and flaws in the system as a whole and in the software, and find solutions to those problems. Analyzing the results of some tests showed that the system has an open WiFi network and the communication between the controller and the drone are unencrypted. Analyzing the Linux operating system that the drone uses, we see that "Pairing Mode" is the only way the system protects itself from unauthorized control. This is a feature that can be easily bypassed. Port scans reveal that the system has all the ports for its services open and exposed. This makes it susceptible to attacks like DoS and takeover. This research also focuses on some of the software vulnerabilities, such as Busybox that the drone runs. Lastly, this paper discuses some of the possible methods that can be used to secure the drone. These methods include securing the messages via SSH Tunnel, closing unused ports, and re-implementing the software used by the drone and the controller.

Malware threat classification involves understanding the behavior of the malicious software and how it affects a victim host system. Classifying threats allows for measured response appropriate to the risk involved. Malware incident response depends on many automated tools for the classification of threat to help identify the appropriate reaction to a threat alert. Cuckoo Sandbox is one such tool which can be used for automated analysis of malware and one method of threat classification provided is a threat score. A security analyst might submit a suspicious file to Cuckoo for analysis to determine whether or not the file contains malware or performs potentially malicious behavior on a system. Cuckoo is capable of producing a report of this behavior and ranks the severity of the observed actions as a score from one to ten, with ten being the most severe. As such, a malware sample classified as an 8 would likely take priority over a sample classified as a 3. Unfortunately, this scoring classification can be misleading due to the underlying methodology of severity classification. In this paper we demonstrate why the current methodology of threat scoring is flawed and therefore we believe it can be improved with greater emphasis on analyzing the behavior of the malware. This allows for a threat classification rating which scales with the risk involved in the malware behavior.