Anonymization of Network Traces Data through Condensation-based Differential Privacy

Network traces are considered a primary source of information to researchers, who use them to investigate research problems such as identifying user behavior, analyzing network hierarchy, maintaining network security, classifying packet flows, and much more. However, most organizations are reluctant to share their data with a third party or the public due to privacy concerns. Therefore, data anonymization prior to sharing becomes a convenient solution to both organizations and researchers. Although several anonymization algorithms are available, few of them allow sufficient privacy (organization need), acceptable data utility (researcher need), and efficient data analysis at the same time. This article introduces a condensation-based differential privacy anonymization approach that achieves an improved tradeoff between privacy and utility compared to existing techniques and produces anonymized network trace data that can be shared publicly without lowering its utility value. Our solution also does not incur extra computation overhead for the data analyzer. A prototype system has been implemented, and experiments have shown that the proposed approach preserves privacy and allows data analysis without revealing the original data even when injection attacks are launched against it. When anonymized datasets are given as input to graph-based intrusion detection techniques, they yield almost identical intrusion detection rates as the original datasets with only a negligible impact.

Deception in Network Defences Using Unpredictability

In this article, we propose a novel method that aims to improve upon existing moving-target defences by making them unpredictably reactive using probabilistic decision-making. We postulate that unpredictability can improve network defences in two key capacities: (1) by re-configuring the network in direct response to detected threats, tailored to the current threat and a security posture, and (2) by deceiving adversaries using pseudo-random decision-making (selected from a set of acceptable set of responses), potentially leading to adversary delay and failure. Decisions are performed automatically, based on reported events (e.g., Intrusion Detection System (IDS) alerts), security posture, mission processes, and states of assets. Using this codified form of situational awareness, our system can respond differently to threats each time attacker activity is observed, acting as a barrier to further attacker activities. We demonstrate feasibility with both anomaly- and misuse-based detection alerts, for a historical dataset (playback), and a real-time network simulation where asset-to-mission mappings are known. Our findings suggest that unpredictability yields promise as a new approach to deception in laboratory settings. Further research will be necessary to explore unpredictability in production environments.

Informing Cyber Threat Intelligence through Dark Web Situational Awareness: The AZSecure Hacker Assets Portal

To increase situational awareness, major cybersecurity platforms offer Cyber Threat Intelligence (CTI) about emerging cyber threats, key threat actors, and their modus operandi. However, this intelligence is often reactive, as it analyzes event log files after attacks have already occurred, lacking more active scrutiny of potential threats brewing in cyberspace before an attack has occurred. One intelligence source receiving significant attention is the Dark Web, where significant quantities of malicious hacking tools and other cyber assets are hosted. We present the AZSecure Hacker Assets Portal (HAP). The Dark Web-based HAP collects, analyzes, and reports on the major Dark Web data sources to offer unique perspective of hackers, their cybercriminal assets, and their intentions and motivations, ultimately contributing CTI insights to improve situational awareness. HAP currently supports 200+ users internationally from academic institutions such as UT San Antonio and National Taiwan University, law enforcement entities such as Calgary and Ontario Provincial Police, and industry organizations including General Electric and PayPal.

On Secure E-Voting over Blockchain

This article discusses secure methods to conduct e-voting over a blockchain in three different settings: decentralized voting, centralized remote voting, and centralized polling station voting. These settings cover almost all voting scenarios that occur in practice. A proof-of-concept implementation for decentralized voting over Ethereum’s blockchain is presented. This work demonstrates the suitable use of a blockchain not just as a public bulletin board but, more importantly, as a trustworthy computing platform that enforces the correct execution of the voting protocol in a publicly verifiable manner. We also discuss scaling up a blockchain-based voting application for national elections. We show that for national-scale elections the major verifiability problems can be addressed without having to depend on any blockchain. However, a blockchain remains a viable option to realize a public bulletin board, which has the advantage of being a “preventive” measure to stop retrospective changes on previously published records as opposed to a “detective” measure like the use of mirror websites. CCS Concepts: • Security and privacy ;

Vulnerability Forecasting: Theory and Practice

It is possible to forecast the volume of CVEs released within a time frame with a given prediction interval. For example, the number of CVEs published between now and 365 days from now can be predicted a year in advance within 8% of the actual value. Different predictive algorithms perform well at different lookahead values other than 365 days, such as monthly, quarterly, and half year. It is also possible to estimate the proportions of that total volume belonging to specific vendors, software, CVSS scores, or vulnerability types. Some vendors and products can be predicted with accuracy, others with too much uncertainty to be practically useful. This paper documents which ones are amenable to being forecasted. Strategic patch management should become much easier with these tools, and further uncertainty reductions can be built from the methodologies in this paper.

CVSS: Ubiquitous and Broken

The Common Vulnerability Scoring System is at the core of vulnerability management for systems of private corporations to highly classified government networks, allowing organizations to prioritize remediation in descending order of risk. With a lack of justification for its underlying formula, inconsistencies in its specification document, and no correlation to exploited vulnerabilities in the wild, it is unable to provide a meaningful metric for describing a vulnerability's severity, let alone risk. As it stands, this standard compromises the security of America?s most sensitive information systems.

"Off-Label" use of DNS

DNS is one of the most widely abused protocols that threat actors use to hide traffic. DNS is also actively used, or rather misused, by other service providers, vendors, etc., to provide enhanced services. An in-depth examination of DNS logs revealed several very interesting legitimate use cases of the DNS protocol, apart from the usual name resolution service function. We coined the term ?Off-label? use of DNS to represent those use cases. Legitimate here simply means using DNS for non-malicious purposes other than what it was traditionally designed for, providing domain name resolution; a dictionary service mapping domain names to corresponding IP addresses. One of the main reasons DNS is used, or possibly misused, for these off-label use cases is data transfer speed and reduced overhead. These use cases can often reveal important information about the clients and software they are running and can be leveraged by network security analysts to improve their defense of the network. This research will detail some of those legitimate off-label use cases and how analysts can use them to detect malware trends in the network and much more just by analyzing an enterprise?s DNS logs.

Scrybe: A Secure Audit Trail for Clinical Trial Data Fusion

Clinical trials are a multi-billion dollar industry. One of the biggest challenges facing the clinical trial research community is satisfying Part 11 of Title 21 of the Code of Federal Regulations and ISO 27789. These controls provide audit requirements that guarantee the reliability of the data contained in the electronic records. Context-aware smart devices and wearable IoT devices have become increasingly common in clinical trials. Electronic Data Capture (EDC) and Clinical Data Management Systems (CDMS) do not currently address the new challenges introduced using these devices. The healthcare digital threat landscape is continually evolving, and the prevalence of sensor fusion and wearable devices compounds the growing attack surface. We propose Scrybe, a permissioned blockchain, as a method of storing proof of clinical trial data provenance. We illustrate how Scrybe addresses each control and the limitations of the Ethereum-based blockchains. Finally, we provide a proof-of-concept integration with REDCap to show tamper resistance.