Malware in the Future? Forecasting of Analyst Detection of Cyber Events

There have been extensive efforts in government, academia, and industry to anticipate, forecast, and mitigate cyber attacks. A common approach is time-series forecasting of cyber attacks based on data from network telescopes, honeypots, and automated intrusion detection/prevention systems. This research has uncovered key insights such as systematicity in cyber attacks. Here, we propose an alternate perspective of this problem by performing forecasting of attacks that are analyst-detected and -verified occurrences of malware. We call these instances of malware cyber event data. Specifically, our dataset was analyst-detected incidents from a large operational Computer Security Service Provider (CSSP) for the U.S. Department of Defense, which rarely relies only on automated systems. Our data set consists of weekly counts of cyber events over approximately seven years. Since all cyber events were validated by analysts, our dataset is unlikely to have false positives which are often endemic in other sources of data. Further, the higher-quality data could be used for a number for resource allocation, estimation of security resources, and the development of effective risk-management strategies. We used a Bayesian State Space Model for forecasting and found that events one week ahead could be predicted. To quantify bursts, we used a Markov model. Our findings of systematicity in analyst-detected cyber attacks are consistent with previous work using other sources. The advanced information provided by a forecast may help with threat awareness by providing a probable value and range for future cyber events one week ahead. Other potential applications for cyber event forecasting include proactive allocation of resources and capabilities for cyber defense (e.g., analyst staffing and sensor configuration) in CSSPs. Enhanced threat awareness may improve cybersecurity.Comment: Revised version resubmitted to journa

Identifying bugs in digital forensic tools

Bugs can be found in all code and the consequences are usually managed through up-grade releases, patches, and restarting operating systems and applications. However, in mission critical systems complete fall over systems are built to assure service continuity. In our research we asked the question, what are the professional risks of bugs in digital forensic tools? Our investigation reviewed three high use professional proprietary digital forensic tools, one in which we identified six bugs and evaluated these bug in terms of potential impacts on an investigator\u27s work. The findings show that yes major brand name digital forensic tools have software bugs and there is room for improvement. These bugs had potential to frustrate an investigator, to cost time, to lose evidence and to require compensatory strategies. Such software bugs also have the potential for malicious exploitation and anti-forensic use

Identifying Bugs In Digital Forensic Tools

Bugs can be found in all code and the consequences are usually managed through upgrade releases, patches, and restarting operating systems and applications. However, in mission critical systems complete fall over systems are built to assure service continuity. In our research we asked the question, what are the professional risks of bugs in digital forensic tools? Our investigation reviewed three high use professional proprietary digital forensic tools, one in which we identified six bugs and evaluated these bug in terms of potential impacts on an investigator’s work. The findings show that yes major brand name digital forensic tools have software bugs and there is room for improvement. These bugs had potential to frustrate an investigator, to cost time, to lose evidence and to require compensatory strategies. Such software bugs also have the potential for malicious exploitation and anti-forensic use

Bridging the detection gap: a study on a behavior-based approach using malware techniques

In recent years the intensity and complexity of cyber attacks have increased at a rapid rate. The cost of these attacks on U.S. based companies is in the billions of dollars, including the loss of intellectual property and reputation. Novel and diverse approaches are needed to mitigate the cost of a security breach, and bridge the gap between malware detection and a security breach. This thesis focuses on the short term need to mitigate the impact of undetected shellcodes that cause security breaches. The thesis\u27s approach focuses on the agents driving the attacks, capturing their actions, in order to piece together the attacks for forensics purposes, as well as to better understand the opponent. The work presented in this thesis employs models of normal operating system behavior to detect access to the operating system\u27s shell interface. It also utilizes malware techniques to avoid detection and subsequent termination of the monitoring system, as well as dynamic shellcode execution methodologies in the testing of the thesis\u27 modules to implement a monitoring system --Document

Wide spectrum attribution: Using deception for attribution intelligence in cyber attacks

Modern cyber attacks have evolved considerably. The skill level required to conduct a cyber attack is low. Computing power is cheap, targets are diverse and plentiful. Point-and-click crimeware kits are widely circulated in the underground economy, while source code for sophisticated malware such as Stuxnet is available for all to download and repurpose. Despite decades of research into defensive techniques, such as firewalls, intrusion detection systems, anti-virus, code auditing, etc, the quantity of successful cyber attacks continues to increase, as does the number of vulnerabilities identified. Measures to identify perpetrators, known as attribution, have existed for as long as there have been cyber attacks. The most actively researched technical attribution techniques involve the marking and logging of network packets. These techniques are performed by network devices along the packet journey, which most often requires modification of existing router hardware and/or software, or the inclusion of additional devices. These modifications require wide-scale infrastructure changes that are not only complex and costly, but invoke legal, ethical and governance issues. The usefulness of these techniques is also often questioned, as attack actors use multiple stepping stones, often innocent systems that have been compromised, to mask the true source. As such, this thesis identifies that no publicly known previous work has been deployed on a wide-scale basis in the Internet infrastructure. This research investigates the use of an often overlooked tool for attribution: cyber de- ception. The main contribution of this work is a significant advancement in the field of deception and honeypots as technical attribution techniques. Specifically, the design and implementation of two novel honeypot approaches; i) Deception Inside Credential Engine (DICE), that uses policy and honeytokens to identify adversaries returning from different origins and ii) Adaptive Honeynet Framework (AHFW), an introspection and adaptive honeynet framework that uses actor-dependent triggers to modify the honeynet envi- ronment, to engage the adversary, increasing the quantity and diversity of interactions. The two approaches are based on a systematic review of the technical attribution litera- ture that was used to derive a set of requirements for honeypots as technical attribution techniques. Both approaches lead the way for further research in this field