Reasoning about Cyber Threat Actors

abstract: Reasoning about the activities of cyber threat actors is critical to defend against cyber attacks. However, this task is difficult for a variety of reasons. In simple terms, it is difficult to determine who the attacker is, what the desired goals are of the attacker, and how they will carry out their attacks. These three questions essentially entail understanding the attacker’s use of deception, the capabilities available, and the intent of launching the attack. These three issues are highly inter-related. If an adversary can hide their intent, they can better deceive a defender. If an adversary’s capabilities are not well understood, then determining what their goals are becomes difficult as the defender is uncertain if they have the necessary tools to accomplish them. However, the understanding of these aspects are also mutually supportive. If we have a clear picture of capabilities, intent can better be deciphered. If we understand intent and capabilities, a defender may be able to see through deception schemes. In this dissertation, I present three pieces of work to tackle these questions to obtain a better understanding of cyber threats. First, we introduce a new reasoning framework to address deception. We evaluate the framework by building a dataset from DEFCON capture-the-flag exercise to identify the person or group responsible for a cyber attack. We demonstrate that the framework not only handles cases of deception but also provides transparent decision making in identifying the threat actor. The second task uses a cognitive learning model to determine the intent – goals of the threat actor on the target system. The third task looks at understanding the capabilities of threat actors to target systems by identifying at-risk systems from hacker discussions on darkweb websites. To achieve this task we gather discussions from more than 300 darkweb websites relating to malicious hacking.Dissertation/ThesisDoctoral Dissertation Computer Engineering 201

A Novel Malware Target Recognition Architecture for Enhanced Cyberspace Situation Awareness

The rapid transition of critical business processes to computer networks potentially exposes organizations to digital theft or corruption by advanced competitors. One tool used for these tasks is malware, because it circumvents legitimate authentication mechanisms. Malware is an epidemic problem for organizations of all types. This research proposes and evaluates a novel Malware Target Recognition (MaTR) architecture for malware detection and identification of propagation methods and payloads to enhance situation awareness in tactical scenarios using non-instruction-based, static heuristic features. MaTR achieves a 99.92% detection accuracy on known malware with false positive and false negative rates of 8.73e-4 and 8.03e-4 respectively. MaTR outperforms leading static heuristic methods with a statistically significant 1% improvement in detection accuracy and 85% and 94% reductions in false positive and false negative rates respectively. Against a set of publicly unknown malware, MaTR detection accuracy is 98.56%, a 65% performance improvement over the combined effectiveness of three commercial antivirus products

Cyber Security and Critical Infrastructures 2nd Volume

The second volume of the book contains the manuscripts that were accepted for publication in the MDPI Special Topic "Cyber Security and Critical Infrastructure" after a rigorous peer-review process. Authors from academia, government and industry contributed their innovative solutions, consistent with the interdisciplinary nature of cybersecurity. The book contains 16 articles, including an editorial that explains the current challenges, innovative solutions and real-world experiences that include critical infrastructure and 15 original papers that present state-of-the-art innovative solutions to attacks on critical systems

Cyber Law and Espionage Law as Communicating Vessels

Professor Lubin\u27s contribution is Cyber Law and Espionage Law as Communicating Vessels, pp. 203-225. Existing legal literature would have us assume that espionage operations and “below-the-threshold” cyber operations are doctrinally distinct. Whereas one is subject to the scant, amorphous, and under-developed legal framework of espionage law, the other is subject to an emerging, ever-evolving body of legal rules, known cumulatively as cyber law. This dichotomy, however, is erroneous and misleading. In practice, espionage and cyber law function as communicating vessels, and so are better conceived as two elements of a complex system, Information Warfare (IW). This paper therefore first draws attention to the similarities between the practices – the fact that the actors, technologies, and targets are interchangeable, as are the knee-jerk legal reactions of the international community. In light of the convergence between peacetime Low-Intensity Cyber Operations (LICOs) and peacetime Espionage Operations (EOs) the two should be subjected to a single regulatory framework, one which recognizes the role intelligence plays in our public world order and which adopts a contextual and consequential method of inquiry. The paper proceeds in the following order: Part 2 provides a descriptive account of the unique symbiotic relationship between espionage and cyber law, and further explains the reasons for this dynamic. Part 3 places the discussion surrounding this relationship within the broader discourse on IW, making the claim that the convergence between EOs and LICOs, as described in Part 2, could further be explained by an even larger convergence across all the various elements of the informational environment. Parts 2 and 3 then serve as the backdrop for Part 4, which details the attempt of the drafters of the Tallinn Manual 2.0 to compartmentalize espionage law and cyber law, and the deficits of their approach. The paper concludes by proposing an alternative holistic understanding of espionage law, grounded in general principles of law, which is more practically transferable to the cyber realmhttps://www.repository.law.indiana.edu/facbooks/1220/thumbnail.jp

Bulk Analysis of Malicious PDF Documents

From 2007 onward, the PDF document has proven to be a successful vector for malware infections, making up 80% of all exploits found by Cisco ScanSafe in 2009 [1]. Creating new PDF documents is very easy and the volume of PDF documents identified as malicious has grown beyond the capabilities of security researchers to analyze by hand. The solution proposed by this thesis is to automatically extract features from the PDF documents to group and classify them, so that similar malware may be identified without manual analysis, thus reducing the workload of the malware analyst. These features may also be studied to identify trends within the PDF documents, such as similar exploits or obfuscation techniques. Our results show that the object graph structure of the PDF document is an effective way to create an initial grouping of malicious PDF documents. Finding similarities in PDF documents reveals further information about a data set. In our first case study, we examine the entire data set to identify large groups of similar PDF documents and make conjectures about their origins. In our second case study, we use a PDF document of known origin to find similar PDF documents within a data set. Through the two case studies, we were able to identify 50.3% of our data set with very little manual analysis of the malicious PDF documents

Integrating TrustZone Protection with Communication Paths for Mobile Operating System

Nowadays, users perform various essential activities through their smartphones, including mobile payment and financial transaction. Therefore, users’ sensitive data processed by smartphones will be at risk if underlying mobile OSes are compromised. A technology called Trusted Execution Environment (TEE) has been introduced to protect sensitive data in the event of compromised OS and hypervisor. This dissertation points out the limitations of the current design model of mobile TEE, which has a low adoption rate among application developers and has a large size of Trusted Computing Base (TCB). It proposes a new design model for mobile TEE to increase the TEE adoption rate and to decrease the size of TCB. This dissertation applies a new model to protect mobile communication paths in the Android platform. Evaluations are performed to demonstrate the effectiveness of the proposed design model

Securing Virtualized System via Active Protection

Virtualization is the predominant enabling technology of current cloud infrastructure

Analysis of Malware and Domain Name System Traffic

Malicious domains host Command and Control servers that are used to instruct infected machines to perpetuate malicious activities such as sending spam, stealing credentials, and launching denial of service attacks. Both static and dynamic analysis of malware as well as monitoring Domain Name System (DNS) traffic provide valuable insight into such malicious activities and help security experts detect and protect against many cyber attacks. Advanced crimeware toolkits were responsible for many recent cyber attacks. In order to understand the inner workings of such toolkits, we present a detailed reverse engineering analysis of the Zeus crimeware toolkit to unveil its underlying architecture and enable its mitigation. Our analysis allows us to provide a breakdown for the structure of the Zeus botnet network messages. In the second part of this work, we develop a framework for analyzing dynamic analysis reports of malware samples. This framework can be used to extract valuable cyber intelligence from the analyzed malware. The obtained intelligence helps reveal more insight into different cyber attacks and uncovers abused domains as well as malicious infrastructure networks. Based on this framework, we develop a severity ranking system for domain names. The system leverages the interaction between domain names and malware samples to extract indicators for malicious behaviors or abuse actions. The system utilizes these behavioral features on a daily basis to produce severity or abuse scores for domain names. Since our system assigns maliciousness scores that describe the level of abuse for each analyzed domain name, it can be considered as a complementary component to existing (binary) reputation systems, which produce long lists with no priorities. We also developed a severity system for name servers based on passive DNS traffic. The system leverages the domain names that reside under the authority of name servers to extract indicators for malicious behaviors or abuse actions. It also utilizes these behavioral features on a daily basis to dynamically produce severity or abuse scores for name servers. Finally, we present a system to characterize and detect the payload distribution channels within passive DNS traffic. Our system observes the DNS zone activities of access counts of each resource record type and determines payload distribution channels. Our experiments on near real-time passive DNS traffic demonstrate that our system can detect several resilient malicious payload distribution channels

Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021

As almost all human activities have been moved online due to the pandemic, novel robust and efficient approaches and further research have been in higher demand in the field of computer science and telecommunication. Therefore, this (reprint) book contains 13 high-quality papers presenting advancements in theoretical and practical aspects of computer recognition, pattern recognition, image processing and machine learning (shallow and deep), including, in particular, novel implementations of these techniques in the areas of modern telecommunications and cybersecurity

Multimodal Approach for Malware Detection

Although malware detection is a very active area of research, few works were focused on using physical properties (e.g., power consumption) and multimodal features for malware detection. We designed an experimental testbed that allowed us to run samples of malware and non-malicious software applications and to collect power consumption, network traffic, and system logs data, and subsequently to extract dynamic behavioral-based features. We also extracted code-based static features of both malware and non-malicious software applications. These features were used for malware detection based on: feature level fusion using power consumption and network traffic data, feature level fusion using network traffic data and system logs, and multimodal feature level and decision level fusion. The contributions when using feature level fusion of power consumption and network traffic data are: (1) We focused on detecting real malware using the extracted dynamic behavioral features (both power-based and network traffic-based) and supervised machine learning algorithms, which has not been done by any of the prior works. (2) We ran a large number of machine learning experiments, which allowed us to identify the best performing learner, DC voltage rails that led to the best malware detection performance, and the subset of features that are the best predictors for malware detection. (3) The comparison of malware detection performance was done using a comprehensive set of metrics that reflect different aspects of the quality of malware detection. In the case of the feature level fusion using network traffic data and system logs, the contributions are: (1) Most of the previous works that have used network flows-based features have done classification of the network traffic, while our focus was on classifying the software running in a machine as malware and non-malicious software using the extracted dynamic behavioral features. (2) We experimented with different sizes of the training set (i.e., 90%, 75%, 50%, and 25% of the data) and found that smaller training sets produced very good classification results. This aspect of our work has a practical value because the manual labeling of the training set is a tedious and time consuming process. In this dissertation we present a multimodal deep learning neural network that integrates different modalities (i.e., power consumption, system logs, network traffic, and code-based static data) using decision level fusion. We evaluated the performance of each modality individually, when using feature level fusion, and when using decision level fusion. The contributions of our multimodal approach are as follow: (1) Collecting data from different modalities allowed us to develop a multimodal approach to malware detection, which has not been widely explored by prior works. Even more, none of the previous works compared the performance of feature level fusion with decision level fusion, which is explored in this dissertation. (2) We proposed a multimodal decision level fusion malware detection approach using a deep neural network and compared its performance with the performance of feature level fusion approaches based on deep neural network and standard supervised machine learning algorithms (i.e., Random Forest, J48, JRip, PART, Naive Bayes, and SMO)