Automating Cyberdeception Evaluation with Deep Learning

A machine learning-based methodology is proposed and implemented for conducting evaluations of cyberdeceptive defenses with minimal human involvement. This avoids impediments associated with deceptive research on humans, maximizing the efficacy of automated evaluation before human subjects research must be undertaken. Leveraging recent advances in deep learning, the approach synthesizes realistic, interactive, and adaptive traffic for consumption by target web services. A case study applies the approach to evaluate an intrusion detection system equipped with application-layer embedded deceptive responses to attacks. Results demonstrate that synthesizing adaptive web traffic laced with evasive attacks powered by ensemble learning, online adaptive metric learning, and novel class detection to simulate skillful adversaries constitutes a challenging and aggressive test of cyberdeceptive defenses

HoneyCode: Automating Deceptive Software Repositories with Deep Generative Models

We propose HoneyCode, an architecture for the generation of synthetic software repositories for cyber deception. The synthetic repositories have the characteristics of real software, including language features, file names and extensions, but contain no real intellectual property. The fake software can be used as a honeypot or form part of a deceptive environment. Existing approaches to software repository generation lack scalability due to reliance on hand-crafted structures for specific languages. Our approach is language agnostic and learns the underlying representations of repository structures, filenames and file content through a novel Tree Recurrent Network (TRN) and two recurrent networks (RNN) respectively. Each stage of the sequential generation process utilises features from prior steps, which increases the honey repository’s authenticity and consistency. Experiments show TRN generates tree samples that reduce degree mean maximal distance (MMD) by 90-92% and depth MMD by 75-86% to a held out test data set in comparison to recent deep graph generators and a baseline random tree generator. In addition, our RNN models generate convincing filenames with authentic syntax and realistic file content

Design Thinking for Cyber Deception

Cyber deception tools are increasingly sophisticated but rely on a limited set of deception techniques. In current deployments of cyber deception, the network infrastructure between the defender and attacker comprises the defence/attack surface. For cyber deception tools and techniques to evolve further they must address the wider attack surface; from the network through to the physical and cognitive space. One way of achieving this is by fusing deception techniques from the physical and cognitive space with the technology development process. In this paper we trial design thinking as a way of delivering this fused approach. We detail the results from a design thinking workshop conducted using deception experts from different fields. The workshop outputs include a critical analysis of design provocations for cyber deception and a journey map detailing considerations for operationalising cyber deception scenarios that fuse deception techniques from other contexts. We conclude with recommendations for future research

Design thinking for cyber deception

Cyber deception tools are increasingly sophisticated but rely on a limited set of deception techniques. In current deployments of cyber deception, the network infrastructure between the defender and attacker comprises the defence/attack surface. For cyber deception tools and techniques to evolve further they must address the wider attack surface; from the network through to the physical and cognitive space. One way of achieving this is by fusing deception techniques from the physical and cognitive space with the technology development process. In this paper we trial design thinking as a way of delivering this fused approach. We detail the results from a design thinking workshop conducted using deception experts from different fields. The workshop outputs include a critical analysis of design provocations for cyber deception and a journey map detailing considerations for operationalising cyber deception scenarios that fuse deception techniques from other contexts. We conclude with recommendations for future research

An Empirical Assessment of the Effectiveness of Deception for Cyber Defense

The threat of cyber attacks is a growing concern across the world, leading to an increasing need for sophisticated cyber defense techniques. The Tularosa Study, was designed and conducted to understand how defensive deception, both cyber and psychological, affects cyber attackers Ferguson-Walter et al. [2019c]. More specifically, for this empirical study, cyber deception refers to a decoy system and psychological deception refers to false information of the presence of defensive deception techniques on the network. Over 130 red teamers participated in a network penetration test over two days in which we controlled both the presence of and explicit mention of deceptive defensive techniques. To our knowledge, this represents the largest study of its kind ever conducted on a skilled red team population. In addition to the abundant host and network data collected, we conducted a battery of questionnaires, e.g., experience, personality; and cognitive tasks, e.g., fluid intelligence, working memory; as well as physiological measures, e.g., galvanic skin response (GSR), heart rate, to be correlated with the cyber events at a later date. The design and execution of this study and the lessons learned are a major contribution of this thesis. I investigate the effectiveness of decoy systems for cyber defense by comparing performance across all experimental conditions. Results support a new finding that the combination of the presence of deception and the true information that deception is present has the greatest effect on cyber attackers, when compared to a control condition in which no deception was used. Evidence of cognitive biases in the red teamers’ behavior is then detailed and explained, to further support our theory of oppositional human factors (OHF). The final chapter discusses how elements of the experimental design contribute to the validity of assessing the effectiveness of cyber deception and reviews trade-offs and lessons learned

DECEPTION BASED TECHNIQUES AGAINST RANSOMWARES: A SYSTEMATIC REVIEW

Ransomware is the most prevalent emerging business risk nowadays. It seriously affects business continuity and operations. According to Deloitte Cyber Security Landscape 2022, up to 4000 ransomware attacks occur daily, while the average number of days an organization takes to identify a breach is 191. Sophisticated cyber-attacks such as ransomware typically must go through multiple consecutive phases (initial foothold, network propagation, and action on objectives) before accomplishing its final objective. This study analyzed decoy-based solutions as an approach (detection, prevention, or mitigation) to overcome ransomware. A systematic literature review was conducted, in which the result has shown that deception-based techniques have given effective and significant performance against ransomware with minimal resources. It is also identified that contrary to general belief, deception techniques mainly involved in passive approaches (i.e., prevention, detection) possess other active capabilities such as ransomware traceback and obstruction (thwarting), file decryption, and decryption key recovery. Based on the literature review, several evaluation methods are also analyzed to measure the effectiveness of these deception-based techniques during the implementation process

BEHAVIORAL CHARACTERIZATION OF ATTACKS ON THE REMOTE DESKTOP PROTOCOL

The Remote Desktop Protocol (RDP) is popular for enabling remote access and administration of Windows systems; however, attackers can take advantage of RDP to cause harm to critical systems using it. Detection and classification of RDP attacks is a challenge because most RDP traffic is encrypted, and it is not always clear which connections to a system are malicious after manual decryption of RDP traffic. In this research, we used open-source tools to generate and analyze RDP attack data using a power-grid honeypot under our control. We developed methods for detecting and characterizing RDP attacks through malicious signatures, Windows event log entries, and network traffic metadata. Testing and evaluation of our characterization methods on actual attack data collected by four instances of our honeypot showed that we could effectively delineate benign and malicious RDP traffic and classify the severity of RDP attacks on unprotected or misconfigured Windows systems. The classification of attack patterns and severity levels can inform defenders of adversarial behavior in RDP attacks. Our results can also help protect national critical infrastructure, including Department of Defense systems.DOE, Washington DC 20805Civilian, SFSApproved for public release. Distribution is unlimited

Trustworthy Edge Machine Learning: A Survey

The convergence of Edge Computing (EC) and Machine Learning (ML), known as Edge Machine Learning (EML), has become a highly regarded research area by utilizing distributed network resources to perform joint training and inference in a cooperative manner. However, EML faces various challenges due to resource constraints, heterogeneous network environments, and diverse service requirements of different applications, which together affect the trustworthiness of EML in the eyes of its stakeholders. This survey provides a comprehensive summary of definitions, attributes, frameworks, techniques, and solutions for trustworthy EML. Specifically, we first emphasize the importance of trustworthy EML within the context of Sixth-Generation (6G) networks. We then discuss the necessity of trustworthiness from the perspective of challenges encountered during deployment and real-world application scenarios. Subsequently, we provide a preliminary definition of trustworthy EML and explore its key attributes. Following this, we introduce fundamental frameworks and enabling technologies for trustworthy EML systems, and provide an in-depth literature review of the latest solutions to enhance trustworthiness of EML. Finally, we discuss corresponding research challenges and open issues.Comment: 27 pages, 7 figures, 10 table