Abstract Model-agnostic feature attribution techniques are used to explain the decisions of complex machine learning (ML) models including ensemble models, and deep neural networks (DNNs). However, since complex ML models perform best when trained on low-level features, the explanations generated by these algorithms are often not interpretable or usable by humans. Recently proposed model-agnostic methods that support the generation of human-interpretable explanations are impractical because they require a fully invertible transformation function that maps the model’s input features to human-interpretable features. While some practical human-interpretable explainability methods exist (e.g., concept-based methods), they typically require direct access to the model and are not fully model-agnostic. In this paper, we introduce Latent SHAP, a model-agnostic black-box feature attribution framework that provides human-interpretable explanations without necessitating a fully invertible transformation function. We validate the fidelity of Latent SHAP ’s explanations through quantitative faithfulness assessments on two controlled datasets—a self-generated artificial dataset and the dSprites dataset. Furthermore, we showcase the practical utility of Latent SHAP in various real-world scenarios across domains such as computer vision, natural language processing, and cybersecurity. Each domain involves complex models (ensembles, DNNs, and LLMs), where invertible transformation functions are not available.

Abstract State-of-the-art deep neural networks (DNNs) are highly effective at tackling many real-world tasks. However, their widespread adoption in mission-critical contexts is limited due to two major weaknesses - their susceptibility to adversarial attacks and their opaqueness. The former raises concerns about DNNs’ security and generalization in real-world conditions, while the latter, opaqueness, directly impacts interpretability. The lack of interpretability diminishes user trust as it is challenging to have confidence in a model’s decision when its reasoning is not aligned with human perspectives. In this research, we (1) examine the effect of adversarial robustness on interpretability, and (2) present a novel approach for improving DNNs’ interpretability that is based on the regularization of neural activation sensitivity. We evaluate the interpretability of models trained using our method to that of standard models and models trained using state-of-the-art adversarial robustness techniques. Our results show that adversarially robust models are superior to standard models, and that models trained using our proposed method are even better than adversarially robust models in terms of interpretability.(Code provided in supplementary material.)

An attack graph is a method used to enumerate the possible paths that an attacker can take in the organizational network. MulVAL is a known open-source framework used to automatically generate attack graphs. MulVAL's default modeling has two main shortcomings. First, it lacks the ability to represent network protocol vulnerabilities, and thus it cannot be used to model common network attacks, such as ARP poisoning. Second, it does not support advanced types of communication, such as wireless and bus communication, and thus it cannot be used to model cyber-attacks on networks that include IoT devices or industrial components. In this article, we present an extended network security model for MulVAL that: (1) considers the physical network topology, (2) supports short-range communication protocols, (3) models vulnerabilities in the design of network protocols, and (4) models specific industrial communication architectures. Using the proposed extensions, we were able to model multiple attack techniques including: spoofing, man-in-the-middle, and denial of service attacks, as well as attacks on advanced types of communication. We demonstrate the proposed model in a testbed which implements a simplified network architecture comprised of both IT and industrial components.

Selecting the optimal set of countermeasures to secure a network is a challenging task, since it involves various considerations and trade-offs, such as prioritizing the risks to mitigate given the mitigation costs. Previously suggested approaches are based on limited and largely manual risk assessment procedures, provide recommendations for a specific event, or don't consider the organization's constraints (e.g., limited budget). In this paper, we present an improved attack graph-based risk assessment process and apply heuristic search to select an optimal countermeasure plan for a given network and budget. The risk assessment process represents the risk in the system in such a way that incorporates the quantitative risk factors and relevant countermeasures; this allows us to assess the risk in the system under different countermeasure plans during the search, without the need to regenerate the attack graph. We also provide a detailed description of countermeasure modeling and discuss how the countermeasures can be automatically matched to the security issues discovered in the network.

Attack graphs are one of the main techniques used to automate the cybersecurity risk assessment process. In order to derive a relevant attack graph, up-to-date information on known cyber attack techniques should be represented as interaction rules. However, designing and creating new interaction rules is a time consuming task performed manually by security experts. We present a novel, end-to-end, automated framework for modeling new attack techniques from the textual description of security vulnerabilities. Given a description of a security vulnerability, the proposed framework first extracts the relevant attack entities required to model the attack, completes missing information on the vulnerability, and derives a new interaction rule that models the attack; this new rule is then integrated within the MulVal attack graph tool. The proposed framework implements a novel data science pipeline that includes a dedicated cybersecurity linguistic model trained on the NVD repository, a recurrent neural network model used for attack entity extraction, a logistic regression model used for completing the missing information, and a transition probability matrix for automatically generating new interaction rule. We evaluated the performance of each of the individual algorithms, as well as the complete framework, and demonstrated its effectiveness.

As the number of drones increases and the era in which they begin to fill the skies approaches, an important question needs to be answered: From a security and privacy perspective, are society and drones really prepared to handle the challenges that a large volume of flights will create? In this paper, we investigate security and privacy in the age of commercial drones. First, we focus on the research question: Are drones and their ecosystems protected against attacks performed by malicious entities? We list a drone’s targets, present a methodology for reviewing attack and countermeasure methods, perform a comprehensive review, analyze scientific gaps, present conclusions, and discuss future research directions. Then, we focus on the research question: Is society protected against attacks conducted using drones? We list targets within society, profile the adversaries, review threats, present a methodology for reviewing countermeasures, perform a comprehensive review, analyze scientific gaps, present conclusions, and discuss future research directions. Finally, we focus on the primary research question: From the security and privacy perspective, are society and drones prepared to take their relationship one step further? Our analysis reveals that the technological means required to protect drones and society from one another has not yet been developed, and there is a tradeoff between the security and privacy of drones and that of society. That is, the level of security and privacy cannot be optimized concurrently for both entities, because the security and privacy of drones cannot be optimized without decreasing the security and privacy of society, and vice versa.

Remote desktop protocols (RDP) are commonly used for connecting and interacting with computers remotely. In this case, a server component runs on the remote computer and shares its desktop (i.e., screen) with the client component which runs on an end user device. In recent years, a number of vulnerabilities have been identified in two widely used remote desktop implementations, Microsoft Remote Desktop and RealVNC. These vulnerabilities may expose the remote server to a new attack vector. This concern is increased when it comes to a cyber-physical system (CPS) in which a client device with a low trust level connects to the critical system via the remote desktop server. In order to mitigate this risk, in this paper we propose a network based intrusion detection system (NIDS) specifically designed for securing the remote desktop connections. The propose method utilizes an innovative anomaly detection technique based on machine learning for detecting malicious TCP packets, which can carry exploits aimed at the RDP server. An empirical evaluation conducted on an avionic system setup consisting of a commercial tablet (Samsung Galaxy Tab) connected through a Virtual Network Computing (VNC) remote desktop implementation to a real electronic flight bag (EFB) server shows that the proposed method can detect malicious packets carrying real exploits (reported in recent years) with a true positive rate of 0.863 and a false positive rate of 0.0001.

Information security awareness (ISA) is a practice focused on the set of skills which help a user successfully mitigate social engineering (SE) attacks. Evaluating the ISA of users is crucial, since early identification of users who are more vulnerable to SE attacks improves system security. Previous studies for evaluating the ISA of smartphone users rely on subjective data sources (questionnaires) and do not address the differences between classes of SE attacks. This paper presents a framework for evaluating the ISA of smartphone users for specific attack classes. In addition to questionnaires, we utilize objective data sources: a mobile agent, a network traffic monitor, and cybersecurity challenges. We evaluated the framework by conducting a long-term user study involving 162 users. The results show that: the self-reported behavior of users differs significantly from their actual behavior and the ISA level derived from the actual behavior of users is highly correlated with their ability to mitigate SE attacks.

State-of-the-art deep neural networks (DNNs) are highly effective in solving many complex real-world problems. However, these models are vulnerable to adversarial perturbation attacks, and despite the plethora of research in this domain, to this day, adversaries still have the upper hand in the cat and mouse game of adversarial example generation methods vs. detection and prevention methods. In this research, we present a novel detection method that uses Shapley Additive Explanations (SHAP) values computed for the internal layers of a DNN classifier to discriminate between normal and adversarial inputs. We evaluate our method by building an extensive dataset of adversarial examples over the popular CIFAR-10 and MNIST datasets, and training a neural network-based detector to distinguish between normal and adversarial inputs. We evaluate our detector against adversarial examples generated by diverse state-of-the-art attacks and demonstrate its high detection accuracy and strong generalization ability to adversarial inputs generated with different attack methods.

Cyber security assessment is an essential activity for understanding the security risks in an enterprise environment. While many tools have been developed in order to evaluate the security risks for individual hosts, it is still a challenge to identify multi-hop cyber security risks in a large-scale environment. An attack graph, which provides a comprehensive view of attacks, assists in identifying high-risk attack paths and efficiently deploying countermeasures. Several frameworks which generate an attack graph from system information and knowledge base have also been developed in the past. Although these tools are widely adopted, their expression capabilities are insufficient. The expansion of knowledge base is needed to handle comprehensive attack scenario. In this research, we developed an attack graph generation system by extending the MulVAL framework which is widely adopted due to its high extensibility. We designed and implemented knowledge base (also known as "interaction rules" in the MulVAL framework) for practical attack graph generation. A structured design procedure is necessary to construct a knowledge base that enables comprehensive analysis, which is highly important for actual risk assessment. We describe the design procedure, design considerations and implementation of our rule set. Additionally, we demonstrate the improvement to the generated attack graph by the implemented rules in a case study.