arXiv subjects

Azqa Nadeem

Publications and source records attributed to Azqa Nadeem.

9 recordsLinked to original sources

arXiv2024

Forecasting Attacker Actions using Alert-driven Attack Graphs

While intrusion detection systems form the first line-of-defense against cyberattacks, they often generate an overwhelming volume of alerts, leading to alert fatigue among security operations center (SOC) analysts. Alert-driven attack graphs (AGs) have been developed to reduce alert fatigue by automatically discovering attack paths in intrusion alerts. However, they only work in offline settings and cannot prioritize critical attack paths. This paper builds an action forecasting capability on top of the existing alert-driven AG framework for predicting the next likely attacker action given a sequence of observed actions, thus enabling analysts to prioritize non-trivial attack paths. We also modify the framework to build AGs in real time, as new alerts are triggered. This way, we convert alert-driven AGs into an early warning system that enables analysts to circumvent ongoing attacks and break the cyber killchain. We propose an expectation maximization approach to forecast future actions in a reversed suffix-based probabilistic deterministic finite automaton (rSPDFA). By utilizing three real-world intrusion and endpoint alert datasets, we empirically demonstrate that the best performing rSPDFA achieves an average top-3 accuracy of 67.27%, which reflects a 57.17% improvement over three baselines, on average. We also invite six SOC analysts to use the evolving AGs in two scenarios. Their responses suggest that the action forecasts help them prioritize critical incidents, while the evolving AGs enable them to choose countermeasures in real-time.

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arXiv2023

Critical Path Prioritization Dashboard for Alert-driven Attack Graphs

Although intrusion alerts can provide threat intelligence regarding attacker strategies, extracting such intelligence via existing tools is expensive and time-consuming. Earlier work has proposed SAGE, which generates attack graphs from intrusion alerts using unsupervised sequential machine learning. This paper proposes a querying and prioritization-enabled visual analytics dashboard for SAGE. The dashboard has three main components: (i) a Graph Explorer that presents a global view of all attacker strategies, (ii) a Timeline Viewer that correlates attacker actions chronologically, and (iii) a Recommender Matrix that highlights prevalent critical alerts via a MITRE ATT&CK-inspired attack stage matrix. We describe the utility of the proposed dashboard using intrusion alerts collected from a distributed multi-stage team-based attack scenario. We evaluate the utility of the dashboard through a user study. Based on the responses of a small set of security practitioners, we find that the dashboard is useful in depicting attacker strategies and attack progression, but can be improved in terms of usability.

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arXiv2023

Cybersecurity as a Crosscutting Concept Across an Undergrad Computer Science Curriculum: An Experience Report

Although many Computer Science (CS) programs offer cybersecurity courses, they are typically optional and placed at the periphery of the program. We advocate to integrate cybersecurity as a crosscutting concept in CS curricula, which is also consistent with latest cybersecurity curricular guidelines, e.g., CSEC2017. We describe our experience of implementing this crosscutting intervention across three undergraduate core CS courses at a leading technical university in Europe between 2018 and 2023, collectively educating over 2200 students. The security education was incorporated within CS courses using a partnership between the responsible course instructor and a security expert, i.e., the security expert (after consultation with course instructors) developed and taught lectures covering multiple CSEC2017 knowledge areas. This created a complex dynamic between three stakeholders: the course instructor, the security expert, and the students. We reflect on our intervention from the perspective of the three stakeholders -- we conducted a post-course survey to collect student perceptions, and semi-supervised interviews with responsible course instructors and the security expert to gauge their experience. We found that while the students were extremely enthusiastic about the security content and retained its impact several years later, the misaligned incentives for the instructors and the security expert made it difficult to sustain this intervention without organizational support. By identifying limitations in our intervention, we suggest ideas for sustaining it.

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arXiv2022

SoK: Explainable Machine Learning for Computer Security Applications

Explainable Artificial Intelligence (XAI) aims to improve the transparency of machine learning (ML) pipelines. We systematize the increasingly growing (but fragmented) microcosm of studies that develop and utilize XAI methods for defensive and offensive cybersecurity tasks. We identify 3 cybersecurity stakeholders, i.e., model users, designers, and adversaries, who utilize XAI for 4 distinct objectives within an ML pipeline, namely 1) XAI-enabled user assistance, 2) XAI-enabled model verification, 3) explanation verification & robustness, and 4) offensive use of explanations. Our analysis of the literature indicates that many of the XAI applications are designed with little understanding of how they might be integrated into analyst workflows -- user studies for explanation evaluation are conducted in only 14% of the cases. The security literature sometimes also fails to disentangle the role of the various stakeholders, e.g., by providing explanations to model users and designers while also exposing them to adversaries. Additionally, the role of model designers is particularly minimized in the security literature. To this end, we present an illustrative tutorial for model designers, demonstrating how XAI can help with model verification. We also discuss scenarios where interpretability by design may be a better alternative. The systematization and the tutorial enable us to challenge several assumptions, and present open problems that can help shape the future of XAI research within cybersecurity.

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arXiv2022

SECLEDS: Sequence Clustering in Evolving Data Streams via Multiple Medoids and Medoid Voting

Sequence clustering in a streaming environment is challenging because it is computationally expensive, and the sequences may evolve over time. K-medoids or Partitioning Around Medoids (PAM) is commonly used to cluster sequences since it supports alignment-based distances, and the k-centers being actual data items helps with cluster interpretability. However, offline k-medoids has no support for concept drift, while also being prohibitively expensive for clustering data streams. We therefore propose SECLEDS, a streaming variant of the k-medoids algorithm with constant memory footprint. SECLEDS has two unique properties: i) it uses multiple medoids per cluster, producing stable high-quality clusters, and ii) it handles concept drift using an intuitive Medoid Voting scheme for approximating cluster distances. Unlike existing adaptive algorithms that create new clusters for new concepts, SECLEDS follows a fundamentally different approach, where the clusters themselves evolve with an evolving stream. Using real and synthetic datasets, we empirically demonstrate that SECLEDS produces high-quality clusters regardless of drift, stream size, data dimensionality, and number of clusters. We compare against three popular stream and batch clustering algorithms. The state-of-the-art BanditPAM is used as an offline benchmark. SECLEDS achieves comparable F1 score to BanditPAM while reducing the number of required distance computations by 83.7%. Importantly, SECLEDS outperforms all baselines by 138.7% when the stream contains drift. We also cluster real network traffic, and provide evidence that SECLEDS can support network bandwidths of up to 1.08 Gbps while using the (expensive) dynamic time warping distance.

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arXiv2022

Robust Attack Graph Generation

We present a method to learn automaton models that are more robust to input modifications. It iteratively aligns sequences to a learned model, modifies the sequences to their aligned versions, and re-learns the model. Automaton learning algorithms are typically very good at modeling the frequent behavior of a software system. Our solution can be used to also learn the behavior present in infrequent sequences, as these will be aligned to the frequent ones represented by the model. We apply our method to the SAGE tool for modeling attacker behavior from intrusion alerts. In experiments, we demonstrate that our algorithm learns models that can handle noise such as added and removed symbols from sequences. Furthermore, it learns more concise models that fit better to the training data.

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arXiv2021

SAGE: Intrusion Alert-driven Attack Graph Extractor

Attack graphs (AG) are used to assess pathways availed by cyber adversaries to penetrate a network. State-of-the-art approaches for AG generation focus mostly on deriving dependencies between system vulnerabilities based on network scans and expert knowledge. In real-world operations however, it is costly and ineffective to rely on constant vulnerability scanning and expert-crafted AGs. We propose to automatically learn AGs based on actions observed through intrusion alerts, without prior expert knowledge. Specifically, we develop an unsupervised sequence learning system, SAGE, that leverages the temporal and probabilistic dependence between alerts in a suffix-based probabilistic deterministic finite automaton (S-PDFA) -- a model that accentuates infrequent severe alerts and summarizes paths leading to them. AGs are then derived from the S-PDFA on a per-objective, per-victim basis. Tested with intrusion alerts collected through Collegiate Penetration Testing Competition, SAGE compresses over 330k alerts into 93 AGs. These AGs reflect the strategies used by the participating teams. The AGs are succinct, interpretable, and capture behavioral dynamics, e.g., that attackers will often follow shorter paths to re-exploit objectives.

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arXiv2019

Laptop Theft in a University Setting can be Avoided with Warnings

Laptops have become an indispensable asset in today's digital age. They often contain highly sensitive information, such as credentials and confidential documents. As a result, the value of a laptop is an accumulation of the value of both the physical device itself and the cyber assets it contains, making it a lucrative target for theft. Educational institutions have a large population of potential victims of laptop theft. To mitigate this risk, we investigate whether a simple warning sign can reduce the opportunity for potential offenders. To this end, we have conducted an empirical study to observe the prevalence of students/staff leaving their laptops unattended at a university study hall at the Delft University of Technology in the Netherlands, both with and without a warning sign. We observed 148 out of 220 subjects leaving their laptops unattended in just three weeks. The results also showed that without the warning banner, 75.5% (83 out of 110) of subjects left their laptops unattended and with the warning, only 59.1% (65 out of 110) of subjects showed the same behavior, which is a significant reduction of 16.4%. In addition, a qualitative analysis was performed on the responses of subjects who left their laptops unattended after the warning banner was placed. The results showed a mix of convenience, and a blind trust on the safety of the faculty. In conclusion, a simple banner was effective in reducing the opportunity for laptop theft. However, the percentage of laptops left unattended was still high even after the introduction of the banner.

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arXiv2019

Beyond Labeling: Using Clustering to Build Network Behavioral Profiles of Malware Families

Malware family labels are known to be inconsistent. They are also black-box since they do not represent the capabilities of malware. The current state-of-the-art in malware capability assessment include mostly manual approaches, which are infeasible due to the ever-increasing volume of discovered malware samples. We propose a novel unsupervised machine learning-based method called MalPaCA, which automates capability assessment by clustering the temporal behavior in malware's network traces. MalPaCA provides meaningful behavioral clusters using only 20 packet headers. Behavioral profiles are generated based on the cluster membership of malware's network traces. A Directed Acyclic Graph shows the relationship between malwares according to their overlapping behaviors. The behavioral profiles together with the DAG provide more insightful characterization of malware than current family designations. We also propose a visualization-based evaluation method for the obtained clusters to assist practitioners in understanding the clustering results. We apply MalPaCA on a financial malware dataset collected in the wild that comprises of 1.1k malware samples resulting in 3.6M packets. Our experiments show that (i) MalPaCA successfully identifies capabilities, such as port scans and reuse of Command and Control servers; (ii) It uncovers multiple discrepancies between behavioral clusters and malware family labels; and (iii) It demonstrates the effectiveness of clustering traces using temporal features by producing an error rate of 8.3%, compared to 57.5% obtained from statistical features.

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