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International Conference on Power Electronics and Artificial Intelligence (PEAI)

作者： Zhong, Ling E, Shengguo Lin, Shibo Li, Shuo Shenyang Univ Technol 111 Shenliao West Rd Shenyang Liaoning Peoples R China

ISBN: (纸本)9798400716638

Dynamic taint analysis methods, due to their language independence, reliance on binary code, and high accuracy, have been widely applied in the field of binary program vulnerability detection and security. However, these methods often incur significant performance overhead due to binary instrumentation. To address these issues, this study first categorizes x86 instructions and designs corresponding taint propagation strategies for each instruction category. It introduces the concept of taint analysis-agnostic classes to reduce redundant analysis and minimize performance overhead. Furthermore, a taint flow filtering mechanism is proposed during the taint propagation process to reduce inefficient analysis and improve analysis efficiency. Experimental results demonstrate that the improved dynamic taint analysis method can accurately detect vulnerabilities with CVE identifiers and outperforms traditional dynamic taint analysis methods in terms of performance. Therefore, the proposed improved dynamic taint analysis method effectively enhances both detection effectiveness and performance.

关键词： intrusion/anomaly detection and malware mitigation Systems security Network security Software and application security

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Tuning Automatic Summarization for Incident Report Visualization 15

Tuning Automatic Summarization for Incident Report Visualiza...

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IEEE 15th Pacific Visualization Symposium (PacificVis)

作者： Danneman, Nathan Gove, Robert Data Machines Corp Ashburn VA 20147 USA Two Six Technol Arlington VA USA

ISBN: (纸本)9781665423359

We present a machine learning approach to improve the accuracy of summarized incident report visualizations for cyber security. We extend a recent incident report summarization method by training a Bayesian hierarchical model to optimize the summarization algorithm's weights. We also train a flat model and a neural network as alternative models to compare against our hierarchical model. Summaries generated by our hierarchical model achieve higher accuracy than the other methods, with an AUC 0.2 higher than the unweighted method while achieving comparable summarization size. We further demonstrate that visualizations of the hierarchical model's summaries are at least as useful the unweighted method's summaries, and possibly more useful.

关键词： Human-centered computing Visualization Visualization application domains Information visualization Security and privacy Usability in security and privacy Security and privacy intrusion/anomaly detection and malware mitigation

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SNTA'22: The 5thWorkshop on Systems and Network Telemetry and Analytics 22

SNTA'22: The 5thWorkshop on Systems and Network Telemetry an...

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31st International Symposium on High-Performance Parallel and Distributed Computing (HPDC)

作者： Kim, Jinoh Cafaro, Massimo Chou, Jerry Sim, Alex Texas A&M Univ Commerce TX 75428 USA Univ Salento Lecce LE Italy Natl Tsing Hua Univ Hsinchu Taiwan Lawrence Berkeley Natl Lab Berkeley CA USA

ISBN: (纸本)9781450391993

HPC and distributed systems are the driving force for the advancement of many emerging technologies, such as exascale systems, quantum machines, terabit networking, 5G/6G wireless, and cloud/edge computing. The tasks of systems and network telemetry are a key element for effective operations and management of the advancement of many emerging systems and technologies, and require more scalable telemetry and analysis techniques for comprehensive monitoring and analysis. Various input sources such as end systems, switches, firewalls, intrusion sensors and the emerging network elements speaking with different syntax and semantics make organizing and incorporating the generated data challenging for the quantitative and qualitative analysis. This workshop looks for new approaches and methods at the intersection of HPC systems and data sciences to address these difficult challenges of emerging technologies from the diverse angles of systems/network performance, availability, reliability, and security.

关键词： distributed computing methodologies parallel computing methodologies intrusion/anomaly detection and malware mitigation network security network performance evaluation

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SAGE: intrusion Alert-driven Attack Graph Extractor

SAGE: Intrusion Alert-driven Attack Graph Extractor

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IEEE Symposium on Visualization for Cyber Security (VizSec)

作者： Nadeem, Azqa Verwer, Sicco Yang, Shanchieh Jay Delft Univ Technol Delft Netherlands Rochester Inst Technol Rochester NY 14623 USA

ISBN: (纸本)9781665420853

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.

关键词： Security and privacy intrusion/anomaly detection and malware mitigation intrusion detection systems Human-centered computing Visualization Visualization application domains Visual analytics Computing methodologies Machine learning Learning paradigms Unsupervised learning

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Impact of AI on the Cyber Kill Chain: A Systematic Review

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Heliyon 2024年第24期10卷 e40699页

作者： Kazimierczak, Mateusz Habib, Nuzaira Chan, Jonathan H. Thanapattheerakul, Thanyathorn Division of Engineering Science University of Toronto Toronto Canada Innovative Cognitive Computing (IC2) Research Center School of Information Technology (SIT) King Mongkut's University of Technology Thonburi Bangkok Thailand

The Cyber Kill Chain (CKC) defense model aims to assist subject matter experts in planning, identifying, and executing against cyber intrusion activity, by outlining seven stages required for adversaries to execute an attack. Recent advancements in Artificial Intelligence (AI) have empowered adversaries to execute sophisticated attacks to exploit system vulnerabilities. As a result, it is essential to consider how AI-based tools change the cyber threat landscape and affect the current standard CKC model. Thus, this study examines and categorizes how attackers use AI-based tools, and offers potential defense mechanisms. We conducted a systematic literature review of 62 papers published between 2013 and 2023 from the Web of Science and Google Scholar databases. Our findings indicate that AI-based tools are used most effectively in the initial stages of cyberattacks. However, we find that current defense tools are not designed to counter these sophisticated attacks during these stages. Thus, we provide insights to 1) highlight the changing threat landscape due to AI and 2) to guide the development of cyber defense mechanisms. © 2024 The Author(s)

关键词： Adversarial AI AI-based cyber attacks Artificial intelligence in cybersecurity Cyber attacks Cyber kill chain Cybersecurity intrusion/anomaly detection and malware mitigation Software and application security Systems security

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Visualizing Automatically Detected Periodic Network Activity

Visualizing Automatically Detected Periodic Network Activity

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15th IEEE Symposium on Visualization for Cyber Security (VizSec)

作者： Gove, Robert Deason, Lauren Two Six Labs Arlington VA 22203 USA PUNCH Cyber Analyt Grp Ashburn VA USA

ISBN: (纸本)9781538681947

malware frequently leaves periodic signals in network logs, but these signals are easily drowned out by non-malicious periodic network activity, such as software updates and other polling activity. This paper describes a novel algorithm based on Discrete Fourier Transforms capable of detecting multiple distinct period lengths in a given time series. We pair the output of this algorithm with aggregation summary tables that give users information scent about which detections are worth investigating based on the metadata of the log events rather than the periodic signal. A visualization of selected detections enables users to see all detected period lengths per entity, and compare detections between entities to check for coordinated activity. We evaluate our approach on real-world netflow and DNS data from a large organization, demonstrating how to successfully find malicious periodic activity in a large pool of noise and non-malicious periodic activity.

关键词： Human-centered computing Visualization Visualization application domains Information visualization Human-centered computing Visualization Visualization design and evaluation methods Security and privacy intrusion/anomaly detection and malware mitigation malware and its mitigation

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Fighting malicious code: an eternal struggle 14

Fighting malicious code: an eternal struggle

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Proceedings of the 9th ACM symposium on Information, computer and communications security

作者： Christopher Kruegel University of California Santa Barbara Santa Barbara CA USA

ISBN: (纸本)9781450328005

Despite many years of research and significant commercial investment, the malware problem is far from being solved (or even reasonably well contained). Every week, the mainstream press publishes articles that describe yet another incident where millions of credit cards were leaked, a large company discloses that adversaries had remote access to its corporate secrets for years, and we discover a new botnet with tens of thousands of compromised machines. Clearly, this situation is not acceptable, but why isn't it getting any better?In this talk, I will discuss some of the reasons why the malware problem is fundamentally hard, and why existing defenses in industry are no longer working. I will then outline progress that researchers and industry have made over the last years, and highlight a few milestones in our struggle to keep malicious code off our computer systems. This part will not focus on advances related to the analysis of malicious code alone, but take a broader perspective. How can we prevent malicious code from getting onto our machines in the first place? How can we detect network communication between malware programs and remote control nodes? And how can we lower the benefits that attackers obtain from their compromised machines? Finally, I will point out a few areas in which I believe that we should make progress to have the most impact in our fight against malicious code.

关键词： intrusion/anomaly detection and malware mitigation

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