{
  "schema_version": "0.1.0",
  "record_type": "research-topic",
  "topic_id": "machine-learning",
  "label": "Machine Learning",
  "title": "Machine Learning for Systems Research",
  "description": "Research papers applying machine learning to performance, tracing, anomaly detection, security, and systems analysis.",
  "introduction": "This topic collects papers that use machine-learning models or learning-based analysis for systems and software problems. The records identify the model, features, training data, baselines, evaluation metrics, and operational goal—such as anomaly detection, prediction, classification, or trace analysis.",
  "aliases": [
    "machine learning systems analysis",
    "learning-based performance analysis"
  ],
  "search_terms": [
    "machine learning",
    "systems machine learning",
    "anomaly detection",
    "performance prediction",
    "trace mining"
  ],
  "related_topics": [
    "anomaly-detection",
    "predictive-monitoring",
    "llm-assisted-analysis",
    "graph-neural-networks",
    "performance-analysis"
  ],
  "canonical_url": "https://threadslab.org/research-publications/topics/machine-learning.html",
  "paper_count": 23,
  "papers": [
    {
      "paper_id": "tracesynth-kernel-trace-generation",
      "title": "TraceSynth: Generating Production-Quality Kernel Traces with Constraint-Guided Diffusion Models",
      "year": 2026,
      "authors": [
        "Yuvraj Sehgal",
        "Sneh Patel",
        "Mahsa Panahandeh",
        "Naser Ezzati-Jivan",
        "Francois Tetreault"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/tracesynth-kernel-trace-generation/",
      "canonical_source_url": "https://doi.org/10.1145/3803437.3805222",
      "core_contribution": "TraceSynth generates novel structured kernel-trace windows with a Transformer diffusion model and repairs generated events against invariants mined from real LTTng traces.",
      "tags": [
        "kernel-tracing",
        "system-tracing",
        "trace-analysis",
        "machine-learning"
      ],
      "keywords": [
        "kernel traces",
        "trace generation",
        "diffusion models",
        "constraint-guided generation",
        "LTTng",
        "Parquet",
        "Phoronix Test Suite",
        "DDIM",
        "next-event prediction",
        "synthetic traces"
      ]
    },
    {
      "paper_id": "ai-video-retrieval-semantic-search-timestamp-alignment",
      "title": "AI Video Retrieval: A Semantic Search & Timestamp Alignment System",
      "year": 2025,
      "authors": [
        "Hridoy Rahman",
        "Naser Ezzati-Jivan",
        "Blessing Ogbuokiri"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/ai-video-retrieval-semantic-search-timestamp-alignment/",
      "canonical_source_url": "https://doi.org/10.1109/ACDSA65407.2025.11166430",
      "core_contribution": "The paper implements a timestamp-aware multimodal video-retrieval pipeline that joins speech transcription, sampled-frame captioning, text embeddings, and approximate-nearest-neighbor search.",
      "tags": [
        "multimodal-ai",
        "machine-learning",
        "benchmark-datasets"
      ],
      "keywords": [
        "video retrieval",
        "semantic search",
        "timestamp alignment",
        "AI video search",
        "ACDSA 2025"
      ]
    },
    {
      "paper_id": "efficient-communication-performance-antipattern-detection-microservices",
      "title": "Efficient Detection of Communication-related Performance Anti-patterns in Microservices",
      "year": 2025,
      "authors": [
        "Masoumeh Nourollahi",
        "Naser Ezzati-Jivan",
        "Adel Belkheiri",
        "Michel Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/efficient-communication-performance-antipattern-detection-microservices/",
      "canonical_source_url": "https://doi.org/10.22541/au.175533132.24109345/v1",
      "core_contribution": "The preprint combines communication-related kernel/system-call events with distributed traces to detect communication performance anti-patterns in microservices.",
      "tags": [
        "microservices",
        "system-tracing",
        "anomaly-detection",
        "machine-learning",
        "performance-analysis"
      ],
      "keywords": [
        "communication anti-patterns",
        "DeathStarBench",
        "LTTng",
        "Trace Compass",
        "Jaeger",
        "system calls",
        "distributed traces",
        "Blob",
        "Empty Semi Trucks",
        "supervised learning",
        "semi-supervised learning"
      ]
    },
    {
      "paper_id": "energy-consumption-analysis-large-language-models-cpu-gpu",
      "title": "Energy Consumption Analysis of Large Language Models Across CPU and GPU Using Diverse Metric Types",
      "year": 2025,
      "authors": [
        "Tong Zhang",
        "Leila Tahmooresnejad",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/energy-consumption-analysis-large-language-models-cpu-gpu/",
      "canonical_source_url": "https://doi.org/10.1109/CASCON66301.2025.00056",
      "core_contribution": "The paper models LLM inference energy separately on CPU and GPU using hardware counters, device metrics, and task/model features, then compares classical and neural regressors across language tasks.",
      "tags": [
        "energy-efficiency",
        "llm-efficiency",
        "performance-modeling",
        "machine-learning",
        "performance-analysis"
      ],
      "keywords": [
        "LLM energy consumption",
        "CPU energy",
        "GPU energy",
        "green AI",
        "static metrics",
        "dynamic metrics",
        "NLP workloads",
        "energy prediction",
        "CASCON 2025"
      ]
    },
    {
      "paper_id": "multi-dimensional-bias-analysis-llms-hierarchical-interactions",
      "title": "Multi-Dimensional Bias Analysis in LLMs Using Hierarchical and Interaction Models",
      "year": 2025,
      "authors": [
        "Basil Syed",
        "Daniel Arana Charlebois",
        "Naser Ezzati-Jivan",
        "Leila Tahmooresnejad",
        "Anteneh Ayanso"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/multi-dimensional-bias-analysis-llms-hierarchical-interactions/",
      "canonical_source_url": "https://aisel.aisnet.org/amcis2025/data_science/sig_dsa/15/",
      "core_contribution": "The paper proposes the Triangle Multi-Dimensional Model for Bias Analysis, a hierarchical and interaction-based framework for tracing how bias originates, propagates, compounds, and feeds back across an LLM lifecycle.",
      "tags": [
        "machine-learning",
        "responsible-ai",
        "llm-evaluation"
      ],
      "keywords": [
        "Triangle Multi-Dimensional Model",
        "LLM bias",
        "hierarchical bias analysis",
        "cross-layer propagation",
        "feedback loops",
        "intersectional bias",
        "LLMBI",
        "bias ranking",
        "GPT-4",
        "Gemini 1.5",
        "temperature adjustment",
        "instruction guiding",
        "Pearson correlation",
        "Spearman correlation",
        "mutual information",
        "Granger causality",
        "structural equation modeling",
        "StereoSet",
        "Wino-Bias"
      ]
    },
    {
      "paper_id": "synthlogai-synthetic-linux-log-generation",
      "title": "SynthLogAI: Generative AI for Synthetic Linux Log Generation and Evaluation",
      "year": 2025,
      "authors": [
        "Hridoy Rahman",
        "Naser Ezzati-Jivan",
        "Blessing Ogbuokiri"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/synthlogai-synthetic-linux-log-generation/",
      "canonical_source_url": "https://doi.org/10.1109/CASCON66301.2025.00032",
      "core_contribution": "SynthLogAI benchmarks statistical, sequence, transformer, and prompt-based generative models for producing synthetic Linux logs while measuring fidelity, downstream utility, and privacy.",
      "tags": [
        "machine-learning",
        "observability",
        "anomaly-detection",
        "system-tracing"
      ],
      "keywords": [
        "synthetic Linux logs",
        "generative AI",
        "log generation",
        "log evaluation",
        "CASCON 2025"
      ]
    },
    {
      "paper_id": "iot-anomaly-classification-cnn-bilstm-two-tiered",
      "title": "A Two-Tiered Framework for Anomaly Classification in IoT Networks Utilizing CNN-BiLSTM Model",
      "year": 2024,
      "authors": [
        "Yue Guan",
        "Morteza Noferesti",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/iot-anomaly-classification-cnn-bilstm-two-tiered/",
      "canonical_source_url": "https://doi.org/10.1016/j.simpa.2024.100646",
      "core_contribution": "The paper proposes a two-tier IoT intrusion detector that first separates normal from anomalous flows and then classifies the attack type with a CNN-BiLSTM model.",
      "tags": [
        "iot-security",
        "anomaly-detection",
        "machine-learning",
        "deep-learning-systems"
      ],
      "keywords": [
        "IoT anomaly detection",
        "CNN-BiLSTM",
        "SMOTE",
        "particle swarm optimization",
        "PSO",
        "Software Impacts",
        "classification"
      ]
    },
    {
      "paper_id": "an-adaptive-logging-system-als-enhancing-software-logging-with-reinforcement-learning-techniques",
      "title": "An Adaptive Logging System (ALS): Enhancing Software Logging with Reinforcement Learning Techniques",
      "year": 2024,
      "authors": [
        "Amirmahdi Khosravi Tabrizi",
        "Naser Ezzati-Jivan",
        "Francois Tetreault"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/an-adaptive-logging-system-als-enhancing-software-logging-with-reinforcement-learning-techniques/",
      "canonical_source_url": "https://doi.org/10.1145/3629526.3645033",
      "core_contribution": "ALS uses source-code features and reinforcement learning to recommend which Python functions to log and which log levels to use for performance-bug diagnosis.",
      "tags": [
        "observability",
        "performance-engineering",
        "machine-learning",
        "trace-reduction"
      ],
      "keywords": [
        "adaptive logging",
        "ALS",
        "reinforcement learning",
        "log placement",
        "log level selection",
        "performance bugs",
        "Python AST",
        "Stable-Baselines",
        "DQN",
        "A2C",
        "PPO",
        "Apache",
        "Django",
        "cross-project evaluation",
        "Ciena",
        "source-code features"
      ]
    },
    {
      "paper_id": "assessing-predictive-models-energy-consumption-software",
      "title": "Assessing Predictive Models for Energy Consumption Across Varied Software Environments",
      "year": 2024,
      "authors": [
        "Tong Zhang",
        "Sarwat Islam Dipanzan",
        "Leila Tahmooresnejad",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/assessing-predictive-models-energy-consumption-software/",
      "canonical_source_url": "https://doi.org/10.1109/BigData62323.2024.10825500",
      "core_contribution": "The paper evaluates whether software-energy predictors transfer across applications when they use hardware-performance and operating-system event representations rather than application-specific measurements alone.",
      "tags": [
        "energy-efficiency",
        "performance-modeling",
        "predictive-monitoring",
        "machine-learning"
      ],
      "keywords": [
        "software energy consumption",
        "predictive models",
        "energy efficiency",
        "software environments",
        "IEEE Big Data 2024"
      ]
    },
    {
      "paper_id": "decoding-log-parsing-challenges-taxonomy",
      "title": "Decoding Log Parsing Challenges: A Comprehensive Taxonomy for Actionable Solutions",
      "year": 2024,
      "authors": [
        "Issam Sedki",
        "Abdelwahab Hamou-Lhadj",
        "Otmane Ait-Mohamed",
        "Naser Ezzati-Jivan",
        "Mohammed A. Shehab"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/decoding-log-parsing-challenges-taxonomy/",
      "canonical_source_url": "https://doi.org/10.1145/3639478.3643523",
      "core_contribution": "The paper derives a 30-item taxonomy of log event characteristics that induce parsing errors and quantifies the characteristics with the largest impact across eight parsers.",
      "tags": [
        "observability",
        "machine-learning",
        "trace-analysis",
        "benchmark-datasets"
      ],
      "keywords": [
        "log parsing",
        "log event characteristics",
        "LEC taxonomy",
        "LogHub",
        "open coding",
        "Drain",
        "IPLoM",
        "AEL",
        "Spell",
        "LenMa",
        "LogMine",
        "SHISO",
        "ULP",
        "log templates",
        "parsing errors",
        "ICSE 2024"
      ]
    },
    {
      "paper_id": "memadapt-adaptive-monitoring-memory-usage",
      "title": "MemAdapt: Adaptive Monitoring of Memory Usage Through Irregularly Sampled Data",
      "year": 2024,
      "authors": [
        "Pranjal Chakraborty",
        "Majid Babaei",
        "Leila Tahmooresnejad",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/memadapt-adaptive-monitoring-memory-usage/",
      "canonical_source_url": "https://doi.org/10.1109/CASCON62161.2024.10838037",
      "core_contribution": "MemAdapt forecasts memory behavior under irregular sampling and uses the forecast to choose an adaptive monitoring rate that balances estimation quality with collection overhead.",
      "tags": [
        "resource-analysis",
        "predictive-monitoring",
        "performance-analysis",
        "machine-learning"
      ],
      "keywords": [
        "memory monitoring",
        "irregular sampling",
        "adaptive monitoring",
        "time series",
        "memory usage",
        "MemAdapt"
      ]
    },
    {
      "paper_id": "picturing-ambiguity-winograd-schema",
      "title": "Picturing Ambiguity: A Visual Twist on the Winograd Schema Challenge",
      "year": 2024,
      "authors": [
        "Brendan Park",
        "Madeline Janecek",
        "Naser Ezzati-Jivan",
        "Yifeng Li",
        "Ali Emami"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/picturing-ambiguity-winograd-schema/",
      "canonical_source_url": "https://aclanthology.org/2024.acl-long.22/",
      "core_contribution": "The paper introduces WinoVis, a multimodal benchmark and analysis framework for testing pronoun disambiguation in text-to-image models.",
      "tags": [
        "multimodal-ai",
        "benchmark-datasets",
        "common-sense-reasoning",
        "machine-learning"
      ],
      "keywords": [
        "Winograd Schema Challenge",
        "WinoVis",
        "text-to-image models",
        "pronoun disambiguation",
        "DAAM",
        "Stable Diffusion"
      ]
    },
    {
      "paper_id": "cnn-bilstm-rpl-attacks-iot-smart-grid",
      "title": "CNN-BiLSTM-Based Classification of RPL Attacks in IoT Smart Grid Networks (Industry Track)",
      "year": 2023,
      "authors": [
        "Yue Guan",
        "Morteza Noferesti",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/cnn-bilstm-rpl-attacks-iot-smart-grid/",
      "canonical_source_url": "https://doi.org/10.1145/3626562.3626832",
      "core_contribution": "The paper applies a CNN-BiLSTM intrusion classifier to RPL/IoT traffic, combining convolutional feature extraction with bidirectional sequence modeling after imbalance-aware flow preprocessing.",
      "tags": [
        "iot-security",
        "deep-learning-systems",
        "machine-learning",
        "anomaly-detection"
      ],
      "keywords": [
        "RPL attacks",
        "IoT smart grid",
        "CNN-BiLSTM",
        "routing attacks",
        "intrusion detection",
        "Middleware 2023"
      ]
    },
    {
      "paper_id": "emd-scs-malware-detection-sonification-system-calls",
      "title": "EMD-SCS: A Dynamic Behavioral Approach for Early Malware Detection with Sonification of System Call Sequences",
      "year": 2023,
      "authors": [
        "Raghav Bhardwaj",
        "Morteza Noferesti",
        "Madeline Janecek",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/emd-scs-malware-detection-sonification-system-calls/",
      "canonical_source_url": "https://doi.org/10.1109/TRUSTCOM60117.2023.00235",
      "core_contribution": "EMD-SCS combines sequence prediction of system calls with sonification so that partial execution prefixes can support early malware detection and an interpretable auditory alert.",
      "tags": [
        "system-tracing",
        "anomaly-detection",
        "machine-learning",
        "performance-analysis"
      ],
      "keywords": [
        "malware detection",
        "system-call sequences",
        "sonification",
        "Hamming distance",
        "detection rate",
        "false-positive rate",
        "EMD-SCS"
      ]
    },
    {
      "paper_id": "identification-and-classification-of-jmh-microbenchmark-states-using-time-series-analysis",
      "title": "Identification and Classification of JMH Microbenchmark States using Time Series Analysis",
      "year": 2023,
      "authors": [
        "Tom Wallace",
        "Beatrice M. Ombuki-Berman",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/identification-and-classification-of-jmh-microbenchmark-states-using-time-series-analysis/",
      "canonical_source_url": "https://doi.org/10.1145/3578245.3584694",
      "core_contribution": "The paper uses time-series analysis to identify and classify warmup, steady-state, and anomalous states in Java JMH microbenchmarks.",
      "tags": [
        "performance-analysis",
        "performance-engineering",
        "anomaly-detection",
        "machine-learning"
      ],
      "keywords": [
        "Java Microbenchmark Harness",
        "JMH",
        "benchmark states",
        "steady state",
        "warmup",
        "Matrix Profile",
        "motifs",
        "discords",
        "PELT",
        "change points",
        "Stumpy",
        "Ruptures",
        "time-series analysis"
      ]
    },
    {
      "paper_id": "software-mining-investigating-correlation-between-source-code-features-and-michrobenchmark-s-ste",
      "title": "Software Mining - Investigating Correlation between Source Code Features and Michrobenchmark's Steady State",
      "year": 2023,
      "authors": [
        "Amirmahdi Khosravi Tabrizi",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/software-mining-investigating-correlation-between-source-code-features-and-michrobenchmark-s-ste/",
      "canonical_source_url": "https://doi.org/10.1145/3578245.3584695",
      "core_contribution": "The study examines whether static source-code features are associated with the steady-state behavior of Java microbenchmarks during JVM warmup.",
      "tags": [
        "performance-analysis",
        "performance-engineering",
        "machine-learning"
      ],
      "keywords": [
        "Java Microbenchmark Harness",
        "JMH",
        "srcML",
        "Lizard",
        "Apriori",
        "Java microbenchmarks",
        "JVM warmup",
        "steady state",
        "source-code features",
        "BMNFC",
        "BMLOC",
        "DNNSF"
      ]
    },
    {
      "paper_id": "uncovering-steady-state-executions-in-java-microbenchmarking-with-call-graph-analysis",
      "title": "Uncovering Steady State Executions in Java Microbenchmarking with Call Graph Analysis",
      "year": 2023,
      "authors": [
        "Madeline Janecek",
        "Sneh Patel",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/uncovering-steady-state-executions-in-java-microbenchmarking-with-call-graph-analysis/",
      "canonical_source_url": "https://doi.org/10.1145/3578245.3584689",
      "core_contribution": "The paper uses sampled call graphs and machine learning to distinguish warmup from steady-state executions in Java microbenchmarks.",
      "tags": [
        "performance-analysis",
        "performance-engineering",
        "machine-learning",
        "trace-analysis"
      ],
      "keywords": [
        "Java",
        "JMH",
        "microbenchmarking",
        "steady-state detection",
        "warmup",
        "call-graph analysis",
        "Perf",
        "Graph2Vec",
        "change-point detection",
        "PELT",
        "Random Forest",
        "SVC",
        "flame graphs"
      ]
    },
    {
      "paper_id": "iot-anomaly-intrusion-detection-poster-abstract",
      "title": "Deep Learning Driven Anomaly Based Intrusion Detection System for IoT: Poster Abstract",
      "year": 2022,
      "authors": [
        "Yue Guan",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/iot-anomaly-intrusion-detection-poster-abstract/",
      "canonical_source_url": "https://doi.org/10.1145/3565386.3565493",
      "core_contribution": "The poster proposes a hybrid IoT intrusion-detection pipeline with binary anomaly detection followed by multiclass attack classification.",
      "tags": [
        "iot-security",
        "anomaly-detection",
        "machine-learning",
        "deep-learning-systems"
      ],
      "keywords": [
        "IoTID20",
        "RNN",
        "SMOTE",
        "PSO",
        "binary classification",
        "multiclass classification",
        "IoT attacks",
        "intrusion detection"
      ]
    },
    {
      "paper_id": "malware-system-calls-detection-using-hybrid-system",
      "title": "Malware System Calls Detection Using Hybrid System",
      "year": 2021,
      "authors": [
        "Yue Guan",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/malware-system-calls-detection-using-hybrid-system/",
      "canonical_source_url": "https://doi.org/10.1109/SysCon48628.2021.9447094",
      "core_contribution": "The paper combines LTTng system-call monitoring with recurrent and Transformer-style models for malware and anomalous system-behavior detection.",
      "tags": [
        "kernel-tracing",
        "system-tracing",
        "anomaly-detection",
        "machine-learning",
        "observability"
      ],
      "keywords": [
        "malware detection",
        "system-call analysis",
        "LTTng",
        "Trace Compass",
        "LSTM",
        "Transformer",
        "multi-head attention",
        "PyTorch",
        "Linux VMs",
        "sequence classification",
        "precision",
        "recall",
        "unknown attacks"
      ]
    },
    {
      "paper_id": "anomaly-detection-ddos-attacks-cloud-environment",
      "title": "The Use of Anomaly Detection for the Detection of Different Types of DDoS Attacks in Cloud Environment",
      "year": 2021,
      "authors": [
        "Hossein Abbasi",
        "Naser Ezzati-Jivan",
        "Martine Bellaiche",
        "Chamseddine Talhi",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/anomaly-detection-ddos-attacks-cloud-environment/",
      "canonical_source_url": "https://doi.org/10.1007/s41635-021-00119-z",
      "core_contribution": "The paper proposes a cloud-side anomaly detector that combines traffic, virtual-machine resource, and kernel-level indicators to identify several DDoS classes through change-point evidence.",
      "tags": [
        "anomaly-detection",
        "machine-learning",
        "resource-analysis",
        "performance-analysis"
      ],
      "keywords": [
        "DDoS attacks",
        "cloud environment",
        "CUSUM",
        "bandwidth exhaustion",
        "application exhaustion",
        "connection exhaustion",
        "anomaly detection"
      ]
    },
    {
      "paper_id": "multi-level-host-based-intrusion-detection-system-for-internet-of-things",
      "title": "Multi-level Host-based Intrusion Detection System for Internet of Things",
      "year": 2020,
      "authors": [
        "Robin Gassais",
        "Naser Ezzati-Jivan",
        "Jose M. Fernandez",
        "Daniel Aloise",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/multi-level-host-based-intrusion-detection-system-for-internet-of-things/",
      "canonical_source_url": "https://doi.org/10.1186/s13677-020-00206-6",
      "core_contribution": "The paper combines host-level user/kernel tracing with machine learning to detect intrusion behavior on constrained IoT and home-automation systems.",
      "tags": [
        "iot-security",
        "kernel-tracing",
        "machine-learning",
        "anomaly-detection",
        "lttng",
        "observability"
      ],
      "keywords": [
        "IoT intrusion detection",
        "host-based IDS",
        "LTTng",
        "barectf",
        "Common Trace Format",
        "Babeltrace",
        "Raspberry Pi 3",
        "Home Assistant",
        "Decision Tree",
        "Random Forest",
        "Gradient Boosted Trees",
        "SVM",
        "MLP",
        "LSTM",
        "Mirai",
        "ransomware",
        "CPU overhead",
        "memory overhead"
      ]
    },
    {
      "paper_id": "machine-learning-based-edos-attack-detection-technique-using-execution-trace-analysis",
      "title": "Machine Learning-Based EDoS Attack Detection Technique Using Execution Trace Analysis",
      "year": 2019,
      "authors": [
        "Hossein Abbasi",
        "Naser Ezzati-Jivan",
        "Martine Bellaiche",
        "Chamseddine Talhi",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/machine-learning-based-edos-attack-detection-technique-using-execution-trace-analysis/",
      "canonical_source_url": "https://doi.org/10.1007/s41635-018-0061-2",
      "core_contribution": "The paper combines execution-trace and virtual-machine metrics with machine learning to detect EDoS behavior and restrict resource expansion to apparently normal VMs.",
      "tags": [
        "anomaly-detection",
        "kernel-tracing",
        "machine-learning",
        "performance-analysis",
        "resource-analysis",
        "system-tracing"
      ],
      "keywords": [
        "Economic Denial of Sustainability",
        "EDoS",
        "DDoS",
        "cloud computing",
        "execution trace analysis",
        "LTTng",
        "QEMU",
        "KVM",
        "Apache",
        "MySQL",
        "HTTPFlooder",
        "LoadRunner",
        "WEKA",
        "multilayer perceptron",
        "SVM",
        "Snort"
      ]
    },
    {
      "paper_id": "automated-categorizing-similarities-persian-news",
      "title": "New Approach for Automated Categorizing and Finding Similarities in Online Persian News",
      "year": 2010,
      "authors": [
        "Naser Ezzati Jivan",
        "Mahlagha Fazeli",
        "Khadije Sadat Yousefi"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/automated-categorizing-similarities-persian-news/",
      "canonical_source_url": "https://doi.org/10.1007/978-3-642-16032-5_11",
      "core_contribution": "The paper combines automated Persian-news categorization with a web system for retrieving similar news items.",
      "tags": [
        "machine-learning",
        "benchmark-datasets"
      ],
      "keywords": [
        "Persian news",
        "text categorization",
        "document similarity",
        "tf-idf",
        "SVM",
        "Reuters",
        "web crawler",
        "PHP",
        "keyword extraction",
        "semantic similarity"
      ]
    }
  ]
}