Search Results for author:
Found 15 papers, 4 papers with code
SAQL: A Stream-based Query System for Real-Time Abnormal System Behavior Detection
To facilitate the task of expressing anomalies based on expert knowledge, our system provides a domain-specific query language, SAQL, which allows analysts to express models for (1) rule-based anomalies, (2) time-series anomalies, (3) invariant-based anomalies, and (4) outlier-based anomalies.
Cryptography and Security Databases
Enabling Efficient Cyber Threat Hunting With Cyber Threat Intelligence
Log-based cyber threat hunting has emerged as an important solution to counter sophisticated attacks.
TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking
Traditional eye tracking requires specialized hardware, which means collecting gaze data from many observers is expensive, tedious and slow.
Machine Learning Methods for Attack Detection in the Smart Grid
The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods.
Fusion of Image Segmentation Algorithms using Consensus Clustering
A new segmentation fusion method is proposed that ensembles the output of several segmentation algorithms applied on a remotely sensed image.
A Random Walk Based Model Incorporating Social Information for Recommendations
Collaborative filtering (CF) is one of the most popular approaches to build a recommendation system.
Wisdom of the Crowd: Incorporating Social Influence in Recommendation Models
In this paper, we propose two recommendation models, for individuals and for groups respectively, based on social contagion and social influence network theory.
Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge
At each iteration of FL, a subset of the devices are scheduled to transmit their local model updates to the PS over orthogonal channel resources, while each participating device must compress its model update to accommodate to its link capacity.
Federated Learning With Quantized Global Model Updates
We analyze the convergence behavior of the proposed LFL algorithm assuming the availability of accurate local model updates at the server.
Convergence of Federated Learning over a Noisy Downlink
The PS has access to the global model and shares it with the devices for local training, and the devices return the result of their local updates to the PS to update the global model.
Blind Federated Edge Learning
At each iteration, wireless devices perform local updates using their local data and the most recent global model received from the PS, and send their local updates to the PS over a wireless fading multiple access channel (MAC).
A System for Efficiently Hunting for Cyber Threats in Computer Systems Using Threat Intelligence
Log-based cyber threat hunting has emerged as an important solution to counter sophisticated cyber attacks.
Federated Learning with Downlink Device Selection
At each iteration, the PS broadcasts different quantized global model updates to different participating devices based on the last global model estimates available at the devices.
Byzantine-Robust Clustered Federated Learning
This paper focuses on the problem of adversarial attacks from Byzantine machines in a Federated Learning setting where non-Byzantine machines can be partitioned into disjoint clusters.
DASA: Delay-Adaptive Multi-Agent Stochastic Approximation
We consider a setting in which $N$ agents aim to speedup a common Stochastic Approximation (SA) problem by acting in parallel and communicating with a central server.
