# Detection of Insider Attacks in Distributed Projected Subgradient Algorithms

arXiv: https://arxiv.org/abs/2101.06917 · HTML: https://mlsanity.com/paper/2101.06917/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 43 references that are in the map.
- First posted on arXiv 18 Jan 2021.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks](https://mlsanity.com/paper/1912.13163/) (2019)
- [Collaborative Deep Learning in Fixed Topology Networks](https://mlsanity.com/paper/1706.07880/) (2017)
- [Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks](https://mlsanity.com/paper/1712.00162/) (2017)
- [Distributed Optimization Under Adversarial Nodes](https://mlsanity.com/paper/1606.08939/) (2016)
- [GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descent](https://mlsanity.com/paper/1803.05880/) (2018)
- [Monitoring and Optimization for Power Grids: A Signal Processing Perspective](https://mlsanity.com/paper/1302.0885/) (2013)
- [GoSGD: Distributed Optimization for Deep Learning with Gossip Exchange](https://mlsanity.com/paper/1804.01852/) (2018)
- [Consensus based Detection in the Presence of Data Falsification Attacks](https://mlsanity.com/paper/1504.03413/) (2015)
- [AST-Based Deep Learning for Detecting Malicious PowerShell](https://mlsanity.com/paper/1810.09230/) (2018)
- [Detection and Isolation of Adversaries in Decentralized Optimization for Non-Strongly Convex Objectives](https://mlsanity.com/paper/1910.13020/) (2019)
- [Distributed Wideband Sensing-based Architecture for Unlicensed Massive IoT Communications](https://mlsanity.com/paper/1812.06238/) (2018)

## Abstract (arXiv)

The gossip-based distributed algorithms are widely used to solve decentralized optimization problems in various multi-agent applications, while they are generally vulnerable to data injection attacks by internal malicious agents as each agent locally estimates its decent direction without an authorized supervision. In this work, we explore the application of artificial intelligence (AI) technologies to detect internal attacks. We show that a general neural network is particularly suitable for detecting and localizing the malicious agents, as they can effectively explore nonlinear relationship underlying the collected data. Moreover, we propose to adopt one of the state-of-art approaches in federated learning, i.e., a collaborative peer-to-peer machine learning protocol, to facilitate training our neural network models by gossip exchanges. This advanced approach is expected to make our model more robust to challenges with insufficient training data, or mismatched test data. In our simulations, a least-squared problem is considered to verify the feasibility and effectiveness of AI-based methods. Simulation results demonstrate that the proposed AI-based methods are beneficial to improve performance of detecting and localizing malicious agents over score-based methods, and the peer-to-peer neural network model is indeed robust to target issues.