arXiv subjects

Parviz Rashidi-Khazaee

Publications and source records attributed to Parviz Rashidi-Khazaee.

3 recordsLinked to original sources

arXiv2025

A unified framework for detecting point and collective anomalies in operating system logs via collaborative transformers

Log anomaly detection is crucial for preserving the security of operating systems. Depending on the source of log data collection, various information is recorded in logs that can be considered log modalities. In light of this intuition, unimodal methods often struggle by ignoring the different modalities of log data. Meanwhile, multimodal methods fail to handle the interactions between these modalities. Applying multimodal sentiment analysis to log anomaly detection, we propose CoLog, a framework that collaboratively encodes logs utilizing various modalities. CoLog utilizes collaborative transformers and multi-head impressed attention to learn interactions among several modalities, ensuring comprehensive anomaly detection. To handle the heterogeneity caused by these interactions, CoLog incorporates a modality adaptation layer, which adapts the representations from different log modalities. This methodology enables CoLog to learn nuanced patterns and dependencies within the data, enhancing its anomaly detection capabilities. Extensive experiments demonstrate CoLog's superiority over existing state-of-the-art methods. Furthermore, in detecting both point and collective anomalies, CoLog achieves a mean precision of 99.63%, a mean recall of 99.59%, and a mean F1 score of 99.61% across seven benchmark datasets for log-based anomaly detection. The comprehensive detection capabilities of CoLog make it highly suitable for cybersecurity, system monitoring, and operational efficiency. CoLog represents a significant advancement in log anomaly detection, providing a sophisticated and effective solution to point and collective anomaly detection through a unified framework and a solution to the complex challenges automatic log data analysis poses. We also provide the implementation of CoLog at https://github.com/NasirzadehMoh/CoLog.

cs.LG↗

arXiv2024

Improving Deep Reinforcement Learning Agent Trading Performance in Forex using Auxiliary Task

Advanced algorithms based on Deep Reinforcement Learning (DRL) have been able to become a reliable tool for the Forex market traders and provide a suitable strategy for maximizing profit and reducing trading risk. These tools try to find the most profitable strategy in this market by examining past market data. Artificial intelligent agents based on the Proximal Policy Optimization (PPO) algorithm, one of the DRL algorithms, have shown a special ability to determine a profitable strategy. In this research, to increase profitability and determine the optimal strategy for the PPO, an auxiliary task has been used. The auxiliary function helps the PPO to model the reward function in a better way by recognizing and classifying patterns to obtain additional information from the problem's inputs and data. The results of simulation and backtesting on the EUR/USD currency pair have shown that the new proposed model has been able to increase the overall return from -25.25% to 14.86% and the value of sharp ratio from -2.61 to 0.24 in dataset 1 and increased overall return from 2.12% to 42.22% and the value of sharp ratio from -2.93 to 0.47 in dataset 2. These results indicate better identification of trends, timely trading, and great improvement in risk reduction while increasing the profit. It has also reduced the transaction risk and can be used as an effective tool for automatic trading in the complex forex market

cs.CE↗

arXiv2024

A Deep Reinforcement Learning Approach for Trading Optimization in the Forex Market with Multi-Agent Asynchronous Distribution

In today's forex market traders increasingly turn to algorithmic trading, leveraging computers to seek more profits. Deep learning techniques as cutting-edge advancements in machine learning, capable of identifying patterns in financial data. Traders utilize these patterns to execute more effective trades, adhering to algorithmic trading rules. Deep reinforcement learning methods (DRL), by directly executing trades based on identified patterns and assessing their profitability, offer advantages over traditional DL approaches. This research pioneers the application of a multi-agent (MA) RL framework with the state-of-the-art Asynchronous Advantage Actor-Critic (A3C) algorithm. The proposed method employs parallel learning across multiple asynchronous workers, each specialized in trading across multiple currency pairs to explore the potential for nuanced strategies tailored to different market conditions and currency pairs. Two different A3C with lock and without lock MA model was proposed and trained on single currency and multi-currency. The results indicate that both model outperform on Proximal Policy Optimization model. A3C with lock outperforms other in single currency training scenario and A3C without Lock outperforms other in multi-currency scenario. The findings demonstrate that this approach facilitates broader and faster exploration of different currency pairs, significantly enhancing trading returns. Additionally, the agent can learn a more profitable trading strategy in a shorter time.

cs.CE↗