187 citations · 277 across the 17 of their papers we have counts for
19 papers
EarlyMalDetect: A Novel Approach for Early Windows Malware Detection Based on Sequences of API Calls
Pascal Maniriho, Abdun Naser Mahmood, Mohammad Jabed Morshed Chowdhury
In this work, we propose EarlyMalDetect, a novel approach for early Windows malware detection based on sequences of API calls. Our approach leverages generative transformer models…
Decentralized Federated Anomaly Detection in Smart Grids: A P2P Gossip Approach
Muhammad Akbar Husnoo, Adnan Anwar, Md Enamul Haque +1
The increasing security and privacy concerns in the Smart Grid sector have led to a significant demand for robust intrusion detection systems within critical smart grid infrastruct…
FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination
Muhammad Akbar Husnoo, Adnan Anwar, Haftu Tasew Reda +4
With the growing concern about the security and privacy of smart grid systems, cyberattacks on critical power grid components, such as state estimation, have proven to be one of th…
FeDiSa: A Semi-asynchronous Federated Learning Framework for Power System Fault and Cyberattack Discrimination
Muhammad Akbar Husnoo, Adnan Anwar, Haftu Tasew Reda +4
With growing security and privacy concerns in the Smart Grid domain, intrusion detection on critical energy infrastructure has become a high priority in recent years. To remedy the…
MalDetConv: Automated Behaviour-based Malware Detection Framework Based on Natural Language Processing and Deep Learning Techniques
Pascal Maniriho, Abdun Naser Mahmood, Mohammad Jabed Morshed Chowdhury
The popularity of Windows attracts the attention of hackers/cyber-attackers, making Windows devices the primary target of malware attacks in recent years. Several sophisticated mal…
A Secure Federated Learning Framework for Residential Short Term Load Forecasting
Muhammad Akbar Husnoo, Adnan Anwar, Nasser Hosseinzadeh +3
Smart meter measurements, though critical for accurate demand forecasting, face several drawbacks including consumers' privacy, data breach issues, to name a few. Recent literature…
Deep Learning Models for Detecting Malware Attacks
Pascal Maniriho, Abdun Naser Mahmood, Mohammad Jabed Morshed Chowdhury
Malware is one of the most common and severe cyber-attack today. Malware infects millions of devices and can perform several malicious activities including mining sensitive data, e…
Adversarial Models Towards Data Availability and Integrity of Distributed State Estimation for Industrial IoT-Based Smart Grid
Haftu Tasew Reda, Abdun Mahmood, Adnan Anwar +1
Security issue of distributed state estimation (DSE) is an important prospect for the rapidly growing smart grid ecosystem. Any coordinated cyberattack targeting the distributed sy…
FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providers
Muhammad Akbar Husnoo, Adnan Anwar, Nasser Hosseinzadeh +3
As Smart Meters are collecting and transmitting household energy consumption data to Retail Energy Providers (REP), the main challenge is to ensure the effective use of fine-graine…
False Data Injection Threats in Active Distribution Systems: A Comprehensive Survey
Muhammad Akbar Husnoo, Adnan Anwar, Nasser Hosseinzadeh +3
With the proliferation of smart devices and revolutions in communications, electrical distribution systems are gradually shifting from passive, manually-operated and inflexible one…
A Taxonomy of Cyber Defence Strategies Against False Data Attacks in Smart Grid
Haftu Tasew Reda, Adnan Anwar, Abdun Naser Mahmood +1
Modern electric power grid, known as the Smart Grid, has fast transformed the isolated and centrally controlled power system to a fast and massively connected cyber-physical system…
Comprehensive Survey and Taxonomies of False Injection Attacks in Smart Grid: Attack Models, Targets, and Impacts
Haftu Tasew Reda, Adnan Anwar, Abdun Mahmood
Smart Grid has rapidly transformed the centrally controlled power system into a massively interconnected cyber-physical system that benefits from the revolutions happening in the c…
Modeling and performance evaluation of stealthy false data injection attacks on smart grid in the presence of corrupted measurements
Adnan Anwar, Abdun Naser Mahmood, Mark Pickering
The false data injection (FDI) attack cannot be detected by the traditional anomaly detection techniques used in the energy system state estimators. In this paper, we demonstrate h…
Vulnerabilities of Smart Grid State Estimation against False Data Injection Attack
A. Anwar, A. N. Mahmood
In recent years, Information Security has become a notable issue in the energy sector. After the invention of The Stuxnet worm in 2010, data integrity, privacy and confidentiality…
Swarm Intelligence Based Multi-phase OPF For Peak Power Loss Reduction In A Smart Grid
Adnan Anwar, A. N. Mahmood
Recently there has been increasing interest in improving smart grids efficiency using computational intelligence. A key challenge in future smart grid is designing Optimal Power Fl…
Enhanced Estimation of Autoregressive Wind Power Prediction Model Using Constriction Factor Particle Swarm Optimization
Adnan Anwar, Abdun Naser Mahmood
Accurate forecasting is important for cost-effective and efficient monitoring and control of the renewable energy based power generation. Wind based power is one of the most diffic…
Uncertainty And Evolutionary Optimization: A Novel Approach
Maumita Bhattacharya, R. Islam, A. Mahmood
Evolutionary algorithms (EA) have been widely accepted as efficient solvers for complex real world optimization problems, including engineering optimization. However, real world op…
Cyber Security of Smart Grid Infrastructure
Adnan Anwar, Abdun Naser Mahmood
Smart grid security is crucial to maintain stable and reliable power system operation during the contingency situation due to the failure of any critical power system component. En…
Random Shuffling to Reduce Disorder in Adaptive Sorting Scheme
Md. Enamul Karim, Abdun Naser Mahmood
In this paper we present a random shuffling scheme to apply with adaptive sorting algorithms. Adaptive sorting algorithms utilize the presortedness present in a given sequence. We…