Rahim TaheriUniversity of Portsmouth · School of Computing
Rahim Taheri
Ph.d
About
22
Publications
7,508
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362
Citations
Citations since 2017
Introduction
Rahim Taheri is a Lecturer (Assistant Professor) in Cyber Security and Forensics at the University of Portsmouth, UK. Before joining the University of Portsmouth, he was a postdoctoral at King’s Communications, Learning and Information Processing (kclip) lab, King’s College London, UK. He has a Ph.D in information technology—computer networking from Shiraz University of Technology, and in 2018, he was a visiting Ph.D student in the SPRITZ Security and Privacy Research group at the UNPD.
Additional affiliations
June 2021 - May 2022
Position
- Reserach Associate
Description
- Research in King’s Communications, Learning and Information Processing (kclip) lab
September 2015 - January 2020
September 2013 - September 2015
Education
September 2015 - January 2020
Publications
Publications (22)
Label manipulation attacks are a subclass of data poisoning attacks in adversarial machine learning used against different applications, such as malware detection. These types of attacks represent a serious threat to detection systems in environments having high noise rate or uncertainty, such as complex networks and Internet of Thing (IoT). Recent...
In this paper, we develop four malware detection methods using Hamming distance to find similarity between samples which are first nearest neighbors (FNN), all nearest neighbors (ANN), weighted all nearest neighbors (WANN), and k-medoid based nearest neighbors (KMNN). In our proposed methods, we can trigger the alarm if we detect an Android app is...
Cloud envisioned Cyber-Physical Systems (CCPS) is a practical technology that relies on the interaction among cyber elements like mobile users to transfer data in cloud computing. In CCPS, cloud storage applies data deduplication techniques aiming to save data storage and bandwidth for real-time services. In this infrastructure, data deduplication...
Authentication protocols are powerful tools to ensure confidentiality as an important feature of Internet of Things (IoT). The denial of service (DoS) attack is one of the significant threats to availability, as another essential feature of IoT, which deprives users of services by consuming the energy of IoT nodes. On the other hand, computational...
The sheer volume of Industrial Internet of Things (IIoT) malware is one of the most serious security threats in today’s interconnected world, with new types of advanced persistent threats and advanced forms of obfuscations. This paper presents a robust Federated Learning-based architecture called Fed-IIoT for detecting Android malware applications...
O-RAN reaches the goal of network flexibility through AIenabled functions, particularly with machine learning (ML) systems. Hence, the role of AI/ML is immense in the envisioned 6G paradigm. However, the alliance between O-RAN and AI/ML may also be a double-edged sword in 6G as their applicability for protecting or infringing security and privacy....
In recent years, malware detection has become an active research topic in the area of Internet of Things (IoT) security. The principle is to exploit knowledge from large quantities of continuously generated malware. Existing algorithms practise available malware features for IoT devices and lack real-time prediction behaviours. More research is thu...
A rising number of botnet families have been successfully detected using deep learning architectures. While the variety of attacks increases, these architectures should become more robust against attacks. They have been proven to be very sensitive to small but well constructed perturbations in the input. Botnet detection requires extremely low fals...
In recent years, malware detection has become an active research topic in the area of Internet of Things (IoT) security. The principle is to exploit knowledge from large quantities of continuously generated malware. Existing algorithms practice available malware features for IoT devices and lack real-time prediction behaviors. More research is thus...
Microgrids are industrial technologies that can provide energy resources for the Internet of things (IoT) demands in smart grids. Hybrid microgrids supply quality power to the IoT devices and ensure high resiliency in supply and demand for PV-based grid-tied microgrids. In this system, the usage of predictive energy management systems (EMS) is esse...
The volume of malware and the number of attacks in IoT devices are rising everyday, which encourages security professionals to continually enhance their malware analysis tools. Researchers in the field of cyber security have extensively explored the usage of sophisticated analytics and the efficiency of malware detection. With the introduction of n...
In this paper, we propose two defense methods against adversarial attack to a malware detection system for mobile multimedia applications in IoT environments. They are Robust-NN and a combination of convolutional neural network and 1- nearest neighbors(C4N) which modify training data that has been poisoned by an adversarial attack. As a result, the...
The widespread adoption of smartphones dramatically increases the risk of attacks and the spread of mobile malware, especially on the Android platform. Machine learning-based solutions have been already used as a tool to supersede signature-based anti-malware systems. However, malware authors leverage features from malicious and legitimate samples...
The widespread acceptance of smartphones has dramatically increased the risk of attack and spread of cellphone malware, especially within Android platform. Machine learning based solutions have been used as a tool in anti-malware systems. Malware authors also use the characteristics of malicious and benign instances in order to estimate statistical...
Intrusion Detection System (IDS) can identify the malicious exercises and anomalies in the network and present robust protection for the network systems. Also, clustering of attacks in IDS is important for defining defense policies. Identifying appropriate number of clusters is one of the issues that several scholars in literature are dealing with...
Deep learning-based communication using autoencoder has revolutionized the design of the physical layer in wireless communication. In this paper, we propose an adversarial autoencoder to mitigate vulnerability of autoencoder against adversarial attacks. Results confirm the effectiveness of adversarial training by reducing block error rate (BLER) fr...
Short Message Service (SMS) is one of the mobile communication services that allows easy and inexpensive communication. Producing unwanted messages with the aim of advertising or harassment and sending these messages on SMS have become the biggest challenge in this service. Various methods have been presented to detect unsolicited short messages; m...
An Intrusion detection system in the cyber-networks is one of the most important lines of defense against the threats.Two main challenges in the field of intrusion detection systems are their ability to work in real-time domain and their attack detection accuracy. Elimination of non-critical features and discretization are two systematic ways to re...
Two main problems that most Data Mining systems face with are high volume of training data and uncertainty in information. To solve these problems, the methods of discretization are used. The discretization of data is very useful for the automatic production of the numeric data conceptual hierarchy. In this paper, InfoGainAttributeEval , GainRatioA...
Energy limitations in wireless sensor networks has always been a critical issue. In designing routing protocols, the energy consumed while transferring data from sensor node to destination is considered. Most researches on wireless sensor networks are performed to decrease electric power consumption, because each node's required power is supplied b...
ELearning is coming from applying information technology (IT) in learning. ELearning can be widely presented in an efficient and cheap price method. An ELearning framework based on open standards with little initial capital can provide knowledge-based economy by means of public learning. Still, e-learning framework consumes lots of money for buildi...
There are numerous intrusion detection systems with different methods for detection of the attacks whose main challenge is enhancement of the efficiency and accuracy rates. Therefore, development of methods to enhance their efficiency is necessary. In this paper, we are searching for a solution to reduce number of the features. Cuttlefish algorithm...
Questions
Questions (2)
Is it possible to implement it with Keras and similar packages?
IOT : Internet Of Things
Projects
Projects (3)
Microgrids are industrial technologies that can provide energy resources for the Internet of things (IoT) demands in smart grids. Hybrid microgrids supply quality power to the IoT devices and ensure high resiliency in supply and demand for PV-based grid-tied microgrids. In this system, the usage of predictive energy management systems (EMS) is essential to dispatch power from different resources, whilst the battery energy storage system (BESS) is feeding the loads. In this work, we deploy a one-day-ahead prediction algorithm using a deep neural network for a fast-response BESS in an intelligent energy management system (I-EMS) that is called SIEMS. The main role of the SIEMS is to maintain the state of charge at high rates based on the one-day-ahead information about solar power, which depends on meteorological conditions. The remaining power is supplied by the main grid for sustained power streaming between BESS and end-users. Considering the usage of information and communication technology components in the microgrids, the main objective of this paper is to focus on the hybrid microgrid performance under cyber-physical security adversarial attacks. Fast gradient sign, basic iterative, and DeepFool methods, are investigated for the first time in power systems e.g. smart grids and microgrids, in order to produce perturbation for training data. To secure the microgrid's SIEMS, we propose two Defence algorithms based on defensive distillation and adversarial training strategies for the first time in EMSs. We apply and evaluate these benchmark adversarial attack and Defence methods against the proposed machine learning models to increase the robustness of the models in the system against adversarial attacks.
Working on designing and implementing Bayesian learning algorithms for Spiking Neural Networks.
In this Project, I am studying new algorithms based on Adversarial Machine Learning and especially Deep Learning methods to solve Malware Detection problems more efficiently and effectively.
