AI Security and Privacy
- Federated Learning (FL) Based Anomaly Detection
- Securing FL systems
- Privacy improvement of FL
- Explainable AI (XAI) for Attack detection
- Attacking XAI
- AI based security for open RAN system
- Automated Zero-day attack detection
26 related publications
DisLLM: Distributed LLMs for Privacy Assurance in Resource-Constrained Environments
Large Language Models (LLMs) have revolutionized natural language processing, but deploying them in resource-constrained environments and privacy-sensitive domains remains challenging. This paper introduces the Distributed Large Language Model (DisLLM), a novel distributed learning framework that addresses privacy preservation and computational efficiency issues in LLM fine-tuning and inference. DisLLM leverages the
A survey on privacy of personal and non-personal data in B5G/6G networks
The upcoming Beyond 5G (B5G) and 6G networks are expected to provide enhanced capabilities such asultra-high data rates, dense connectivity, and high scalability. It opens many possibilities for a new generation of services driven by Artificial Intelligence (AI) and billions of interconnected smart devices. However, with this expected massive upgrade,
SHERPA: Explainable Robust Algorithms for Privacy-Preserved Federated Learning in Future Networks to Defend Against Data Poisoning Attacks
With the rapid progression of communication and localisation of big data over billions of devices, distributed Machine Learning (ML) techniques are emerging to cater for the development of Artificial Intelligence (AI)-based services in a distributed manner. Federated Learning (FL) is such an innovative approach to achieve a privacy-preserved AI that
Rec-Def: A Recommendation-based Defence Mechanism for Privacy Preservation in Federated Learning Systems
An emergence of attention and regulations on consumer privacy can be observed over the recent years with the ubiquitous availability of IoT systems handling personal data. Federated Learning (FL) arises as a privacy-preserved Machine Learning (ML) technique where data can be kept private within these devices without transmitting to third
From Opacity to Clarity: Leveraging XAI for Robust Network Traffic Classification
A wide adoption of Artificial Intelligence (AI) can be observed in recent years over networking to provide zero-touch, full autonomy of services towards the next generation Beyond 5G (B5G)/6G. However, AI-driven attacks on these services are a major concern in reaching the full potential of this future vision. Identifying how
Privacy-preserved Collaborative Federated Learning Platform for Industrial Internet of Things
Federated learning (FL) is an intriguing approach to privacy-preserving collaborative learning. Decentralised FL is achieving increased favour for investigation due to the mitigation of vulnerability for a single point of failure and more controllability for end users over their models. However, many existing decentralised FL systems face limitations, such as
FL-TIA: Novel Time Inference Attacks on Federated Learning
Federated Learning (FL) is an emerging privacy-preserved distributed Machine Learning (ML) technique where multiple clients can contribute to training an ML model without sharing private data. Even though FL offers a certain level of privacy by design, recent works show that FL is vulnerable to numerous privacy attacks. One of
A Federated Learning Approach for Improving Security in Network Slicing
Network Slicing (NS) is a predominant technology in future telecommunication networks, including Fifth Generation (5G), which supports the realization of heterogeneous applications and services. It allows the allocation of a dedicated logical network slice of the physical network to each application. Security is one of the paramount challenges in an
Service Migration Authentication Protocol for MEC
Multi-Access Edge Computing (MEC) is a novel edge computing paradigm that enhances the access level capacity of mobile networks by shifting the serviceable Data center infrastructure proximate to the end devices. With this proximate placement and service provisioning, migration of a service from one edge enabled gNodeB (gNB) to another
A Survey on Privacy for B5G/6G: New Privacy Challenges, and Research Directions
Massive developments in mobile wireless telecommunication networks have been made during the last few decades. At present, mobile users are getting familiar with the latest 5G networks, and the discussion for the next generation of Beyond 5G (B5G)/6G networks has already been initiated. It is expected that B5G/6G will push
An improved and provably secure symmetric-key based 5G-AKA Protocol
One of the primary authentication mechanisms defined for the 5G system is the 5G-Authentication and Key Agreement (5G-AKA) protocol. It is set to be used in the next generation of mobile communications but has several serious flaws such as privacy issues, vulnerability to traceability attacks, and has de-synchronization problem. To deal with
A survey on the convergence of edge computing and AI for UAVs: Opportunities and challenges
The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial intelligence (AI) technologies, for instance, computer vision and path planning. These AI methods must process data and provide
An EAP-Based Mutual Authentication Protocol for WLAN-Connected IoT Devices
Several symmetric and asymmetric encryption based authentication protocols have been developed for the wireless local area networks (WLANs). However, recent findings reveal that these protocols are either vulnerable to numerous attacks or computationally expensive. Considering the demerits of these protocols and the necessity to provide enhanced security, a lightweight extensible
Robust and Resilient Federated Learning for Securing Future Networks
Machine Learning (ML) and Artificial Intelligence (AI) techniques are widely adopted in the telecommunication industry, especially to automate beyond 5G networks. Federated Learning (FL) recently emerged as a distributed ML approach that enables localized model training to keep data decentralized to ensure data privacy. In this paper, we identify the
Federated Learning based Anomaly Detection as an Enabler for Securing Network and Service Management Automation in Beyond 5G Networks
Network automation is a necessity in order to meet the unprecedented demand in the future networks and zero touch network architecture is proposed to cater such requirements. Closed-loop and artificial intelligence are key enablers in this proposed architecture in critical elements such as security. Apart from the arising privacy concerns,
LEMAP: A Lightweight EAP based Mutual Authentication Protocol for IEEE 802.11 WLAN
The growing usage of wireless devices has significantly increased the need for Wireless Local Area Network (WLAN) during the past two decades. However, security (most notably authentication) remains a major roadblock to WLAN adoption. Several authentication protocols exist for verifying a supplicant’s identity who attempts to connect his wireless device
Security enhanced Emergency Situation Detection System for Ambient Assisted LivingSystem for Ambient Assisted Living
Typical wearable devices use a dedicated mobile phone as relay node to transfer the collected sensor data toa server. However, such relay nodes can be faulty or inactive due to various reasons, leading to interruptions of the communication link. To mitigate this challenge, we propose anovel security-enhanced emergency situation detection
AI and 6G security: Opportunities and challenges
While 5G is well-known for network cloudification with micro-service based architecture, the next generation networks or the 6G era is closely coupled with intelligent network orchestration and management. Hence, the role of Artificial Intelligence (AI) is immense in the envisioned 6G paradigm. However, the alliance between 6G and AI may
Survey on Multi-Access Edge Computing Security and Privacy
The European Telecommunications Standards Institute (ETSI) has introduced the paradigm of Multi-Access Edge Computing (MEC) to enable efficient and fast data processing in mobile networks. Among other technological requirements, security and privacy are significant factors in the realization of MECdeployments. In this paper, we analyse the security and privacy of
A survey on mobile augmented reality with 5G mobile edge computing: architectures, applications, and technical aspects
The Augmented Reality (AR) technology enhances the human perception of the world by combining the real environment with the virtual space. With the explosive growth of powerful, less expensive mobile devices, and the emergence of sophisticated communication infrastructure, Mobile Augmented Reality (MAR) applications are gaining increased popularity. MAR allows users
Secure and user efficient eap-based authentication protocol for ieee 802.11 wireless lans
Wireless Local Area Networks (WLANs) have experienced significant growth in the last two decades due to the extensive use of wireless devices. Security (especially authentication) is a staple concern as the wireless medium is accessible to everybody. Extensible Authentication Protocol (EAP) is thewidely used authentication framework in WLANs to secure
Security Considerations for Internet of Things: A Survey
Interconnecting “things” and devices that takes the form of wearables, sensors, actuators, mobiles, computers, meters, or even vehicles is a critical requirement for the current era. These inter-networked connections are serving the emerging applications home and building automation, smart cities and infrastructure, smart industries, and smart-everything. However, the security of
Dynamic Orchestration of Security Services at Fog Nodes for 5G IoT
Fog Computing is one of the edge computing paradigms that envisages being the proximate processing and storage infrastructure for a multitude of IoT appliances. With its dynamic deployability as a medium level cloud service, fog nodes are enabling heterogeneous service provisioning infrastructure that features scalability, interoperability, and adaptability. Out of
Security as a Service Platform Leveraging
Multi-Access Edge Computing Infrastructure
Provisions
The mobile service platform envisaged by emerging IoT and 5G is guaranteeing gigabit-level bandwidth, ultra-low latency and ultra-high storage capacity for their subscribers. In The mobile service platform envisaged by emerging IoT and 5G is guaranteeing gigabit-level bandwidth, ultra-low latency and ultra-high storage capacity for their subscribers. In spite of
Realizing Multi-Access Edge Computing
Feasibility: Security Perspective
Internet of Things (IoT) and 5G are emerging technologies that prompt a mobile service platform capable of provisioning billions of communication devices which enable ubiquitous computing and ambient intelligence. These novel approaches are guaranteeing gigabit-level bandwidth, ultra-low latency and ultra-high storage capacity for their subscribers. To achieve these limitations, ETSI
Introduction to IoT Security
In a world with “things” and devices interconnected at every level, from wearables to home and building automation, to smart cities and infrastructure, to smart industries, and to smart everything, the Internet of Things (IoT) security plays a centric role with no margin for error or shortage on supply. Securing,