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See detailVirtual Network Embedding for NGSO Systems: Algorithmic Solution and SDN-Testbed Validation
Minardi, Mario UL; Vu, Thang Xuan UL; Lei, Lei UL et al

in IEEE Transactions on Network and Service Management (2022)

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See detailSwarm of UAVs for Network Management in 6G: A Technical Review
Khan, Muhammad Asghar; Kumar, Neeraj; Mohsan, Syed Agha Hassnain et al

in IEEE Transactions on Network and Service Management (2022)

Fifth-generation (5G) cellular networks have led to the implementation of beyond 5G (B5G) networks, which are capable of incorporating autonomous services to swarm of unmanned aerial vehicles (UAVs). They ... [more ▼]

Fifth-generation (5G) cellular networks have led to the implementation of beyond 5G (B5G) networks, which are capable of incorporating autonomous services to swarm of unmanned aerial vehicles (UAVs). They provide capacity expansion strategies to address massive connectivity issues and guarantee ultra-high throughput and low latency, especially in extreme or emergency situations where network density, bandwidth, and traffic patterns fluctuate. On the one hand, 6G technology integrates AI/ML, IoT, and blockchain to establish ultra-reliable, intelligent, secure, and ubiquitous UAV networks. 6G networks, on the other hand, rely on new enabling technologies such as air interface and transmission technologies, as well as a unique network design, posing new challenges for the swarm of UAVs.Keeping these challenges in mind, this article focuses on the security and privacy, intelligence, and energy-efficiency issues faced by swarms of UAVs operating in 6G mobile network. In this state-of-the-art review, we integrated blockchain and AI/ML with UAV networks utilizing the 6G ecosystem. The key findings are then presented, and potential research challenges are identified. We conclude the review by shedding light on future research in this emerging field of research. [less ▲]

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See detailPhishStorm: Detecting Phishing With Streaming Analytics
Marchal, Samuel UL; François, Jérôme UL; State, Radu UL et al

in IEEE Transactions on Network and Service Management (2014), 11(December), 458-471

Despite the growth of prevention techniques, phishing remains an important threat since the principal countermeasures in use are still based on reactive URL blacklisting. This technique is inefficient due ... [more ▼]

Despite the growth of prevention techniques, phishing remains an important threat since the principal countermeasures in use are still based on reactive URL blacklisting. This technique is inefficient due to the short lifetime of phishing Web sites, making recent approaches relying on real-time or proactive phishing URL detection techniques more appropriate. In this paper, we introduce PhishStorm, an automated phishing detection system that can analyze in real time any URL in order to identify potential phishing sites. PhishStorm can interface with any email server or HTTP proxy. We argue that phishing URLs usually have few relationships between the part of the URL that must be registered (low-level domain) and the remaining part of the URL (upper-level domain, path, query). We show in this paper that experimental evidence supports this observation and can be used to detect phishing sites. For this purpose, we define the new concept of intra-URL relatedness and evaluate it using features extracted from words that compose a URL based on query data from Google and Yahoo search engines. These features are then used in machine-learning-based classification to detect phishing URLs from a real dataset. Our technique is assessed on 96 018 phishing and legitimate URLs that result in a correct classification rate of 94.91% with only 1.44% false positives. An extension for a URL phishingness rating system exhibiting high confidence rate ( $>$ 99%) is proposed. We discuss in this paper efficient implementation patterns that allow real-time analytics using Big Data architectures such as STORM and advanced data structures based on the Bloom filter. [less ▲]

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See detailMachine Learning Techniques for Passive Network Inventory
François, Jérôme UL; Abdelnur, Humberto J.; State, Radu UL et al

in IEEE Transactions on Network and Service Management (2010), 7(4), 244-257

Being able to fingerprint devices and services, i.e., remotely identify running code, is a powerful service for both security assessment and inventory management. This paper describes two novel ... [more ▼]

Being able to fingerprint devices and services, i.e., remotely identify running code, is a powerful service for both security assessment and inventory management. This paper describes two novel fingerprinting techniques supported by isomorphic based distances which are adapted for measuring the similarity between two syntactic trees. The first method leverages the support vector machines paradigm and requires a learning stage. The second method operates in an unsupervised manner thanks to a new classification algorithm derived from the ROCK and QROCK algorithms. It provides an efficient and accurate classification. We highlight the use of such classification techniques for identifying the remote running applications. The approaches are validated through extensive experimentations on SIP (Session Initiation Protocol) for evaluating the impact of the different parameters and identifying the best configuration before applying the techniques to network traces collected by a real operator. [less ▲]

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