Publikasi Archives - Page 3 of 6 - Program Studi S1 Informatika

Machine Learning-Driven Scaling and Placement of Virtual Network Functions at the Network Edges

January 21, 2023/in Publikasi /by Informatika

Machine Learning-Driven Scaling and Placement of Virtual Network Functions at the Network Edges

by Tejas Subramanya, Roberto Riggio

2019 IEEE Conference on Network Softwarization (NetSoft)

Network Function Virtualization is a promising technology that proposes to decouple the network functions from their underlying hardware and transform them into software entities called Virtual Network Functions (VNFs). This approach offers network operators with more flexibility to instantiate, configure, scale, and migrate VNFs at runtime depending on the demand. On introducing these VNFs at the network edges (e.g., base stations), emerging use cases such as connected cars can be supported. However, in such an environment, efficient VNF placement and orchestration mechanisms are needed to address the challenges of continuously changing network dynamics, service latency requirements and user mobility patterns. The purpose of this paper is twofold. Firstly, we propose a neural-network model (i.e., a subset of machine learning) that can assist in proactive auto-scaling by predicting the number of VNF instances required as a function of the network traffic they should process. Based on the traffic traces collected over a commercial mobile network, the model achieves a prediction accuracy of 97%. Secondly, we provide an Integer Linear Programming formulation for placing these VNFs at the edge nodes with a primary objective of minimizing end-to-end latency from all users to their respective VNFs. Our results show an improvement in latency by upto 75% when VNFs are placed at the network edges.

Botnet detection based on network flow summary and deep learning

January 14, 2023/in Publikasi /by Informatika

Botnet detection based on network flow summary and deep learning

by Abdurrahman Pektaş, Tankut Acarman

International Journal of Network Management

A botnet is a group of compromised Internet-connected devices controlled remotely by cyber criminals to launch coordinated attacks and to perform various malicious activities. Since botnets continuously adapt themselves to the evolving countermeasures introduced by both network and host-based detection mechanism, the traditional approaches do not provide adequate protection to botnet threat. On the one hand, behavioral analysis of network traffic can play a key role to detect botnets. For instance, behavioral analysis can be applied to observe and discover communication patterns that botnets operate during their life cycle. On the other hand, deep learning has been successfully applied to various classification tasks, and it is also a promising solution for botnet discovery. In this paper, we apply deep neural network to detect botnet by modeling network traffic flow. The performance of the proposed method is evaluated with publicly available large-scale communication traces. The experimental results illustrate that deep learning is an efficient and effective method for identifying botnet traffic with a high true positive rate (attack detection rate) and low false positive alarm rate.

Network anomaly detection based on logistic regression of nonlinear chaotic invariants

January 13, 2023/in Publikasi /by Informatika

Network anomaly detection based on logistic regression of nonlinear chaotic invariants

by Francesco Palmieri

Journal of Network and Computer Applications

Network anomaly detection has the essential goal of reliably identifying malicious activities within traffic observations collected at specific monitoring points, in order to raise alarms and timely trigger specific reactions and countermeasures. This, ideally, should be done also in presence of previously unknown phenomena, also known as zero-day attacks. However, distinguishing anomalous events due to attacks from normal spikes or sharp variations in traffic flows can become a classic “finding a needle in a haystack” problem, due to the very complex and unpredictable nature of Internet traffic, which is extremely affected by randomness and background noise effects. To face this challenge we leveraged machine learning for developing a novel network anomaly detection solution, based on the exploitation of nonlinear invariant properties of the Internet traffic. These properties, by capturing its chaotic and fractal features, are better suited to represent the more intrinsic and discriminative dynamics within an inductively learned model to be used for effectively classifying, through logistic regression, previously unseen traffic aggregates or individual flows into “normal” or “anomalous” ones. The results of the performance evaluation, obtained within a standard and reproducible experimental validation framework, show that the approach is able to effectively isolate very different kinds of volumetric Denial of Service attacks within the context of complex mixes of traffic flows, with really satisfactory accuracy and precision.

Reliability for Smart Healthcare: A Network Slicing Perspective

January 11, 2023/in Publikasi /by Informatika

Reliability for Smart Healthcare: A Network Slicing Perspective

by Andressa Vergutz, Guevara Noubir, Michele Nogueira

Institute of Electrical and Electronics Engineers (IEEE)

Pursuing improvements in the healthcare system is mandatory for its efficiency and cost reduction. The fast popularization of implantable and wearable sensors promotes the diversity of healthcare applications and services, ranging from real-time and critical care monitoring to telemedicine. For smart healthcare (s-health), reliability plays an essential role, given the sensitivity of its data and services. In this article, we envision an architecture based on network slicing that can provide reliability for s-health applications and services. The architecture relies on fingerprinting healthcare applications to quickly customize resources and meet the level of reliability required for each s-health application. A fingerprinting study case is presented for s-health, based on a dataset containing real traffic. Results show that application fingerprinting reaches 90 percent accuracy, assisting in network customization. Finally, we discuss the main open issues and opportunities that network slicing technology provides for s-health.

Learning-Assisted Secure End-to-End Network Slicing for Cyber-Physical Systems

January 10, 2023/in Publikasi /by Informatika

Learning-Assisted Secure End-to-End Network Slicing for Cyber-Physical Systems

by Qiang Liu, Tao Han, Nirwan Ansari

Institute of Electrical and Electronics Engineers (IEEE)

There is a pressing need to interconnect physical systems such as power grid and vehicles for efficient management and safe operations. Due to the diverse features of physical systems, there is hardly a one-size-fits-all networking solution for developing cyber-physical systems. Network slicing is a promising technology that allows network operators to create multiple virtual networks on top of a shared network infrastructure. These virtual networks can be tailored to meet the requirements of different cyber-physical systems. However, it is challenging to design secure network slicing solutions that can efficiently create end-to-end network slices for diverse cyber-physical systems. In this article, we discuss the challenges and security issues of network slicing, study learning-assisted network slicing solutions, and analyze their performance under the denial-of-service attack. We also present a design and implementation of a small-scale testbed for evaluating the network slicing solutions.

Efficient Coastal Communications with Sparse Network Coding

January 7, 2023/in Publikasi /by Informatika

Efficient Coastal Communications with Sparse Network Coding

by Ye Li, Jue Wang, Shibing Zhang, Zhihua Bao, Jiangzhou Wang

Institute of Electrical and Electronics Engineers (IEEE)

The demand for wideband communication in the coastal area (i.e., ≤ 100 km from the coastline) has been rapidly increasing in recent years. Compared to the terrestrial scenario, the coastal environment has long-distance and highly dynamic channels, and the communication devices are more strictly constrained by energy supplies. While the RLNC has the fountain erasure-correction property and is suitable for transmissions over the long-distance dynamic channels, it suffers from high coding coefficient delivery cost and decoding complexity. In this article, we look into the application of sparse network coding in coastal communication systems. We identify two typical multicast scenarios that may appear in coastal communications, namely the relay-aided multicast and multicast from a shore-based base station with D2D communication enabled among the subscribers. We provide a detailed comparison of existing sparse network coding schemes. Based on that, we demonstrate through simulations that an appropriate choice of sparse codes is critical to meet the unique requirements in coastal communication systems. We show that batched sparse code is suitable for relay-aided multicast, and subset-based sparse codes are preferable for D2D-enabled multicast.

A Service-Based Architecture for Enabling UAV Enhanced Network Services

January 7, 2023/in Publikasi /by Informatika

A Service-Based Architecture for Enabling UAV Enhanced Network Services

by Oussama Bekkouche, Konstantinos Samdanis, Miloud Bagaa, Tarik Taleb

Institute of Electrical and Electronics Engineers (IEEE)

This article provides an overview of enhanced network services, while emphasizing the role of UAVs as core network equipment with radio and backhaul capabilities. Initially, we elaborate the various deployment options, focusing on UAVs as airborne radio, backhaul and core network equipment, pointing out the benefits and limitations. We then analyze the required enhancements in the SBA to support UAV services including UAV navigation and air traffic management, weather forecasting and UAV connectivity management. The use of airborne UAVs network services is assessed via qualitative means, considering the impact on vehicular applications. Finally, an evaluation has been conducted via a testbed implementation, to explore the performance of UAVs as edge cloud nodes, hosting an ACS function responsible for the control and orchestration of a UAV fleet.

Privacy in Neural Network Learning: Threats and Countermeasures

January 7, 2023/in Publikasi /by Informatika

Privacy in Neural Network Learning: Threats and Countermeasures

by Shan Chang, Chao Li

Institute of Electrical and Electronics Engineers (IEEE)

Algorithmic breakthroughs, the feasibility of collecting huge amount of data, and increasing computational power, contribute to the remarkable achievements of NNs. In particular, since Deep Neural Network (DNN) learning presents astonishing results in speech and image recognition, the amount of sophisticated applications based on it has exploded. However, the increasing number of instances of privacy leakage has been reported, and the corresponding severe consequences have caused great worry in this area. In this article, we focus on privacy issues in NN learning. First, we identify the privacy threats during NN training, and present privacy-preserving training schemes in terms of using centralized and distributed approaches. Second, we consider the privacy of prediction requests, and discuss the privacy-preserving protocols for NN prediction. We also analyze the privacy vulnerabilities of trained models. Three types of attacks on private information embedded in trained NN models are discussed, and a differential privacy-based solution is introduced.

Psychological resilience correlates with EEG source-space brain network flexibility

January 7, 2023/in Publikasi /by Informatika

Psychological resilience correlates with EEG source-space brain network flexibility

by Véronique Paban, Julien Modolo, Ahmad Mheich, Mahmoud Hassan

MIT Press – Journals

We aimed at identifying the potential relationship between the dynamical properties of the human functional network at rest and one of the most prominent traits of personality, namely resilience. To tackle this issue, we used resting-state EEG data recorded from 45 healthy subjects. Resilience was quantified using the 10-item Connor-Davidson Resilience Scale (CD-RISC). By using a sliding windows approach, brain networks in each EEG frequency band (delta, theta, alpha, and beta) were constructed using the EEG source-space connectivity method. Brain networks dynamics were evaluated using the network flexibility, linked with the tendency of a given node to change its modular affiliation over time. The results revealed a negative correlation between the psychological resilience and the brain network flexibility for a limited number of brain regions within the delta, alpha, and beta bands. This study provides evidence that network flexibility, a metric of dynamic functional networks, is strongly correlated with psychological resilience as assessed from personality testing. Beyond this proof-of-principle that reliable EEG-based quantities representative of personality traits can be identified, this motivates further investigation regarding the full spectrum of personality aspects and their relationship with functional networks.

AI-Empowered Maritime Internet of Things: A Parallel-Network-Driven Approach

January 7, 2023/in Publikasi /by Informatika

AI-Empowered Maritime Internet of Things: A Parallel-Network-Driven Approach

by Tingting Yang, Jiacheng Chen, Ning Zhang

Institute of Electrical and Electronics Engineers (IEEE)

As one of the key technologies for realizing a fully digitalized world, the Internet of Things (IoT) requires ubiquitous connections across both land and sea. However, due to lack of infrastructure such as optical fibers and base stations, maritime communications inevitably face a highly complex and heterogeneous environment, which greatly challenges the connection reliability and traffic steering efficiency for future service-oriented maritime IoT. With the recent burgeoning application of artificial intelligence (AI) in many fields, an AI-empowered autonomous network for maritime IoT is envisioned as a promising solution. However, AI typically involves training/learning processes, which require realistic data/environment in order to obtain valuable outcomes. To this end, this article proposes the parallel network, which can be regarded as the “digital twin” of the real network and is responsible for realizing four key functionalities: self-learning and optimizing, state inference and network cognition, event prediction and anomaly detection, and knowledge database and snapshots. We then explain how various AI methods can facilitate the operation of the parallel- network-driven maritime network. A case study is provided to demonstrate the idea. Research directions are also outlined for further studies.

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