Efficient Detection of Attacks in SIP Based VoIP Networks Using Linear l1-SVM Classifier

The Session Initiation Protocol (SIP) is one of the most common protocols that are used for signaling function in Voice over IP (VoIP) networks. The SIP protocol is very popular because of its flexibility, simplicity, and easy implementation, so it is a target of many attacks. In this paper, we propose a new system to detect the Denial of Service (DoS) attacks (i.e. malformed message and invite flooding) and Spam over Internet Telephony (SPIT) attack in the SIP based VoIP networks using a linear Support Vector Machine with l1 regularization (i.e. l1-SVM) classifier. In our approach, we project the SIP messages into a very high dimensional space using string based n-gram features. Hence, a linear classifier is trained on the top of these features. Our experimental results show that the proposed system detects malformed message, invite flooding, and SPIT attacks with a high accuracy. In addition, the proposed system outperformed other systems significantly in the detection speed

Efficient detection of spam over internet telephony by machine learning algorithms

Recent trends show a growing interest in VoIP services and indicate that guaranteeing security in VoIP services and preventing hacker communities from attacking telecommunication solutions is a challenging task. Spam over Internet Telephony (SPIT) is a type of attack which is a significant detriment to the user's experience. A number of techniques have been produced to detect SPIT calls. We reviewed these techniques and have proposed a new approach for quick, efficient and highly accurate detection of SPIT calls using neural networks and novel call parameters. The performance of this system was compared to other state-of-art machine learning algorithms on a real-world dataset, which has been published online and is publicly available. The results of the study demonstrated that new parameters may help improve the effectiveness and accuracy of applied machine learning algorithms. The study explored the entire process of designing a SPIT detection algorithm, including data collection and processing, defining suitable parameters, and final evaluation of machine learning models.Web of Science1013342613341

INSTANT MESSAGING SPAM DETECTION IN LONG TERM EVOLUTION NETWORKS

The lack of efficient spam detection modules for packet data communication is resulting to increased threat exposure for the telecommunication network users and the service providers. In this thesis, we propose a novel approach to classify spam at the server side by intercepting packet-data communication among instant messaging applications. Spam detection is performed using machine learning techniques on packet headers and contents (if unencrypted) in two different phases: offline training and online classification. The contribution of this study is threefold. First, it identifies the scope of deploying a spam detection module in a state-of-the-art telecommunication architecture. Secondly, it compares the usefulness of various existing machine learning algorithms in order to intercept and classify data packets in near real-time communication of the instant messengers. Finally, it evaluates the accuracy and classification time of spam detection using our approach in a simulated environment of continuous packet data communication. Our research results are mainly generated by executing instances of a peer-to-peer instant messaging application prototype within a simulated Long Term Evolution (LTE) telecommunication network environment. This prototype is modeled and executed using OPNET network modeling and simulation tools. The research produces considerable knowledge on addressing unsolicited packet monitoring in instant messaging and similar applications

Multimedia

The nowadays ubiquitous and effortless digital data capture and processing capabilities offered by the majority of devices, lead to an unprecedented penetration of multimedia content in our everyday life. To make the most of this phenomenon, the rapidly increasing volume and usage of digitised content requires constant re-evaluation and adaptation of multimedia methodologies, in order to meet the relentless change of requirements from both the user and system perspectives. Advances in Multimedia provides readers with an overview of the ever-growing field of multimedia by bringing together various research studies and surveys from different subfields that point out such important aspects. Some of the main topics that this book deals with include: multimedia management in peer-to-peer structures & wireless networks, security characteristics in multimedia, semantic gap bridging for multimedia content and novel multimedia applications

Network communication privacy: traffic masking against traffic analysis

An increasing number of recent experimental works have been demonstrating the supposedly secure channels in the Internet are prone to privacy breaking under many respects, due to traffic features leaking information on the user activity and traffic content. As a matter of example, traffic flow classification at application level, web page identification, language/phrase detection in VoIP communications have all been successfully demonstrated against encrypted channels. In this thesis I aim at understanding if and how complex it is to obfuscate the information leaked by traffic features, namely packet lengths, direction, times. I define a security model that points out what the ideal target of masking is, and then define the optimized and practically implementable masking algorithms, yielding a trade-off between privacy and overhead/complexity of the masking algorithm. Numerical results are based on measured Internet traffic traces. Major findings are that: i) optimized full masking achieves similar overhead values with padding only and in case fragmentation is allowed; ii) if practical realizability is accounted for, optimized statistical masking algorithms attain only moderately better overhead than simple fixed pattern masking algorithms, while still leaking correlation information that can be exploited by the adversary

Denial of Service in Web-Domains: Building Defenses Against Next-Generation Attack Behavior

The existing state-of-the-art in the field of application layer Distributed Denial of Service (DDoS) protection is generally designed, and thus effective, only for static web domains. To the best of our knowledge, our work is the first that studies the problem of application layer DDoS defense in web domains of dynamic content and organization, and for next-generation bot behaviour. In the first part of this thesis, we focus on the following research tasks: 1) we identify the main weaknesses of the existing application-layer anti-DDoS solutions as proposed in research literature and in the industry, 2) we obtain a comprehensive picture of the current-day as well as the next-generation application-layer attack behaviour and 3) we propose novel techniques, based on a multidisciplinary approach that combines offline machine learning algorithms and statistical analysis, for detection of suspicious web visitors in static web domains. Then, in the second part of the thesis, we propose and evaluate a novel anti-DDoS system that detects a broad range of application-layer DDoS attacks, both in static and dynamic web domains, through the use of advanced techniques of data mining. The key advantage of our system relative to other systems that resort to the use of challenge-response tests (such as CAPTCHAs) in combating malicious bots is that our system minimizes the number of these tests that are presented to valid human visitors while succeeding in preventing most malicious attackers from accessing the web site. The results of the experimental evaluation of the proposed system demonstrate effective detection of current and future variants of application layer DDoS attacks

Data Hiding and Its Applications

Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of the increased dissemination and distribution of multimedia content over the internet, data hiding methods, such as digital watermarking and steganography, are becoming increasingly relevant in providing multimedia security. The goal of this book is to focus on the improvement of data hiding algorithms and their different applications (both traditional and emerging), bringing together researchers and practitioners from different research fields, including data hiding, signal processing, cryptography, and information theory, among others

Distributed Network Monitoring for Distributed Denial of Service Attacks Detection and Prevention

There are two main categories of Distributed Denial of Service (DDoS) attacks that are capable of disrupting the daily operations of internet users and these are the low and high rate DDoS attacks. The detection and prevention of DDoS attacks is a very important aspect in network security in ensuring that the operations of businesses, communication, and educational facilities operate efficiently without disruptions. Over the years, many DDoS attacks detection systems have been proposed. These detection systems have focused more on obtaining high accuracy, reduction of false alarm rates and simplification of detection systems. However, less attention has been given to the computational costs of detection systems (processing power requirements and memory consumptions), early detection and flexibility in their deployment to support the different needs of networks and distributed monitoring approaches. The focus of this thesis is to investigate the use of a robust feature selection approach and machine learning classifiers to develop useful DDoS detection architectures for fast, effective, and efficient DDoS attacks detection to achieve high performance at low computational cost. To achieve this, a lightweight software architecture which is simple in design using minimal number of network flow features for distinguishing normal from DDoS attack network flows is proposed. The architecture is based on the Decision-Tree (DT) classifier and distinguishes DDoS attack from normal traffic network flows with a detection accuracy of over 99.9% when evaluated with up-to-date DDoS attack datasets. In addition, it can flexibly be deployed in a real-time network environment and at different network nodes to meet the needs of the network being monitored creating an avenue for distributed monitoring. Also, the use of minimal network flow features selected through a robust features selection approach results in a massive reduction in memory requirements when compared to traditional systems. Results from the software implementation of the architecture indicated that it uses just 7% processing power of a core of the detection system’s CPU in offline mode and provides no additional overhead to the monitored network. However, software applications for distinguishing normal from DDoS attack traffic are struggling to cope with the ever-increasing complexity and intensity of DDoS attack traffic. This increased workload ranges from the capturing and processing of millions of packets per second to classification of thousands of network flows per second which is evident in some of the most recent DDoS attacks faced by a variety of companies. To cope with this workload, a hardware accelerated hybrid network monitoring application is proposed. The proposed application is capable of fast network flows classification by leveraging the hardware parallel processing characteristics of a Field Programmable Gate Array (FPGA) whilst using a software application in the CPU for the network flow pre-processing required for classification. The hybrid system is capable of distinguishing DDoS attacks from normal network traffic flows with a detection accuracy of over 98% when deployed in a real-time environment under different network traffic conditions with detection in 1µs which is over thirty times faster than the software implementation of the architecture. The hardware accelerated application was implemented in the Zynq-7000 All Programmable SoCs ZedBoard which can monitor up to 1Gbps line rate. The evaluation results and findings from analysis of the experimental results of the hard ware accelerated application provide some important insights in improving the programmability, overall performance, scalability, and flexibility in deployment of the detection system across a network for accurate and early DDoS attack detection. In the final part of this thesis, the use of distributed network monitoring is explored with the implementation of the lightweight DDoS attacks detection architecture using Network Simulator 3 (NS-3). The systems are distributed at different parts of a network and results from the approach indicated that effective implementation of distributed network monitoring systems dramatically reduces the effect of DDoS attack to a minimal on the target network or network node

Journal of Telecommunications and Information Technology, 2014, nr 4

kwartalni

Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico

Conference proceedings info: ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies Raleigh, HI, United States, March 24-26, 2023 Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementación sistemática de la telemedicina dentro de un gran centro de evaluación de COVID-19 en el área de Baja California, México. Nuestro modelo se basa en factores de diseño centrados en el ser humano y colaboraciones interdisciplinarias para la habilitación escalable basada en datos de tecnologías de teleconsulta de teléfonos inteligentes, celulares y video para vincular hospitales, clínicas y servicios médicos de emergencia para evaluaciones de COVID en el punto de atención. pruebas, y para el tratamiento posterior y decisiones de cuarentena. Rápidamente se creó un equipo multidisciplinario, en cooperación con diferentes instituciones, entre ellas: la Universidad Autónoma de Baja California, la Secretaría de Salud, el Centro de Comando, Comunicaciones y Control Informático. de la Secretaría del Estado de Baja California (C4), Facultades de Medicina y Colegio de Psicólogos. Nuestro objetivo es proporcionar información al público y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignación de recursos con la anticipación de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-