Toward Self-Organising Service Communities

This paper discusses a framework in which catalog service communities are built, linked for interaction, and constantly monitored and adapted over time. A catalog service community (represented as a peer node in a peer-to-peer network) in our system can be viewed as domain specific data integration mediators representing the domain knowledge and the registry information. The query routing among communities is performed to identify a set of data sources that are relevant to answering a given query. The system monitors the interactions between the communities to discover patterns that may lead to restructuring of the network (e.g., irrelevant peers removed, new relationships created, etc.)

How to accelerate your internet : a practical guide to bandwidth management and optimisation using open source software

xiii, 298 p. : ill. ; 24 cm.Libro ElectrónicoAccess to sufficient Internet bandwidth enables worldwide electronic collaboration, access to informational resources, rapid and effective communication, and grants membership to a global community. Therefore, bandwidth is probably the single most critical resource at the disposal of a modern organisation. The goal of this book is to provide practical information on how to gain the largest possible benefit from your connection to the Internet. By applying the monitoring and optimisation techniques discussed here, the effectiveness of your network can be significantly improved

A Blockchain-Based Retribution Mechanism for Collaborative Intrusion Detection

Collaborative intrusion detection approach uses the shared detection signature between the collaborative participants to facilitate coordinated defense. In the context of collaborative intrusion detection system (CIDS), however, there is no research focusing on the efficiency of the shared detection signature. The inefficient detection signature costs not only the IDS resource but also the process of the peer-to-peer (P2P) network. In this paper, we therefore propose a blockchain-based retribution mechanism, which aims to incentivize the participants to contribute to verifying the efficiency of the detection signature in terms of certain distributed consensus. We implement a prototype using Ethereum blockchain, which instantiates a token-based retribution mechanism and a smart contract-enabled voting-based distributed consensus. We conduct a number of experiments built on the prototype, and the experimental results demonstrate the effectiveness of the proposed approach

Word Embeddings for Fake Malware Generation

Signature and anomaly-based techniques are the fundamental methods to detect malware. However, in recent years this type of threat has advanced to become more complex and sophisticated, making these techniques less effective. For this reason, researchers have resorted to state-of-the-art machine learning techniques to combat the threat of information security. Nevertheless, despite the integration of the machine learning models, there is still a shortage of data in training that prevents these models from performing at their peak. In the past, generative models have been found to be highly effective at generating image-like data that are similar to the actual data distribution. In this paper, we leverage the knowledge of generative modeling on opcode sequences and aim to generate malware samples by taking advantage of the contextualized embeddings from BERT. We obtained promising results when differentiating between real and generated samples. We observe that generated malware has such similar characteristics to actual malware that the classifiers are having difficulty in distinguishing between the two, in which the classifiers falsely identify the generated malware as actual malware almost of the time

Impact of Location Spoofing Attacks on Performance Prediction in Mobile Networks

Performance prediction in wireless mobile networks is essential for diverse purposes in network management and operation. Particularly, the position of mobile devices is crucial to estimating the performance in the mobile communication setting. With its importance, this paper investigates mobile communication performance based on the coordinate information of mobile devices. We analyze a recent 5G data collection and examine the feasibility of location-based performance prediction. As location information is key to performance prediction, the basic assumption of making a relevant prediction is the correctness of the coordinate information of devices given. With its criticality, this paper also investigates the impact of position falsification on the ML-based performance predictor, which reveals the significant degradation of the prediction performance under such attacks, suggesting the need for effective defense mechanisms against location spoofing threats

Twitter Bots’ Detection with Benford’s Law and Machine Learning

Online Social Networks (OSNs) have grown exponentially in terms of active users and have now become an influential factor in the formation of public opinions. For this reason, the use of bots and botnets for spreading misinformation on OSNs has become a widespread concern. Identifying bots and botnets on Twitter can require complex statistical methods to score a profile based on multiple features. Benford’s Law, or the Law of Anomalous Numbers, states that, in any naturally occurring sequence of numbers, the First Significant Leading Digit (FSLD) frequency follows a particular pattern such that they are unevenly distributed and reducing. This principle can be applied to the first-degree egocentric network of a Twitter profile to assess its conformity to such law and, thus, classify it as a bot profile or normal profile. This paper focuses on leveraging Benford’s Law in combination with various Machine Learning (ML) classifiers to identify bot profiles on Twitter. In addition, a comparison with other statistical methods is produced to confirm our classification results

A Blockchain-Based Tamper-Resistant Logging Framework

Since its introduction in Bitcoin, the blockchain has proven to be a versatile data structure. In its role as an immutable ledger, it has grown beyond its initial use in financial transactions to be used in recording a wide variety of other useful information. In this paper, we explore the application of the blockchain outside of its traditional decentralized, financial domain. We show how, even with only a single “mining” node, a proof-of-work blockchain can be the cornerstone of a tamper resistant logging framework. By attaching a proof-of-work to blocks of logging messages, we make it increasingly difficult for an attacker to modify those logs even after totally compromising the system. Furthermore, we discuss various strategies an attacker might take to modify the logs without detection and show how effective those evasion techniques are against statistical analysis

Robustness of Image-Based Malware Analysis

In previous work, “gist descriptor” features extracted from images have been used in malware classification problems and have shown promising results. In this research, we determine whether gist descriptors are robust with respect to malware obfuscation techniques, as compared to Convolutional Neural Networks (CNN) trained directly on malware images. Using the Python Image Library (PIL), we create images from malware executables and from malware that we obfuscate. We conduct experiments to compare classifying these images with a CNN as opposed to extracting the gist descriptor features from these images to use in classification. For the gist descriptors, we consider a variety of classification algorithms including k-nearest neighbors, random forest, support vector machine, and multi-layer perceptron. We find that gist descriptors are more robust than CNNs, with respect to the obfuscation techniques that we consider

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges