1,720,981 research outputs found
Reducing the False Negative Rate in Deep Learning Based Network Intrusion Detection Systems
No full textNetwork Intrusion Detection Systems (NIDS) represent a crucial component in the security of a system, and their role is to continuously monitor the network and alert the user of any suspicious activity or event. In recent years, the complexity of networks has been rapidly increasing and network intrusions have become more frequent and less detectable. The increase in complexity pushed researchers to boost NIDS effectiveness by introducing machine learning (ML) and deep learning (DL) techniques. However, even with the addition of ML and DL, some issues still need to be addressed: high false negative rates and low attack predictability for minority classes. Aim of the study was to address these problems that have not been adequately addressed in the literature. Firstly, we have built a deep learning model for network intrusion detection that would be able to perform both binary and multiclass classification of network traffic. The goal of this base model was to achieve at least the same, if not better, performance than the models observed in the state-of-the-art research. Then, we proposed an effective refinement strategy and generated several models for lowering the FNR and increasing the predictability for the minority classes. The obtained results proved that using the proper parameters is possible to achieve a satisfying trade-off between FNR, accuracy, and detection of the minority classes
Mining implicit data association from Tripadvisor hotel reviews
No full textIn this paper, we analyse a dataset of hotel reviews. In details, we enrich the review dataset, by extracting additional features, consisting of information on the reviewers’ profiles and the reviewed hotels. We argue that the enriched data can gain insights on the factors that most influence consumers when composing reviews (e.g., if the appreciation for a certain kind of hotel is tied to specific users’ profiles). Thus, we apply statistical analyses to reveal if there are specific characteristics of reviewers (almost) always related to specific characteristics of hotels. Our experiments are carried out on a very large dataset, consisting of around 190k hotel reviews, collected from the Tripadvisor websit
Mining implicit data association from Tripadvisor hotel reviews
No full textIn this paper, we analyse a dataset of hotel reviews. In details, we enrich the review dataset, by extracting additional features, consisting of information on the reviewers' profiles and the reviewed hotels. We argue that the enriched data can gain insights on the factors that most influence consumers when composing reviews (e.g., if the appreciation for a certain kind of hotel is tied to specific users' profiles). Thus, we apply statistical analyses to reveal if there are specific characteristics of reviewers (almost) always related to specific characteristics of hotels. Our experiments are carried out on a very large dataset, consisting of around 190k hotel reviews, collected from the Tripadvisor website
Experimental measures of news personalization in Google News
No full textSearch engines and social media keep trace of profile-and behavioral-based distinct signals of their users, to provide them personalized and recommended content. Here, we focus on the level of web search personalization, to estimate the risk of trapping the user into so called Filter Bubbles. Our experimentation has been carried out on news, specifically investigating the Google News platform. Our results are in line with existing literature and call for further analyses on which kind of users are the target of specific recommendations by Google
A taxonomy of distributed denial of service attacks
No full textThe Internet of Things revolution promises to make our lives much easier by providing us cheap and convenient smart devices, but all that glitters is not gold. This plethora of devices that flooded the market, generally poorly designed with respect to security aspects, brought back to the top Distributed Denial of Service (DDoS) attacks which are now even more powerful and easier to achieve than the past. Understanding how these attacks work, in all their different forms, represents a first crucial step to tackle this urgent issue. To this end, in this paper we propose a new up-to-date taxonomy and a comprehensive classification of current DDoS attacks
eRIPP-FS: Enforcing Privacy and Security in RFID
No full textIn RFID systems addressing security issues, many authentication techniques require the tag to keep some sort of synchronization with the reader. In particular, this is true in those proposals that leverage hash chains. When the reader and the tag get de-synchronized, possibly by an attacker, this paves the way to several denial of service (DoS) attacks, as well as threatening privacy (e.g., via the timing attack). Even if de-synchronization happens for non-malicious causes, this event has a negative effect on performances (for instance, slowing down the authentication process).
In this paper, we provide a solution to cope with the de-synchronization between the tag and the reader when hash chains are employed. In particular, our solution relies on mutual reader-tag authentication, achieved via hash traversal and Merkle tree techniques. We show that this techniques applied to an existing security protocol for RFID systems, such as RIPP-FS, make timing attacks hard to succeed. Moreover, the proposed solutions can be transparently and independently adopted by similar security protocols as well to thwart timing attack and/or to provide reader-tag mutual authentication.
Finally, extensive simulations show that our proposal introduces a negligible overhead to recover desynchronization
FastRIPP: RFID Privacy Preserving protocol with Forward Secrecy and Fast Resynchronisation
No full textBaRT, Balanced Randomized Tree: A scalable and distributed protocol for look-up in peer-to-peer networks
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