Information Forensics & Security is a scientific journal published by the IEEE Signal Processing Society.

NetSpam: a Network-based Spam Detection Framework for Reviews in Online Social Media

Introduction:

Nowadays, a big part of people rely on available content in social media in their decisions (e.g. reviews and feedback on a topic or product). The possibility that anybody can leave a review provide a golden opportunity for spammers to write spam reviews about products and services for different interests. Identifying these spammers and the spam content is a hot topic of research and although a considerable number of studies have been done recently toward this end, but so far the methodologies put forth still barely detect spam reviews, and none of them show the importance of each extracted feature type. In this study, we propose a novel framework, named NetSpam, which utilizes spam features for modeling review datasets as heterogeneous information networks to map spam detection procedure into a classification problem in such networks. Using the importance of spam features help us to obtain better results in terms of different metrics experimented on real-world review datasets from Yelp and Amazon websites. The results show that NetSpam outperforms the existing methods and among four categories of features; including review-behavioral, user-behavioral, review linguistic, user-linguistic, the first type of features performs better than the other categories.

Reference IEEE paper:

“NetSpam: a Network-based Spam Detection Framework for Reviews in Online Social Media”, IEEE Transactions on Information Forensics and Security, 2017.

Unique ID – SBI1074

Domain – INFORMATION FORENSICS & SECURITY

 

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Preventing Distributed Denial of Service Flooding Attacks with Dynamic Path Identifiers

Introduction:

In recent years, there are increasing interests in using path identifiers (PIDs) as inter-domain routing objects. However, the PIDs used in existing approaches are static, which makes it easy for attackers to launch distributed denial-of service (DDoS) flooding attacks. To address this issue, in this paper, we present the design, implementation, and evaluation of D-PID, a framework that uses PIDs negotiated between neighbouring domains as inter-domain routing objects. In DPID, the PID of an inter-domain path connecting two domains is kept secret and changes dynamically. We describe in detail how neighbouring domains negotiate PIDs, how to maintain ongoing communications when PIDs change. We build a 42-node prototype comprised by six domains to verify D-PID’s feasibility and conduct extensive simulations to evaluate its effectiveness and cost. The results from both simulations and experiments show that D-PID can effectively prevent DDoS attacks.

Reference IEEE paper:

“Preventing Distributed Denial-of-Service Flooding Attacks with Dynamic Path Identifiers”, IEEE TRANSACTIONS ON INFORMATION AND FORENSICS SECURITY, 2017.

Unique ID – SBI1075

Domain – INFORMATION FORENSICS & SECURITY

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Twitter Trends Manipulation: A First Look Inside the Security of Twitter Trending

Introduction:

Twitter trends, a timely updated set of top terms in Twitter, have the ability to affect the public agenda of the community and have attracted much attention. Unfortunately, in the wrong hands, Twitter trends can also be abused to mislead people. In this paper, we attempt to investigate whether Twitter trends are secure from the manipulation of malicious users. We collect more than 69 million tweets from 5 million accounts. Using the collected tweets, we first conduct a data analysis and discover evidence of Twitter trend manipulation. Then, we study at the topic level and infer the key factors that can determine whether a topic starts trending due to its popularity, coverage, transmission, potential coverage, or reputation. What we find is that except for transmission, all of factors above are closely related to trending. Finally, we further investigate the trending manipulation from the perspective of compromised and fake accounts and discuss countermeasures.

Reference IEEE paper:

“Twitter Trends Manipulation: A First Look Inside the Security of Twitter Trending”, IEEE Transactions on Information Forensics and Security, 2017.

Unique ID – SBI1076

DomainINFORMATION FORENSICS & SECURITY

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