Henric Johnson

SOLA: a one-bit identity authentication protocol for access control in IEEE 802.11

Given the wide deployment of IPSec/VPN (virtual private networks) technology, there might be a redundancy in security protection in some configurations. Various commercial companies have replaced 802.11 security with IPSec/VPN to protect the wireless LAN (local area network). How to do it in an efficient and lightweight way is a challenging research problem. This paper introduces a new lightweight identity authentication protocol, SOLA (Statistical One-bit Lightweight Authentication), for access control well suited for IEEE 802.11 networks with IP connections. This protocol prevents unauthorized access on a per packet basis. Since SOLA only adds one identity bit to each packet it will have a low impact on the network bandwidth and power consumption. The performance and efficiency of the SOLA protocol together with IEEE 802.11 is analyzed and evaluated via simulation.

Open Data for Anomaly Detection in Maritime Surveillance

Maritime surveillance has received increased attention from a civilian perspective in recent years. Anomaly detection is one of many techniques available for improving the safety and security in this domain. Maritime authorities use confidential data sources for monitoring the maritime activities; however, a paradigm shift on the Internet has created new open sources of data. We investigate the potential of using open data as a complementary resource for anomaly detection in maritime surveillance. We present and evaluate a decision support system based on open data and expert rules for this purpose. We conduct a case study in which experts from the Swedish coastguard participate to conduct a real-world validation of the system. We conclude that the exploitation of open data as a complementary resource is feasible since our results indicate improvements in the efficiency and effectiveness of the existing surveillance systems by increasing the accuracy and covering unseen aspects of maritime activities.

On user perception of web login — A study on QoE in the context of security

As there is a lack of such studies, this paper investigates user perception (Quality of Experience, QoE) of the response times (Quality of Service, QoS) of a web authentication procedure, in particular a login to a community web page. Comparing the results to well-known user perception of web performance, we show that the users perceive logins in a similar way as standard web pages, which means that similar limits on user patience apply. The derived QoE-QoS relationship, an exponential function, serves then as the basis for assessing the performance of authentication algorithms in the domain of user acceptability.

Finding Influential Users in Social Media Using Association Rule Learning

Influential users play an important role in online social networks since users tend to have an impact on one other. Therefore, the proposed work analyzes users and their behavior in order to identi ...

Read What You Trust: An Open Wiki Model Enhanced by Social Context

Wiki systems, such as Wikipedia, provide a multitude of opportunities for large-scale online knowledge collaboration. Despite Wikipedias successes with the open editing model, dissenting voices give rise to unreliable content due to conflicts amongst contributors. From our perspective, the conflict issue results from presenting the same knowledge to all readers, without regard for the importance of the underlying social context, which both reveals the bias of contributors and influences the knowledge perception of readers. Motivated by the insufficiency of the existing knowledge presentation model for Wiki systems, this paper presents Trust Wiki, a new Wiki model which leverages social context, including social background and relationship information, to present readers with personalized and credible knowledge. Our experiment shows, with reliable social context information, Trust Wiki can efficiently assign readers to their compatible editor community and present credible knowledge derived from that community. Although this new Wiki model focuses on reinforcing the neutrality policy of Wikipedia, it also casts light on the other content reliability problems in Wiki systems, such as vandalism and minority opinion suppression.

Crawling Online Social Networks

Researchers put in tremendous amount of time and effort in order to crawl the information from online social networks. With the variety and the vast amount of information shared on online social networks today, different crawlers have been designed to capture several types of information. We have developed a novel crawler called SINCE. This crawler differs significantly from other existing crawlers in terms of efficiency and crawling depth. We are getting all interactions related to every single post. In addition, are we able to understand interaction dynamics, enabling support for making informed decisions on what content to re-crawl in order to get the most recent snapshot of interactions. Finally we evaluate our crawler against other existing crawlers in terms of completeness and efficiency. Over the last years we have crawled public communities on Facebook, resulting in over 500 million unique Facebook users, 50 million posts, 500 million comments and over 6 billion likes.

Accurate Adware Detection Using Opcode Sequence Extraction

Adware represents a possible threat to the security and privacy of computer users. Traditional signature-based and heuristic-based methods have not been proven to be successful at detecting this type of software. This paper presents an adware detection approach based on the application of data mining on disassembled code. The main contributions of the paper is a large publicly available adware data set, an accurate adware detection algorithm, and an extensive empirical evaluation of several candidate machine learning techniques that can be used in conjunction with the algorithm. We have extracted sequences of opcodes from adware and benign software and we have then applied feature selection, using different configurations, to obtain 63 data sets. Six data mining algorithms have been evaluated on these data sets in order to find an efficient and accurate detector. Our experimental results show that the proposed approach can be used to accurately detect both novel and known adware instances even though the binary difference between adware and legitimate software is usually small.

On the Concept of Trust in Online Social Networks

Online Social Networks (OSNs), such as Facebook, Twitter, and Myspace, provide new and interesting ways to communicate, share, and meet on the Internet. On the one hand, these features have arguably made many of the OSNs quite popular among the general population but the growth of these networks has raised issues and concerns related to trust, privacy and security. On the other hand, some would argue that the true potential of OSNs has yet to be unleashed. The mainstream media have uncovered a rising number of potential and occurring problems, including: incomprehensible security settings, unlawful spreading of private or copyrighted information, the occurrence of threats and so on. We present a set of approaches designed to improve the trustworthiness of OSNs. Each approach is described and related to ongoing research projects and to views expressed about trust by surveyed OSN users. Finally, we present some interesting pointers to future work.

SOLA: lightweight security for access control in IEEE 802.11

The IEEE 802.11 wireless standard provides little support for secure access control. As a result, access control in IEEE 802.11 on a per packet basis requires a new and robust identity authentication protocol. The SOLA (Statistical One-Bit Lightweight Authentication) protocol is well suited in a wireless constrained environment because this protocols communication overhead is extremely low: only one bit. Furthermore, SOLA fulfills the requirements of being secure, useful, cheap, and robust. The synchronization algorithm performs very well. SOLA also makes it easy to develop a framework to detect and respond to, for instance, denial-of-service attacks or an adversary who tries to guess the identity authentication bit for successive packets.

Leveraging Social Interactions to Suggest Friends

Over the past decade Online Social Networks (OSNs) have made it possible for people to stay in touch with people they already know in real life; although, they have not been able to allow users to grow their personal social network. Existence of many successful dating and friend finder applications online today show the need and importance of such applications. In this paper, we describe an application that leverages social interactions in order to suggest people to users that they may find interesting. We allow users to expand their personal social network using their own interactions with other users on public pages and groups in OSNs. We finally evaluate our application by selecting a random set of users and asking them for their honest opinion.