A survey on the cyber security of Small-to-Medium businesses: Challenges, research focus and recommendations

Small-to-medium sized businesses (SMBs) constitute a large fraction of many countries’ economies but according to the literature SMBs are not adequately implementing cyber security which leaves them susceptible to cyber-attacks. Furthermore, research in cyber security is rarely focused on SMBs, despite them representing a large proportion of businesses. In this paper we review recent research on the cyber security of SMBs, with a focus on the alignment of this research to the popular NIST Cyber Security Framework (CSF). From the literature we also summarise the key challenges SMBs face in implementing good cyber security and conclude with key recommendations on how to implement good cyber security. We find that research in SMB cyber security is mainly qualitative analysis and narrowly focused on the Identify and Protect functions of the NIST CSF with very little work on the other existing functions. SMBs should have the ability to detect, respond and recover from cyber-attacks, and if research lacks in those areas, then SMBs may have little guidance on how to act. Future research in SMB cyber security should be more balanced and researchers should adopt well-established powerful quantitative research approaches to refine and test research whilst governments and academia are urged to invest in incentivising researchers to expand their research focus

Valence, arousal and dominance estimation for English, German, Greek,Portuguese and Spanish lexica using semantic models

We propose and evaluate the use of an affective-semantic model to expand the affective lexica of German, Greek, English, Spanish and Portuguese. Motivated by the assumption that semantic similarity implies affective similarity, we use word level semantic similarity scores as semantic features to estimate their corresponding affective scores. Various context-based semantic similarity metrics are investigated using contextual features that include both words and character n-grams. The model produces continuous affective ratings in three dimensions (valence, arousal and dominance) for all five languages, achieving consistent performance. We achieve classification accuracy (valence polarity task) between 85% and 91% for all five languages. For morphologically rich languages the proposed use of character n-grams is shown to improve performance

Modelling email traffic workloads with RNN and LSTM models

Analysis of time series data has been a challenging research subject for decades. Email traffic has recently been modelled as a time series function using a Recurrent Neural Network (RNN) and RNNs were shown to provide higher prediction accuracy than previous probabilistic models from the literature. Given the exponential rise of email workloads which need to be handled by email servers, in this paper we first present and discuss the literature on modelling email traffic. We then explain the advantages and limitations of different approaches as well as their points of agreement and disagreement. Finally, we present a comprehensive comparison between the performance of RNN and Long Short Term Memory (LSTM) models. Our experimental results demonstrate that both approaches can achieve high accuracy over four large datasets acquired from different universities’ servers, outperforming existing work, and show that the use of LSTM and RNN is very promising for modelling email traffic

Bandwidth allocation for integrated MPEG-4 Video, WAP and MP3 traffic over next generation wireless cellular networks

In this paper we explore, via an extensive simulation study, the performance of a Medium Access Control (MAC) protocol when integrating video, WAP and MP3 data packet traffic over a wireless picocellular system of very high capacity, with errors. Mobile terminals are considered to be high performance devices with extended storage capabilities which can act like cache memories streaming multimedia material. Our scheme achieves high aggregate channel throughput in all cases of traffic load, despite the very bursty nature of the examined traffic and the introduction of error in the system

Call Admission Control for wireless videoconference traffic based on the users' willingness to pay

Network designers face a challenging problem when trying to control the traffic entered into a wireless cellular network. The reason is the rapidly growing number of multimedia applications, combined with user mobility. Additionally, the existence of multiple cellular providers leads to strong competition among them. Providers need to use efficient resource management schemes in order to keep existing clients satisfied and attract new customers, so that they can increase their revenue. In this work, we use traffic modeling for H.264 videoconference traffic in the implementation of a new Call Admission Control mechanism which makes decisions on the possible acceptance of a video call into the network not only based on the predicted bandwidth that users will need, but also on the revenue that the provider will make when degrading current users in order to accommodate new ones. Our mechanism is shown, via an extensive simulation study, to provide high Quality of Service (QoS) to wireless H.264 videoconference users

Scheduling for telemedicine traffic transmission over WLANs

The major disadvantage of the Enhanced Distributed Channel Access (EDCA, the contention-based channel access function of 802.11e) is that it is unable to guarantee priority access to higher priority traffic in the presence of significant traffic loads from low priority users. This problem is enhanced by the continuously growing number of multimedia applications and the popularity of Wireless Local Area Networks (WLANs). Hence, solutions in scheduling multimedia traffic transmissions need to take into account both the Quality of Service (QoS) requirements and the Quality of Experience (QoE) associated with each application, especially those of urgent traffic, like telemedicine, which carries critical information regarding the patients’ condition. In this work, we propose an easy-to-implement token-based and self policing-based scheduling scheme combined with a mechanism designed to mitigate congestion. Our approach is shown to guarantee priority access to telemedicine traffic, to satisfy its QoS requirements (delay, packet dropping) and to offer high telemedicine video QoE while preventing bursty video nodes from over-using the medium

Token-and Self-Policing-Based scheduling for multimedia traffic transmission over WLANs

The worldwide popularity of wireless local area networks (WLANs) calls for efficient solutions in scheduling multimedia (voice, data, and video) traffic transmissions. Enhanced distributed channel access (EDCA), which is the contention-based channel access function of IEEE 802.11e, is unable to guarantee priority access to higher priority traffic in the presence of significant traffic loads from low-priority users. In this paper, we propose the use of a token- and self-policing-based scheduling scheme, which not only addresses this problem but also prevents bursty video nodes from overusing the medium and tackles the problem of idle time due to large transmission opportunities (TXOPs)

Improved user similarity computation for finding friends in your location

Recommender systems are most often used to predict possible ratings that a user would assign to items, in order to find and propose items of possible interest to each user. In our work, we are interested in a system that will analyze user preferences in order to find and connect people with common interests that happen to be in the same geographical area, i.e., a “friend” recommendation system. We present and propose an algorithm, Egosimilar+, which is shown to achieve superior performance against a number of well-known similarity computation methods from the literature. The algorithm adapts ideas and techniques from the recommender systems literature and the skyline queries literature and combines them with our own ideas on the importance and utilization of item popularity

Call-Admission-Control and Traffic- Policing mechanisms for the transmission of videoconference traffic from MPEG-4 and H.263 video coders in wireless ATM networks

In this paper, we explore, via an extensive simulation study, the performance of a call-admission-control (CAC) and traffic-policing mechanisms proposed for transmitting multiple quality encoded videoconference movies over a wireless asynchronous transfer mode channel of high capacity, depending on the user's needs and requests. Both the CAC algorithm and the traffic-policing mechanism are novel mechanisms proposed for the first time in this paper. For their implementation, we use an estimation of the equivalent bandwidth of the movies, which has been introduced in the past. We focus on both MPEG-4 and H.263 coded movies, as the integration of such video streams has not been studied in the relevant literature. In the case of H.263 movies, our scheme achieves high aggregate channel throughput, while preserving the very strict quality-of-service requirements of the video traffic