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With the increasing risk of data leakage, information guards have emerged as a novel concept in the field of security which bears similarity to spam filter that examine the content of the exchanged messages. A guard is defined as a high-assurance device used to control the information flow, typically from a domain with a "high" level of confidentiality, such as a corporate or military network,...
After lung cancer, breast cancer is known to be the greatest cause for death among females [20]. The improving effectiveness of machine learning approaches is being given a lot of importance by medical practitioners for breast cancer diagnosis. The paper proposes an effective hybridized classifier for breast cancer diagnosis. The classifier is made by combining an unsupervised artificial neural network...
In this paper, we consider multi-sensor classification when there is a large number of unlabeled samples. The problem is formulated under the multi-view learning framework and a Consensus-based Multi-View Maximum Entropy Discrimination (CMV-MED) algorithm is proposed. By iteratively maximizing the stochastic agreement between multiple classifiers on the unlabeled dataset, the algorithm simultaneously...
With the growth of the Internet and electronic commerce, there is more and more review data on the Internet. Quite a lot of Internet users refer to related comments of a product before they make a decision, which can teach them about the quality and reputation of the product and help them decide whether to buy it. A system that can automatically classify the polarity of a given text would be a great...
Communication between different nations is essential. Languages which are foreign to another impose difficulty in understanding. For this problem to be resolved, options are limited to learning the language, having a dictionary as a guide, or making use of a translator. This paper discusses the development of ASEANMT-Phil, a phrase-based statistical machine translator, to be utilized as a tool beneficial...
Research shows that Twitter is being misused as a platform for online radicalization and contains several hate and extremism promoting users and tweets violating the community guidelines of the website. Manual identification of such tweets is practically impossible due to millions of tweets posted every day and hence solutions to automate the task of tweet classification is required for Twitter moderators...
Phishing is an act of stealing personal and sensitive user information through internet and using it for financial transactions. The goal of phishers is to carry out fraudulent transactions on behalf of the victims by using the information stealed from them. Availing the services of internet has become a dangerous task to the common people with these kinds of attacks. Many methods have been developed...
The exploding volume of network traffic and expanding Quality of Service (QoS) requirements from emerging multimedia and interactive applications in the last decade demand improved internet traffic engineering techniques. In particular, traffic classification and packet marking became essential components for end-to-end QoS assurance of different traffic classes. In this paper we present WekaTIE,...
Along with the information explosion in the Internet era, the traditional classification methods, such as KNN (k-nearest neighbor), Naive Bayes (NB), encounter bottlenecks due to the endless stream of new words. In this paper, through comparing with the Rocchio and Bayesian algorithms, it has been found that centroid-based algorithms are insufficient for text classification. Therefore, a novel feature...
Due to the ubiquitous existence of large-scale data in today's real-world applications including learning on cross media data, we propose a semi-supervised learning method named Multiple Binary Subspace Regression (MBSR) for cross media data classification. In order to mine the common features among the data with multiple modalities, we project the original cross-media data into the same low-rank...
Detecting and identification the network traffic attracts many attentions in recent years. Statistical approach using the machining learning algorithm can classify the network traffic efficiently without detecting the payload of every packet. At the same time, the accuracy depends on the statistical features of the training set. However, the traditional process without pre-treatment of the statistical...
Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks can result in huge loss of data and make resources unavailable for legitimate users. With continuous growth of Internet users and traffic, the importance of Intrusion Detection System (IDS) for detection of DoS/DDoS network attacks has also grown. Different techniques such as data mining and pattern recognition are being used...
The researchers have started looking for Internet traffic recognition techniques that are independent of ‘well known’ TCP or UDP port numbers, or interpreting the contents of packet payloads. Newer approaches classify traffic by recognizing statistical patterns in externally observable attributes of the traffic (such as typical packet lengths and inter-arrival times). The main goal is to cluster or...
Classification of network traffic is extensively required mainly for many network management tasks such as flow prioritization, traffic shaping/policing, and diagnostic monitoring. Many approaches have been evolved for this purpose. The classical approaches such as port number or payload analyis methods has their own limitations. For example, some applications uses dynamic port number and encryption...
The use of online review sites has grown significantly, allowing for communities to share information on products or services.These online review sites are marketed as being independent and trustworthy, but have been criticised for not ensuring the integrity of the reviews.One major concern is that of review fraud; where a person (such as a marketer) is paid to write favourable reviews for one product...
Hierarchical models becomes one of the most widely-adopted and effective solutions in organizing large volume of documents. Although there are general taxonomies on the Web, we observe that in most cases there will be many inconsistencies between general taxonomy and specific resources as the generation of taxonomies is independent of the resources. Besides with the newly available resources into...
Traffic classification plays an important role in many short to medium term network management tasks and in long term network dimensioning/planning. In recent years a number of traffic classifiers have been proposed, in particular classifiers based on machine learning techniques exhibit high levels of accuracy. However, in practice, even if classifiers can be accurately trained at a given time, their...
Many existing machine learning based traffic classifiers require the first five packets in traffic flows to perform traffic classification. In this work, we investigate the flexibility of using arbitrary sets of packets to train traffic classifiers. Such classifiers could be used as auxiliary classifiers that would function in cases where some packets in flows are unavailable, possibly due to packet...
Phishing is turning into a hotbed for vast fraudulency over the internet; therefore it's one of the most challenges toward internet security. Utilizing a centralized list of website is a common solution; as the most of the browsers and commercial anti-phishing products utilize it. Nevertheless, this solution is helpless against zero-day phishing attacks. So, many researches study and suggest methods...
Our research concentrates on anomaly detection techniques, which have both industrial applications such as network monitoring and protection, as well as research applications such as software behavioral analysis or malware classification. During our doctoral research, we worked on anomaly detection from three different perspective, as a complex computer infrastructure has several weak spots that must...
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