ntrusion detection has now been widely accepted as an essential component in a decent security system. This is due to the fact that the task of preventing all attacks is impossible. Intrusion detection can detect malicious attacks that have penetrated preventative mechanisms such as firewalls, which can help provide damage assessment, response, deterrence, and prosecution support.Denning's pioneering work [1] has established the most fundamental principle that the majority of intrusion detection systems (IDSs) have followed. The principle is a hypothesis that security violations can be detected by monitoring a system's audit records for abnormal patterns of system usage. It is suggested that profiles are used to represent the behavior of subjects using statistical measures. IDSs can have several different classifications. An IDS can be classified as an HIDS (host-based IDS) and NIDS (network-based IDS) in terms of the target the IDS protects. Also, an IDS can be classified into misuse intrusion detection and anomaly intrusion detection according to whether the features of an intrusion are known or unknown in advance. The misuse IDS retrieves attacks' signatures and establishes a database for the collection. During the detection process, the IDS will retrieve a subject signature and search a match against the established signature database.An intrusion alert is triggered once a match is found. Such a mechanism is very effective in detecting a priori known attacks, but performs unsatisfactorily in detecting unknown attacks. Anomaly IDSs are promising in detecting unknown attacks. Based on Denning's principle, an anomaly IDS first builds a system's normal behavior profile and then compares operational system behavior against the nominal profile. If a significant deviation is found, an intrusion alert is triggered.While Denning's intrusion detection model is a host-based IDS, extensive research activities have been shifted to network-based IDSs. There are several factors behind this, summarized as follows: • Networking factor: With the rapid proliferation of Internet technology, overwhelming computing applications are network based. Many security problems are introduced from this environment such as denial of service (DoS) attacks and other security loopholes related to networking protocols.• Real-time and computing resource restraints: Ideally, intrusion can be detected as soon as it happens in order to minimize the potential damage. However, audit data collection and processing for detecting intrusion involve large amounts of computing resources. Therefore, a dedicated hardware and software IDS component is required to perform the task efficiently. Normally, a network-based IDS deduces intrusion from analyzing network packets. It is very effective in detecting DoS attempts originating outside the network. The majority AbstractExtensive research activities have been observed on network-based intrusion detection systems (IDSs). However, there are always some attacks that penetrate trafficprofiling-based network IDS...

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We present a crowdsourcing system for large-scale production of accurate wrappers to extract data from data-intensive websites. Our approach is based on supervised wrapper inference algorithms which demand the burden of generating training data to workers recruited on a crowdsourcing platform. Workers are paid for answering simple queries carefully chosen by the system. We present two algorithms: a single worker algorithm (ALFη) and a multiple workers algorithm (alfred). Both the algorithms deal with the inherent uncertainty of the workers’ responses and use an active learning approach to select the most informative queries. alfred estimates the workers’ error rate to decide at runtime how many workers should be recruited to achieve a quality target. The system has been fully implemented and tested: the experimental evaluation conducted with both synthetic workers and real workers recruited on a crowdsourcing platform show that our approach is able to produce accurate wrappers at a low cost, even in presence of workers with a significant error rate

Abstract. The ability to predict future movements for moving objects enables better decisions in terms of time, cost, and impact on the environment. Unfortunately, future location prediction is a challenging task. Existing works exploit techniques to predict a trip destination, but they are effective only when location data are precise (e.g., GPS data) and movements are observed over long periods of time (e.g., weeks).We introduce a data mining approach based on a Hidden Markov Model (HMM) that overcomes these limits and improves existing results in terms of precision of the prediction, for both the route (i.e., trajectory) and the final destination. The model is resistant to uncertain location data, as it works with data collected by using cell-towers to localize the users instead of GPS devices, and reaches good prediction results in shorter times (days instead of weeks in a representative real-world application). Finally, we introduce an enhanced version of the model that is orders of magnitude faster than the standard HMM implementation.

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