Summary
Intrusion detection systems (IDS) are systems aimed at analyzing and detecting security problems. The IDS may be structured into misuse and anomaly detection. The former are often signature/rule IDS that detect malicious software by inspecting the content of packets or files looking for a “signature” labeling malware. They are often very efficient, but their drawback stands in the weakness of the information to check (eg, the signature), which may be quickly dated, and in the computation time because each packet or file needs to be inspected. The IDS based on anomaly detection and, in particular, on statistical analysis have been originated to bypass the mentioned problems. Instead of inspecting packets, each traffic flow is observed so getting a statistical characterization, which represents the fingerprint of the flow. This paper introduces a statistical analysis based intrusion detection system, which, after extracting the statistical fingerprint, uses machine learning classifiers to decide whether a flow is affected by malware or not. A large set of tests is presented. The obtained results allow selecting the best classifiers and show the performance of a decision maker that exploits the decisions of a bank of classifiers acting in parallel.
The paper describes an original architecture aimed at tracking assets within construction sites or similar contexts. The main components are: i) RFID tags, ii) Bluetooth Low Energy (BLE) tags and iii) smartphones. The core functions of the architecture are performed by two Android applications: the Asset Proximity Locator (APL) and the Wandering Objects Location Finder (WOLF). The key feature of the asset tracking function is the ability to maximize smartphone battery lifetime by switching on and off the GPS unit, thus guaranteeing that the smartphone can be used for an entire working shift. A detailed energy consumption analysis is carried out for each component of the architecture. The results achieved by simulations, concerning BLE tag detection probability, are presented and discussed
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