10,720 research outputs found
Optimal Detection of Faulty Traffic Sensors Used in Route Planning
In a smart city, real-time traffic sensors may be deployed for various
applications, such as route planning. Unfortunately, sensors are prone to
failures, which result in erroneous traffic data. Erroneous data can adversely
affect applications such as route planning, and can cause increased travel
time. To minimize the impact of sensor failures, we must detect them promptly
and accurately. However, typical detection algorithms may lead to a large
number of false positives (i.e., false alarms) and false negatives (i.e.,
missed detections), which can result in suboptimal route planning. In this
paper, we devise an effective detector for identifying faulty traffic sensors
using a prediction model based on Gaussian Processes. Further, we present an
approach for computing the optimal parameters of the detector which minimize
losses due to false-positive and false-negative errors. We also characterize
critical sensors, whose failure can have high impact on the route planning
application. Finally, we implement our method and evaluate it numerically using
a real-world dataset and the route planning platform OpenTripPlanner.Comment: Proceedings of The 2nd Workshop on Science of Smart City Operations
and Platforms Engineering (SCOPE 2017), Pittsburgh, PA USA, April 2017, 6
page
ANTIDS: Self-Organized Ant-based Clustering Model for Intrusion Detection System
Security of computers and the networks that connect them is increasingly
becoming of great significance. Computer security is defined as the protection
of computing systems against threats to confidentiality, integrity, and
availability. There are two types of intruders: the external intruders who are
unauthorized users of the machines they attack, and internal intruders, who
have permission to access the system with some restrictions. Due to the fact
that it is more and more improbable to a system administrator to recognize and
manually intervene to stop an attack, there is an increasing recognition that
ID systems should have a lot to earn on following its basic principles on the
behavior of complex natural systems, namely in what refers to
self-organization, allowing for a real distributed and collective perception of
this phenomena. With that aim in mind, the present work presents a
self-organized ant colony based intrusion detection system (ANTIDS) to detect
intrusions in a network infrastructure. The performance is compared among
conventional soft computing paradigms like Decision Trees, Support Vector
Machines and Linear Genetic Programming to model fast, online and efficient
intrusion detection systems.Comment: 13 pages, 3 figures, Swarm Intelligence and Patterns (SIP)- special
track at WSTST 2005, Muroran, JAPA
A machine learning approach with verification of predictions and assisted supervision for a rule-based network intrusion detection system
Network security is a branch of network management in which network intrusion detection systems provide attack detection features by monitorization of traffic data. Rule-based misuse detection systems use a set of rules or signatures to detect attacks that exploit a particular vulnerability. These rules have to be handcoded by experts to properly identify vulnerabilities, which results in misuse detection systems having limited extensibility. This paper proposes a machine learning layer on top of a rule-based misuse detection system that provides automatic generation of detection rules, prediction verification and assisted classification of new data. Our system offers an overall good performance, while adding an heuristic and adaptive approach to existing rule-based misuse detection systems
In-depth comparative evaluation of supervised machine learning approaches for detection of cybersecurity threats
This paper describes the process and results of analyzing CICIDS2017, a modern, labeled data set for testing intrusion detection systems. The data set is divided into several days, each pertaining to different attack classes (Dos, DDoS, infiltration, botnet, etc.). A pipeline has been created that includes nine supervised learning algorithms. The goal was binary classification of benign versus attack traffic. Cross-validated parameter optimization, using a voting mechanism that includes five classification metrics, was employed to select optimal parameters. These results were interpreted to discover whether certain parameter choices were dominant for most (or all) of the attack classes. Ultimately, every algorithm was retested with optimal parameters to obtain the final classification scores. During the review of these results, execution time, both on consumerand corporate-grade equipment, was taken into account as an additional requirement. The work detailed in this paper establishes a novel supervised machine learning performance baseline for CICIDS2017
Optimal Information-Theoretic Wireless Location Verification
We develop a new Location Verification System (LVS) focussed on network-based
Intelligent Transport Systems and vehicular ad hoc networks. The algorithm we
develop is based on an information-theoretic framework which uses the received
signal strength (RSS) from a network of base-stations and the claimed position.
Based on this information we derive the optimal decision regarding the
verification of the user's location. Our algorithm is optimal in the sense of
maximizing the mutual information between its input and output data. Our
approach is based on the practical scenario in which a non-colluding malicious
user some distance from a highway optimally boosts his transmit power in an
attempt to fool the LVS that he is on the highway. We develop a practical
threat model for this attack scenario, and investigate in detail the
performance of the LVS in terms of its input/output mutual information. We show
how our LVS decision rule can be implemented straightforwardly with a
performance that delivers near-optimality under realistic threat conditions,
with information-theoretic optimality approached as the malicious user moves
further from the highway. The practical advantages our new
information-theoretic scheme delivers relative to more traditional Bayesian
verification frameworks are discussed.Comment: Corrected typos and introduced new threat model
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