38,183 research outputs found

    Classification hardness for supervised learners on 20 years of intrusion detection data

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    This article consolidates analysis of established (NSL-KDD) and new intrusion detection datasets (ISCXIDS2012, CICIDS2017, CICIDS2018) through the use of supervised machine learning (ML) algorithms. The uniformity in analysis procedure opens up the option to compare the obtained results. It also provides a stronger foundation for the conclusions about the efficacy of supervised learners on the main classification task in network security. This research is motivated in part to address the lack of adoption of these modern datasets. Starting with a broad scope that includes classification by algorithms from different families on both established and new datasets has been done to expand the existing foundation and reveal the most opportune avenues for further inquiry. After obtaining baseline results, the classification task was increased in difficulty, by reducing the available data to learn from, both horizontally and vertically. The data reduction has been included as a stress-test to verify if the very high baseline results hold up under increasingly harsh constraints. Ultimately, this work contains the most comprehensive set of results on the topic of intrusion detection through supervised machine learning. Researchers working on algorithmic improvements can compare their results to this collection, knowing that all results reported here were gathered through a uniform framework. This work's main contributions are the outstanding classification results on the current state of the art datasets for intrusion detection and the conclusion that these methods show remarkable resilience in classification performance even when aggressively reducing the amount of data to learn from

    A Comprehensive Survey of Intrusion Detection Systems

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    Alongside with digital signatures and Cryptographic protocols, Intrusion Detection Systems (IDS) are judged to be the final contour of protection to protect a system. But the major difficulty with today’s mainly admired IDSs (Intrusion Detection System) is the invention of massive quantity of false positive (FP) alerts alongside with the true positive (TP) alerts, which is an awkward assignment for the operator to examine to arrange the proper responses. So, there is an immense requirement to discover this area of study and to discover a reasonable solution. A main disadvantage of Intrusion Detection Systems (IDSs), despite of their detection method, is the vast number of alerts they produce on a daily basis that can effortlessly exhaust security supervisors. This constraint has guide researchers in the IDS society to not only extend better detection algorithms and signature tuning methods, but to also focus on determining a variety of relations between individual alerts, formally known as alert correlation. There are a variety of approaches of intrusion detection, such as Pattern Matching, Machine Learning, Data Mining, and Measure Based Methods. This paper aims towards the proper survey of IDS so that researchers can make use of it and find the new techniques towards intrusions. Keywords: Intrusion Detection System, False positive alert, KDD Cup99, Anomaly detection, misuse detection, Machine Learning

    A Real-Time Remote IDS Testbed for Connected Vehicles

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    Connected vehicles are becoming commonplace. A constant connection between vehicles and a central server enables new features and services. This added connectivity raises the likelihood of exposure to attackers and risks unauthorized access. A possible countermeasure to this issue are intrusion detection systems (IDS), which aim at detecting these intrusions during or after their occurrence. The problem with IDS is the large variety of possible approaches with no sensible option for comparing them. Our contribution to this problem comprises the conceptualization and implementation of a testbed for an automotive real-world scenario. That amounts to a server-side IDS detecting intrusions into vehicles remotely. To verify the validity of our approach, we evaluate the testbed from multiple perspectives, including its fitness for purpose and the quality of the data it generates. Our evaluation shows that the testbed makes the effective assessment of various IDS possible. It solves multiple problems of existing approaches, including class imbalance. Additionally, it enables reproducibility and generating data of varying detection difficulties. This allows for comprehensive evaluation of real-time, remote IDS.Comment: Peer-reviewed version accepted for publication in the proceedings of the 34th ACM/SIGAPP Symposium On Applied Computing (SAC'19

    A Study of Automotive Security : CAN Bus Intrusion detection Systems, Attack Surface, and Regulations

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    The innovation in the automotive sector enhanced the technology implemented in vehicles by the manufacturers. Consequently, the overall driving experience improved, thanks to the introduction of better safety, utility, and entertainment systems. Moreover, automobiles began collecting and exchanging data with the external world through different communication protocols. However, these additions have started to attract attention from security experts. More importantly, malevolent attackers have exploited the technologies and their related attack points to carry out malicious activities to cause data security and safety issues. These issues have led to establishing standards and regulations (ISO 21434, UNECE 155, etc.) that redefine vehicle design and development by incorporating security protocols and requirements necessary to create secure automobiles. However, these documents analyze the problem at a high level and do not dwell on practical solutions implementation analysis. This work presents an in-depth study of in-vehicle communication concerns via Controller Area Network (CAN) bus safety problems analysis with different proposed solutions. Specifically, a survey of Intrusion Detection Systems developed in the literature is brought up: simulation of three CAN bus intrusion detection systems against various attacks. The results show effectiveness against disruptive attacks, i.e., with numerous messages sent in a short period of time, but conversely have difficulty detecting more targeted attacks with few transmitted packets. The solutions analysis is an excellent starting point for security engineers to be able to develop Intrusion Detection Systems for the CAN bus capable of detecting attacks that will become increasingly complex and difficult to counter over time

    ATLANTIDES: Automatic Configuration for Alert Verification in Network Intrusion Detection Systems

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    We present an architecture designed for alert verification (i.e., to reduce false positives) in network intrusion-detection systems. Our technique is based on a systematic (and automatic) anomaly-based analysis of the system output, which provides useful context information regarding the network services. The false positives raised by the NIDS analyzing the incoming traffic (which can be either signature- or anomaly-based) are reduced by correlating them with the output anomalies. We designed our architecture for TCP-based network services which have a client/server architecture (such as HTTP). Benchmarks show a substantial reduction of false positives between 50% and 100%
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