183 research outputs found

    Impact of Metric Selection on Wireless DeAuthentication DoS Attack Performance

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    DeAuthentication Denial of Service attacks in Public Access WiFi operate by exploiting the lack of authentication of management frames in the 802.11 protocol. Detection of these attacks rely almost exclusively on the selection of appropriate thresholds. In this work the authors demonstrate that there are additional, previously unconsidered, metrics which also influence DoS detection performance. A method of systematically tuning these metrics to optimal values is proposed which ensures that parameter choices are repeatable and verifiable

    IEEE 802.11 i Security and Vulnerabilities

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    Despite using a variety of comprehensive preventive security measures, the Robust Secure Networks (RSNs) remain vulnerable to a number of attacks. Failure of preventive measures to address all RSN vulnerabilities dictates the need for enhancing the performance of Wireless Intrusion Detection Systems (WIDSs) to detect all attacks on RSNs with less false positive and false negative rates

    Security and Privacy Issues in Wireless Mesh Networks: A Survey

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    This book chapter identifies various security threats in wireless mesh network (WMN). Keeping in mind the critical requirement of security and user privacy in WMNs, this chapter provides a comprehensive overview of various possible attacks on different layers of the communication protocol stack for WMNs and their corresponding defense mechanisms. First, it identifies the security vulnerabilities in the physical, link, network, transport, application layers. Furthermore, various possible attacks on the key management protocols, user authentication and access control protocols, and user privacy preservation protocols are presented. After enumerating various possible attacks, the chapter provides a detailed discussion on various existing security mechanisms and protocols to defend against and wherever possible prevent the possible attacks. Comparative analyses are also presented on the security schemes with regards to the cryptographic schemes used, key management strategies deployed, use of any trusted third party, computation and communication overhead involved etc. The chapter then presents a brief discussion on various trust management approaches for WMNs since trust and reputation-based schemes are increasingly becoming popular for enforcing security in wireless networks. A number of open problems in security and privacy issues for WMNs are subsequently discussed before the chapter is finally concluded.Comment: 62 pages, 12 figures, 6 tables. This chapter is an extension of the author's previous submission in arXiv submission: arXiv:1102.1226. There are some text overlaps with the previous submissio

    On fast and accurate detection of unauthorized wireless access points using clock skews

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    Journal ArticleWe explore the use of clock skew of a wireless local area network access point (AP) as its fingerprint to detect unauthorized APs quickly and accurately. The main goal behind using clock skews is to overcome one of the major limitations of existing solutions-the inability to effectively detect Medium Access Control (MAC) address spoofing. We calculate the clock skew of an AP from the IEEE 802.11 Time Synchronization Function (TSF) time stamps sent out in the beacon/probe response frames. We use two different methods for this purpose-one based on linear programming and the other based on least-square fit. We supplement these methods with a heuristic for differentiating original packets from those sent by the fake APs. We collect TSF time stamp data from several APs in three different residential settings. Using our measurement data as well as data obtained from a large conference setting, we find that clock skews remain consistent over time for the same AP but vary significantly across APs. Furthermore, we improve the resolution of received time stamp of the frames and show that with this enhancement, our methodology can find clock skews very quickly, using 50-100 packets in most of the cases. We also discuss and quantify the impact of various external factors including temperature variation, virtualization, clock source selection, and NTP synchronization on clock skews. Our results indicate that the use of clock skews appears to be an efficient and robust method for detecting fake APs in wireless local area networks

    Statistical anomaly denial of service and reconnaissance intrusion detection

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    This dissertation presents the architecture, methods and results of the Hierarchical Intrusion Detection Engine (HIDE) and the Reconnaissance Intrusion Detection System (RIDS); the former is denial-of-service (DoS) attack detector while the latter is a scan and probe (P&S) reconnaissance detector; both are statistical anomaly systems. The HIDE is a packet-oriented, observation-window using, hierarchical, multi-tier, anomaly based network intrusion detection system, which monitors several network traffic parameters simultaneously, constructs a 64-bin probability density function (PDF) for each, statistically compares it to a reference PDF of normal behavior using a similarity metric, then combines the results into an anomaly status vector that is classified by a neural network classifier. Three different data sets have been utilized to test the performance of HIDE; they are OPNET simulation data, DARPA\u2798 intrusion detection evaluation data and the CONEX TESTBED attack data. The results showed that HIDE can reliably detect DoS attacks with high accuracy and very low false alarm rates on all data sets. In particular, the investigation using the DARPA\u2798 data set yielded an overall total misclassification rate of 0.13%, false negative rate of 1.42%, and false positive rate of 0.090%; the latter implies a rate of only about 2.6 false alarms per day. The RIDS is a session oriented, statistical tool, that relies on training to model the parameters of its algorithms, capable of detecting even distributed stealthy reconnaissance attacks. It consists of two main functional modules or stages: the Reconnaissance Activity Profiler (RAP) and the Reconnaissance Alert Correlater (RAC). The RAP is a session-oriented module capable of detecting stealthy scanning and probing attacks, while the RAG is an alert-correlation module that fuses the RAP alerts into attack scenarios and discovers the distributed stealthy attack scenarios. RIDS has been evaluated against two data sets: (a) the DARPA\u2798 data, and (b) 3 weeks of experimental data generated using the CONEX TESTBED network. The RIDS has demonstrably achieved remarkable success; the false positive, false negative and misclassification rates found are low, less than 0.1%, for most reconnaissance attacks; they rise to about 6% for distributed highly stealthy attacks; the latter is a most challenging type of attack, which has been difficult to detect effectively until now

    Intrusion detection and monitoring for wireless networks.

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    Wireless device identification from a phase noise prospective

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    As wireless devices become increasingly pervasive and essential, they are becoming both a target for attacks and the very weapon with which such an attack can be carried out. Wireless networks have to face new kinds of intrusion that had not been considered previously because they are linked to the open nature of wireless networks. In particular, device identity management and intrusion detection are two of the most significant challenges in any network security solution but they are paramount for any wireless local area networks (WLANs) because of the inherent non-exclusivity of the transmission medium. The physical layer of 802.11-based wireless communication does not offer security guarantee because any electromagnetic signal transmitted can be monitored, captured, and analyzed by any sufficiently motivated and equipped adversary within the 802.11 device's transmission range. What is required is a form of identification that is nonmalleable (cannot be spoofed easily). For this reason we have decided to focus on physical characteristics of the network interface card (NIC) to distinguish between different wireless users because it can provide an additional layer of security. The unique properties of the wireless medium are extremely useful to get an additional set of information that can be used to extend and enhance traditional security mechanisms. This approach is commonly referred to as radio frequency fingerprinting (RFF), i.e., determining specific characteristics (fingerprint) of a network device component. More precisely, our main goal is to prove the feasibility of exploiting phase noise in oscillators for fingerprinting design and overcome existing limitations of conventional approaches. The intuition behind our design is that the autonomous nature of oscillators among noisy physical systems makes them unique in their response to perturbations and none of the previous work has ever tried to take advantage of thi

    Cyber-Physical Threat Intelligence for Critical Infrastructures Security

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    Modern critical infrastructures comprise of many interconnected cyber and physical assets, and as such are large scale cyber-physical systems. Hence, the conventional approach of securing these infrastructures by addressing cyber security and physical security separately is no longer effective. Rather more integrated approaches that address the security of cyber and physical assets at the same time are required. This book presents integrated (i.e. cyber and physical) security approaches and technologies for the critical infrastructures that underpin our societies. Specifically, it introduces advanced techniques for threat detection, risk assessment and security information sharing, based on leading edge technologies like machine learning, security knowledge modelling, IoT security and distributed ledger infrastructures. Likewise, it presets how established security technologies like Security Information and Event Management (SIEM), pen-testing, vulnerability assessment and security data analytics can be used in the context of integrated Critical Infrastructure Protection. The novel methods and techniques of the book are exemplified in case studies involving critical infrastructures in four industrial sectors, namely finance, healthcare, energy and communications. The peculiarities of critical infrastructure protection in each one of these sectors is discussed and addressed based on sector-specific solutions. The advent of the fourth industrial revolution (Industry 4.0) is expected to increase the cyber-physical nature of critical infrastructures as well as their interconnection in the scope of sectorial and cross-sector value chains. Therefore, the demand for solutions that foster the interplay between cyber and physical security, and enable Cyber-Physical Threat Intelligence is likely to explode. In this book, we have shed light on the structure of such integrated security systems, as well as on the technologies that will underpin their operation. We hope that Security and Critical Infrastructure Protection stakeholders will find the book useful when planning their future security strategies

    Applying Machine Learning to Advance Cyber Security: Network Based Intrusion Detection Systems

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    Many new devices, such as phones and tablets as well as traditional computer systems, rely on wireless connections to the Internet and are susceptible to attacks. Two important types of attacks are the use of malware and exploiting Internet protocol vulnerabilities in devices and network systems. These attacks form a threat on many levels and therefore any approach to dealing with these nefarious attacks will take several methods to counter. In this research, we utilize machine learning to detect and classify malware, visualize, detect and classify worms, as well as detect deauthentication attacks, a form of Denial of Service (DoS). This work also includes two prevention mechanisms for DoS attacks, namely a one- time password (OTP) and through the use of machine learning. Furthermore, we focus on an exploit of the widely used IEEE 802.11 protocol for wireless local area networks (WLANs). The work proposed here presents a threefold approach for intrusion detection to remedy the effects of malware and an Internet protocol exploit employing machine learning as a primary tool. We conclude with a comparison of dimensionality reduction methods to a deep learning classifier to demonstrate the effectiveness of these methods without compromising the accuracy of classification
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