NeuDetect: A neural network data mining system for wireless network intrusion detection

This thesis proposes an Intrusion Detection System, NeuDetect, which applies Neural Network technique to wireless network packets captured through hardware sensors for purposes of real time detection of anomalous packets. To address the problem of high false alarm rate confronted by the current wireless intrusion detection systems, this thesis presents a method of applying the artificial neural networks technique to the wireless network intrusion detection system. The proposed system solution approach is to find normal and anomalous patterns on preprocessed wireless packet records by comparing them with training data using Back-propagation algorithm. An anomaly score is assigned to each packet by calculating the difference between the output error and threshold. If the anomaly score is positive then the wireless packet is flagged as anomalous and is negative then the packet is flagged as normal. If the anomaly score is zero or close to zero it will be flagged as an unknown attack and will be sent back to training process for re-evaluation

Using metrics from multiple layers to detect attacks in wireless networks

The IEEE 802.11 networks are vulnerable to numerous wireless-specific attacks. Attackers can implement MAC address spoofing techniques to launch these attacks, while masquerading themselves behind a false MAC address. The implementation of Intrusion Detection Systems has become fundamental in the development of security infrastructures for wireless networks. This thesis proposes the designing a novel security system that makes use of metrics from multiple layers of observation to produce a collective decision on whether an attack is taking place. The Dempster-Shafer Theory of Evidence is the data fusion technique used to combine the evidences from the different layers. A novel, unsupervised and self- adaptive Basic Probability Assignment (BPA) approach able to automatically adapt its beliefs assignment to the current characteristics of the wireless network is proposed. This BPA approach is composed of three different and independent statistical techniques, which are capable to identify the presence of attacks in real time. Despite the lightweight processing requirements, the proposed security system produces outstanding detection results, generating high intrusion detection accuracy and very low number of false alarms. A thorough description of the generated results, for all the considered datasets is presented in this thesis. The effectiveness of the proposed system is evaluated using different types of injection attacks. Regarding one of these attacks, to the best of the author knowledge, the security system presented in this thesis is the first one able to efficiently identify the Airpwn attack

Teaching Your Wireless Card New Tricks: Smartphone Performance and Security Enhancements Through Wi-Fi Firmware Modifications

Smartphones come with a variety of sensors and communication interfaces, which make them perfect candidates for mobile communication testbeds. Nevertheless, proprietary firmwares hinder us from accessing the full capabilities of the underlying hardware platform which impedes innovation. Focusing on FullMAC Wi-Fi chips, we present Nexmon, a C-based firmware modification framework. It gives access to raw Wi-Fi frames and advanced capabilities that we found by reverse engineering chips and their firmware. As firmware modifications pose security risks, we discuss how to secure firmware handling without impeding experimentation on Wi-Fi chips. To present and evaluate our findings in the field, we developed the following applications. We start by presenting a ping-offloading application that handles ping requests in the firmware instead of the operating system. It significantly reduces energy consumption and processing delays. Then, we present a software-defined wireless networking application that enhances scalable video streaming by setting flow-based requirements on physical-layer parameters. As security application, we present a reactive Wi-Fi jammer that analyses incoming frames during reception and transmits arbitrary jamming waveforms by operating Wi-Fi chips as software-defined radios (SDRs). We further introduce an acknowledging jammer to ensure the flow of non-targeted frames and an adaptive power-control jammer to adjust transmission powers based on measured jamming successes. Additionally, we discovered how to extract channel state information (CSI) on a per-frame basis. Using both SDR and CSI-extraction capabilities, we present a physical-layer covert channel. It hides covert symbols in phase changes of selected OFDM subcarriers. Those manipulations can be extracted from CSI measurements at a receiver. To ease the analysis of firmware binaries, we created a debugging application that supports single stepping and runs as firmware patch on the Wi-Fi chip. We published the source code of our framework and our applications to ensure reproducibility of our results and to enable other researchers to extend our work. Our framework and the applications emphasize the need for freely modifiable firmware and detailed hardware documentation to create novel and exciting applications on commercial off-the-shelf devices

Journal of Telecommunications and Information Technology, 2008, nr 2

kwartalni

Towards understanding and mitigating attacks leveraging zero-day exploits

Zero-day vulnerabilities are unknown and therefore not addressed with the result that they can be exploited by attackers to gain unauthorised system access. In order to understand and mitigate against attacks leveraging zero-days or unknown techniques, it is necessary to study the vulnerabilities, exploits and attacks that make use of them. In recent years there have been a number of leaks publishing such attacks using various methods to exploit vulnerabilities. This research seeks to understand what types of vulnerabilities exist, why and how these are exploited, and how to defend against such attacks by either mitigating the vulnerabilities or the method / process of exploiting them. By moving beyond merely remedying the vulnerabilities to defences that are able to prevent or detect the actions taken by attackers, the security of the information system will be better positioned to deal with future unknown threats. An interesting finding is how attackers exploit moving beyond the observable bounds to circumvent security defences, for example, compromising syslog servers, or going down to lower system rings to gain access. However, defenders can counter this by employing defences that are external to the system preventing attackers from disabling them or removing collected evidence after gaining system access. Attackers are able to defeat air-gaps via the leakage of electromagnetic radiation as well as misdirect attribution by planting false artefacts for forensic analysis and attacking from third party information systems. They analyse the methods of other attackers to learn new techniques. An example of this is the Umbrage project whereby malware is analysed to decide whether it should be implemented as a proof of concept. Another important finding is that attackers respect defence mechanisms such as: remote syslog (e.g. firewall), core dump files, database auditing, and Tripwire (e.g. SlyHeretic). These defences all have the potential to result in the attacker being discovered. Attackers must either negate the defence mechanism or find unprotected targets. Defenders can use technologies such as encryption to defend against interception and man-in-the-middle attacks. They can also employ honeytokens and honeypots to alarm misdirect, slow down and learn from attackers. By employing various tactics defenders are able to increase their chance of detecting and time to react to attacks, even those exploiting hitherto unknown vulnerabilities. To summarize the information presented in this thesis and to show the practical importance thereof, an examination is presented of the NSA's network intrusion of the SWIFT organisation. It shows that the firewalls were exploited with remote code execution zerodays. This attack has a striking parallel in the approach used in the recent VPNFilter malware. If nothing else, the leaks provide information to other actors on how to attack and what to avoid. However, by studying state actors, we can gain insight into what other actors with fewer resources can do in the future

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

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