Information Fusion for Anomaly Detection with the Dendritic Cell Algorithm

Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system, providing the initial detection of pathogenic invaders. Research into this family of cells has revealed that they perform information fusion which directs immune responses. We have derived a Dendritic Cell Algorithm based on the functionality of these cells, by modelling the biological signals and differentiation pathways to build a control mechanism for an artificial immune system. We present algorithmic details in addition to experimental results, when the algorithm was applied to anomaly detection for the detection of port scans. The results show the Dendritic Cell Algorithm is sucessful at detecting port scans.Comment: 21 pages, 17 figures, Information Fusio

Geometry-based Detection of Flash Worms

While it takes traditional internet worms hours to infect all the vulnerable hosts on the Internet, a flash worm takes seconds. Because of the rapid rate with which flash worms spread, the existing worm defense mechanisms cannot respond fast enough to detect and stop the flash worm infections. In this project, we propose a geometric-based detection mechanism that can detect the spread of flash worms in a short period of time. We tested the mechanism on various simulated flash worm traffics consisting of more than 10,000 nodes. In addition to testing on flash worm traffics, we also tested the mechanism on non-flash worm traffics to see if our detection mechanism produces false alarms. In order to efficiently analyze bulks of various network traffics, we implemented an application that can be used to convert the network traffic data into graphical notations. Using the application, the analysis can be done graphically as it displays the large amount of network relationships as tree structures

Malware Detection Module using Machine Learning Algorithms to Assist in Centralized Security in Enterprise Networks

Malicious software is abundant in a world of innumerable computer users, who are constantly faced with these threats from various sources like the internet, local networks and portable drives. Malware is potentially low to high risk and can cause systems to function incorrectly, steal data and even crash. Malware may be executable or system library files in the form of viruses, worms, Trojans, all aimed at breaching the security of the system and compromising user privacy. Typically, anti-virus software is based on a signature definition system which keeps updating from the internet and thus keeping track of known viruses. While this may be sufficient for home-users, a security risk from a new virus could threaten an entire enterprise network. This paper proposes a new and more sophisticated antivirus engine that can not only scan files, but also build knowledge and detect files as potential viruses. This is done by extracting system API calls made by various normal and harmful executable, and using machine learning algorithms to classify and hence, rank files on a scale of security risk. While such a system is processor heavy, it is very effective when used centrally to protect an enterprise network which maybe more prone to such threats.Comment: 6 page

Shadow Honeypots

We present Shadow Honeypots, a novel hybrid architecture that combines the best features of honeypots and anomaly detection. At a high level, we use a variety of anomaly detectors to monitor all traffic to a protected network or service. Traffic that is considered anomalous is processed by a "shadow honeypot" to determine the accuracy of the anomaly prediction. The shadow is an instance of the protected software that shares all internal state with a regular ("production") instance of the application, and is instrumented to detect potential attacks. Attacks against the shadow are caught, and any incurred state changes are discarded. Legitimate traffic that was misclassified will be validated by the shadow and will be handled correctly by the system transparently to the end user. The outcome of processing a request by the shadow is used to filter future attack instances and could be used to update the anomaly detector. Our architecture allows system designers to fine-tune systems for performance, since false positives will be filtered by the shadow. We demonstrate the feasibility of our approach in a proof-of-concept implementation of the Shadow Honeypot architecture for the Apache web server and the Mozilla Firefox browser. We show that despite a considerable overhead in the instrumentation of the shadow honeypot (up to 20% for Apache), the overall impact on the system is diminished by the ability to minimize the rate of false-positives

Spectral Graph-based Cyber Worm Detection Using Phantom Components and Strong Node Concept

Innovative solutions need to be developed to defend against the continued threat of computer worms. We propose the spectral graph theory worm detection model that utilizes traffic dispersion graphs, the strong node concept, and phantom components to create detection thresholds in the eigenspectrum of the dual basis. This detection method is employed in our proposed model to quickly and accurately detect worm attacks with different attack characteristics. It also intrinsically identifies infected nodes, potential victims, and estimates the worm scan rate. We test our model against the worm-free NPS2013 dataset, a modeled Blaster worm, and the WannaCry CTU-Malware-Capture-Botnet-284-1 and CTU-Malware-Capture-Botnet-285-1 datasets. Our results show that the spectral graph theory worm detection model has better performance rates compared to other models reviewed in literature

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

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