Resilience Strategies for Network Challenge Detection, Identification and Remediation

The enormous growth of the Internet and its use in everyday life make it an attractive target for malicious users. As the network becomes more complex and sophisticated it becomes more vulnerable to attack. There is a pressing need for the future internet to be resilient, manageable and secure. Our research is on distributed challenge detection and is part of the EU Resumenet Project (Resilience and Survivability for Future Networking: Framework, Mechanisms and Experimental Evaluation). It aims to make networks more resilient to a wide range of challenges including malicious attacks, misconfiguration, faults, and operational overloads. Resilience means the ability of the network to provide an acceptable level of service in the face of significant challenges; it is a superset of commonly used definitions for survivability, dependability, and fault tolerance. Our proposed resilience strategy could detect a challenge situation by identifying an occurrence and impact in real time, then initiating appropriate remedial action. Action is autonomously taken to continue operations as much as possible and to mitigate the damage, and allowing an acceptable level of service to be maintained. The contribution of our work is the ability to mitigate a challenge as early as possible and rapidly detect its root cause. Also our proposed multi-stage policy based challenge detection system identifies both the existing and unforeseen challenges. This has been studied and demonstrated with an unknown worm attack. Our multi stage approach reduces the computation complexity compared to the traditional single stage, where one particular managed object is responsible for all the functions. The approach we propose in this thesis has the flexibility, scalability, adaptability, reproducibility and extensibility needed to assist in the identification and remediation of many future network challenges

Doctor of Philosophy

dissertationWe develop a novel framework for friend-to-friend (f2f) distributed services (F3DS) by which applications can easily offer peer-to-peer (p2p) services among social peers with resource sharing governed by approximated levels of social altruism. Our frame- work differs significantly from typical p2p collaboration in that it provides a founda- tion for distributed applications to cooperate based on pre-existing trust and altruism among social peers. With the goal of facilitating the approximation of relative levels of altruism among social peers within F3DS, we introduce a new metric: SocialDistance. SocialDistance is a synthetic metric that combines direct levels of altruism between peers with an altruism decay for each hop to approximate indirect levels of altruism. The resulting multihop altruism levels are used by F3DS applications to proportion and prioritize the sharing of resources with other social peers. We use SocialDistance to implement a novel flash file/patch distribution method, SocialSwarm. SocialSwarm uses the SocialDistance metric as part of its resource allocation to overcome the neces- sity of (and inefficiency created by) resource bartering among friends participating in a BitTorrent swarm. We find that SocialSwarm achieves an average file download time reduction of 25% to 35% in comparison with standard BitTorrent under a variety of configurations and conditions, including file sizes, maximum SocialDistance, as well as leech and seed counts. The most socially connected peers yield up to a 47% decrease in download completion time in comparison with average nonsocial BitTorrent swarms. We also use the F3DS framework to implement novel malware detection application- F3DS Antivirus (F3AV)-and evaluate it on the Amazon cloud. We show that with f2f sharing of resources, F3AV achieves a 65% increase in the detection rate of 0- to 1-day-old malware among social peers as compared to the average of individual scanners. Furthermore, we show that F3AV provides the greatest diversity of mal- ware scanners (and thus malware protection) to social hubs-those nodes that are positioned to provide strategic defense against socially aware malware

A Survey on Security for Mobile Devices

Nowadays, mobile devices are an important part of our everyday lives since they enable us to access a large variety of ubiquitous services. In recent years, the availability of these ubiquitous and mobile services has signicantly increased due to the dierent form of connectivity provided by mobile devices, such as GSM, GPRS, Bluetooth and Wi-Fi. In the same trend, the number and typologies of vulnerabilities exploiting these services and communication channels have increased as well. Therefore, smartphones may now represent an ideal target for malware writers. As the number of vulnerabilities and, hence, of attacks increase, there has been a corresponding rise of security solutions proposed by researchers. Due to the fact that this research eld is immature and still unexplored in depth, with this paper we aim to provide a structured and comprehensive overview of the research on security solutions for mobile devices. This paper surveys the state of the art on threats, vulnerabilities and security solutions over the period 2004-2011. We focus on high-level attacks, such those to user applications, through SMS/MMS, denial-of-service, overcharging and privacy. We group existing approaches aimed at protecting mobile devices against these classes of attacks into dierent categories, based upon the detection principles, architectures, collected data and operating systems, especially focusing on IDS-based models and tools. With this categorization we aim to provide an easy and concise view of the underlying model adopted by each approach

Gotham Testbed: a Reproducible IoT Testbed for Security Experiments and Dataset Generation

The scarcity of available Internet of Things (IoT) datasets remains a limiting factor in developing machine learning based security systems. Static datasets get outdated due to evolving IoT threat landscape. Meanwhile, the testbeds used to generate them are rarely published. This paper presents the Gotham testbed, a reproducible and flexible network security testbed, implemented as a middleware over the GNS3 emulator, that is extendable to accommodate new emulated devices, services or attackers. The testbed is used to build an IoT scenario composed of 100 emulated devices communicating via MQTT, CoAP and RTSP protocols in a topology composed of 30 switches and 10 routers. The scenario presents three threat actors, including the entire Mirai botnet lifecycle and additional red-teaming tools performing DoS, scanning and various attacks targeting the MQTT and CoAP protocols. The generated network traffic and application logs can be used to capture datasets containing legitimate and attacking traces. We hope that researchers can leverage the testbed and adapt it to include other types of devices and state-of-the-art attacks to generate new datasets that reflect the current threat landscape and IoT protocols. The source code to reproduce the scenario is publicly accessible

Algorizmi: A Configurable Virtual Testbed to Generate Datasets for Offline Evaluation of Intrusion Detection Systems

Intrusion detection systems (IDSes) are an important security measure that network administrators adopt to defend computer networks against malicious attacks and intrusions. The field of IDS research includes many challenges. However, one open problem remains orthogonal to the others: IDS evaluation. In other words, researchers have not yet succeeded to agree on a general systematic methodology and/or a set of metrics to fairly evaluate different IDS algorithms. This leads to another problem: the lack of an appropriate IDS evaluation dataset that satisfies the common research needs. One major contribution in this area is the DARPA dataset offered by the Massachusetts Institute of Technology Lincoln Lab (MIT/LL), which has been extensively used to evaluate a number of IDS algorithms proposed in the literature. Despite this, the DARPA dataset received a lot of criticism concerning the way it was designed, especially concerning its obsoleteness and inability to incorporate new sorts of network attacks. In this thesis, we survey previous research projects that attempted to provide a system for IDS offline evaluation. From the survey, we identify a set of design requirements for such a system based on the research community needs. We, then, propose Algorizmi as an open-source configurable virtual testbed for generating datasets for offline IDS evaluation. We provide an architectural overview of Algorizmi and its software and hardware components. Algorizmi provides its users with tools that allow them to create their own experimental testbed using the concepts of virtualization and cloud computing. Algorizmi users can configure the virtual machine instances running in their experiments, select what background traffic those instances will generate and what attacks will be launched against them. At any point in time, an Algorizmi user can generate a dataset (network traffic trace) for any of her experiments so that she can use this dataset afterwards to evaluate an IDS the same way the DARPA dataset is used. Our analysis shows that Algorizmi satisfies more requirements than previous research projects that target the same research problem of generating datasets for IDS offline evaluation. Finally, we prove the utility of Algorizmi by building a sample network of machines, generate both background and attack traffic within that network. We then download a snapshot of the dataset for that experiment and run it against Snort IDS. Snort successfully detected the attacks we launched against the sample network. Additionally, we evaluate the performance of Algorizmi while processing some of the common usages of a typical user based on 5 metrics: CPU time, CPU usage, memory usage, network traffic sent/received and the execution time

Intrusion Detection for Cyber-Physical Attacks in Cyber-Manufacturing System

In the vision of Cyber-Manufacturing System (CMS) , the physical components such as products, machines, and tools are connected, identifiable and can communicate via the industrial network and the Internet. This integration of connectivity enables manufacturing systems access to computational resources, such as cloud computing, digital twin, and blockchain. The connected manufacturing systems are expected to be more efficient, sustainable and cost-effective. However, the extensive connectivity also increases the vulnerability of physical components. The attack surface of a connected manufacturing environment is greatly enlarged. Machines, products and tools could be targeted by cyber-physical attacks via the network. Among many emerging security concerns, this research focuses on the intrusion detection of cyber-physical attacks. The Intrusion Detection System (IDS) is used to monitor cyber-attacks in the computer security domain. For cyber-physical attacks, however, there is limited work. Currently, the IDS cannot effectively address cyber-physical attacks in manufacturing system: (i) the IDS takes time to reveal true alarms, sometimes over months; (ii) manufacturing production life-cycle is shorter than the detection period, which can cause physical consequences such as defective products and equipment damage; (iii) the increasing complexity of network will also make the detection period even longer. This gap leaves the cyber-physical attacks in manufacturing to cause issues like over-wearing, breakage, defects or any other changes that the original design didn’t intend. A review on the history of cyber-physical attacks, and available detection methods are presented. The detection methods are reviewed in terms of intrusion detection algorithms, and alert correlation methods. The attacks are further broken down into a taxonomy covering four dimensions with over thirty attack scenarios to comprehensively study and simulate cyber-physical attacks. A new intrusion detection and correlation method was proposed to address the cyber-physical attacks in CMS. The detection method incorporates IDS software in cyber domain and machine learning analysis in physical domain. The correlation relies on a new similarity-based cyber-physical alert correlation method. Four experimental case studies were used to validate the proposed method. Each case study focused on different aspects of correlation method performance. The experiments were conducted on a security-oriented manufacturing testbed established for this research at Syracuse University. The results showed the proposed intrusion detection and alert correlation method can effectively disclose unknown attack, known attack and attack interference that causes false alarms. In case study one, the alarm reduction rate reached 99.1%, with improvement of detection accuracy from 49.6% to 100%. The case studies also proved the proposed method can mitigate false alarms, detect attacks on multiple machines, and attacks from the supply chain. This work contributes to the security domain in cyber-physical manufacturing systems, with the focus on intrusion detection. The dataset collected during the experiments has been shared with the research community. The alert correlation methodology also contributes to cyber-physical systems, such as smart grid and connected vehicles, which requires enhanced security protection in today’s connected world

Defending Against IoT-Enabled DDoS Attacks at Critical Vantage Points on the Internet

The number of Internet of Things (IoT) devices continues to grow every year. Unfortunately, with the rise of IoT devices, the Internet is also witnessing a rise in the number and scale of IoT-enabled distributed denial-of-service (DDoS) attacks. However, there is a lack of network-based solutions targeted directly for IoT networks to address the problem of IoT-enabled DDoS. Unlike most security approaches for IoT which focus on hardening device security through hardware and/or software modification, which in many cases is infeasible, we introduce network-based approaches for addressing IoT-enabled DDoS attacks. We argue that in order to effectively defend the Internet against IoT-enabled DDoS attacks, it is necessary to consider network-wide defense at critical vantage points on the Internet. This dissertation is focused on three inherently connected and complimentary components: (1) preventing IoT devices from being turned into DDoS bots by inspecting traffic towards IoT networks at an upstream ISP/IXP, (2) detecting DDoS traffic leaving an IoT network by inspecting traffic at its gateway, and (3) mitigating attacks as close to the devices in an IoT network originating DDoS traffic. To this end, we present three security solutions to address the three aforementioned components to defend against IoT-enabled DDoS attacks

Telescience Testbed Pilot Program

The Telescience Testbed Pilot Program is developing initial recommendations for requirements and design approaches for the information systems of the Space Station era. During this quarter, drafting of the final reports of the various participants was initiated. Several drafts are included in this report as the University technical reports

Nonintrusive tracing in the Internet

Intruders that log in through a series of machines when conducting an attack are hard to trace because of the complex architecture of the Internet. The thumbprinting method provides an efficient way of tracing such intruders by determining whether two connections are part of the same connection chain. Because many connections are transient and therefore short in length, choosing the best time interval to thumbprint over can be an issue. In this paper, we provide a way to shorten the time interval used for thumbprinting. We then study some special properties of the thumbprinting function. We also study another mechanism for tracing intruders in the Internet based on a timestamping approach, which passively monitors flows between source and destination pairs. Given a potentially suspicious source, we identify its true destination. We compute the error probability of our algorithm and show that its value decreases exponentially as the observation time increases. Our simulation results show that our approach performs well