Wide spectrum attribution: Using deception for attribution intelligence in cyber attacks

Modern cyber attacks have evolved considerably. The skill level required to conduct a cyber attack is low. Computing power is cheap, targets are diverse and plentiful. Point-and-click crimeware kits are widely circulated in the underground economy, while source code for sophisticated malware such as Stuxnet is available for all to download and repurpose. Despite decades of research into defensive techniques, such as firewalls, intrusion detection systems, anti-virus, code auditing, etc, the quantity of successful cyber attacks continues to increase, as does the number of vulnerabilities identified. Measures to identify perpetrators, known as attribution, have existed for as long as there have been cyber attacks. The most actively researched technical attribution techniques involve the marking and logging of network packets. These techniques are performed by network devices along the packet journey, which most often requires modification of existing router hardware and/or software, or the inclusion of additional devices. These modifications require wide-scale infrastructure changes that are not only complex and costly, but invoke legal, ethical and governance issues. The usefulness of these techniques is also often questioned, as attack actors use multiple stepping stones, often innocent systems that have been compromised, to mask the true source. As such, this thesis identifies that no publicly known previous work has been deployed on a wide-scale basis in the Internet infrastructure. This research investigates the use of an often overlooked tool for attribution: cyber de- ception. The main contribution of this work is a significant advancement in the field of deception and honeypots as technical attribution techniques. Specifically, the design and implementation of two novel honeypot approaches; i) Deception Inside Credential Engine (DICE), that uses policy and honeytokens to identify adversaries returning from different origins and ii) Adaptive Honeynet Framework (AHFW), an introspection and adaptive honeynet framework that uses actor-dependent triggers to modify the honeynet envi- ronment, to engage the adversary, increasing the quantity and diversity of interactions. The two approaches are based on a systematic review of the technical attribution litera- ture that was used to derive a set of requirements for honeypots as technical attribution techniques. Both approaches lead the way for further research in this field

A privacy preserving framework for cyber-physical systems and its integration in real world applications

A cyber-physical system (CPS) comprises of a network of processing and communication capable sensors and actuators that are pervasively embedded in the physical world. These intelligent computing elements achieve the tight combination and coordination between the logic processing and physical resources. It is envisioned that CPS will have great economic and societal impact, and alter the qualify of life like what Internet has done. This dissertation focuses on the privacy issues in current and future CPS applications. as thousands of the intelligent devices are deeply embedded in human societies, the system operations may potentially disclose the sensitive information if no privacy preserving mechanism is designed. This dissertation identifies data privacy and location privacy as the representatives to investigate the privacy problems in CPS. The data content privacy infringement occurs if the adversary can determine or partially determine the meaning of the transmitted data or the data stored in the storage. The location privacy, on the other hand, is the secrecy that a certain sensed object is associated to a specific location, the disclosure of which may endanger the sensed object. The location privacy may be compromised by the adversary through hop-by-hop traceback along the reverse direction of the message routing path. This dissertation proposes a public key based access control scheme to protect the data content privacy. Recent advances in efficient public key schemes, such as ECC, have already shown the feasibility to use public key schemes on low power devices including sensor motes. In this dissertation, an efficient public key security primitives, WM-ECC, has been implemented for TelosB and MICAz, the two major hardware platform in current sensor networks. WM-ECC achieves the best performance among the academic implementations. Based on WM-ECC, this dissertation has designed various security schemes, including pairwise key establishment, user access control and false data filtering mechanism, to protect the data content privacy. The experiments presented in this dissertation have shown that the proposed schemes are practical for real world applications. to protect the location privacy, this dissertation has considered two adversary models. For the first model in which an adversary has limited radio detection capability, the privacy-aware routing schemes are designed to slow down the adversary\u27s traceback progress. Through theoretical analysis, this dissertation shows how to maximize the adversary\u27s traceback time given a power consumption budget for message routing. Based on the theoretical results, this dissertation also proposes a simple and practical weighted random stride (WRS) routing scheme. The second model assumes a more powerful adversary that is able to monitor all radio communications in the network. This dissertation proposes a random schedule scheme in which each node transmits at a certain time slot in a period so that the adversary would not be able to profile the difference in communication patterns among all the nodes. Finally, this dissertation integrates the proposed privacy preserving framework into Snoogle, a sensor nodes based search engine for the physical world. Snoogle allows people to search for the physical objects in their vicinity. The previously proposed privacy preserving schemes are applied in the application to achieve the flexible and resilient privacy preserving capabilities. In addition to security and privacy, Snoogle also incorporates a number of energy saving and communication compression techniques that are carefully designed for systems composed of low-cost, low-power embedded devices. The evaluation study comprises of the real world experiments on a prototype Snoogle system and the scalability simulations

Message traceback systems dancing with the devil

The research community has produced a great deal of work in recent years in the areas of IP, layer 2 and connection-chain traceback. We collectively designate these as message traceback systems which, invariably aim to locate the origin of network data, in spite of any alterations effected to that data (whether legitimately or fraudulently). This thesis provides a unifying definition of spoofing and a classification based on this which aims to encompass all streams of message traceback research. The feasibility of this classification is established through its application to our literature review of the numerous known message traceback systems. We propose two layer 2 (L2) traceback systems, switch-SPIE and COTraSE, which adopt different approaches to logging based L2 traceback for switched ethernet. Whilst message traceback in spite of spoofing is interesting and perhaps more challenging than at first seems, one might say that it is rather academic. Logging of network data is a controversial and unpopular notion and network administrators don't want the added installation and maintenance costs. However, European Parliament Directive 2006/24/EC requires that providers of publicly available electronic communications networks retain data in a form similar to mobile telephony call records, from April 2009 and for periods of up to 2 years. This thesis identifies the relevance of work in all areas of message traceback to the European data retention legislation. In the final part of this thesis we apply our experiences with L2 traceback, together with our definitions and classification of spoofing to discuss the issues that EU data retention implementations should consider. It is possible to 'do logging right' and even safeguard user privacy. However this can only occur if we fully understand the technical challenges, requiring much further work in all areas of logging based, message traceback systems. We have no choice but to dance with the devil.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

An Accountability Architecture for the Internet

In the current Internet, senders are not accountable for the packets they send. As a result, malicious users send unwanted traffic that wastes shared resources and degrades network performance. Stopping such attacks requires identifying the responsible principal and filtering any unwanted traffic it sends. However, senders can obscure their identity: a packet identifies its sender only by the source address, but the Internet Protocol does not enforce that this address be correct. Additionally, affected destinations have no way to prevent the sender from continuing to cause harm. An accountable network binds sender identities to packets they send for the purpose of holding senders responsible for their traffic. In this dissertation, I present an accountable network-level architecture that strongly binds senders to packets and gives receivers control over who can send traffic to them. Holding senders accountable for their actions would prevent many of the attacks that disrupt the Internet today. Previous work in attack prevention proposes methods of binding packets to senders, giving receivers control over who sends what to them, or both. However, they all require trusted elements on the forwarding path, to either assist in identifying the sender or to filter unwanted packets. These elements are often not under the control of the receiver and may become corrupt. This dissertation shows that the Internet architecture can be extended to allow receivers to block traffic from unwanted senders, even in the presence of malicious devices in the forwarding path. This dissertation validates this thesis with three contributions. The first contribution is DNA, a network architecture that strongly binds packets to their sender, allowing routers to reject unaccountable traffic and recipients to block traffic from unwanted senders. Unlike prior work, which trusts on-path devices to behave correctly, the only trusted component in DNA is an identity certification authority. All other entities may misbehave and are either blocked or evicted from the network. The second contribution is NeighborhoodWatch, a secure, distributed, scalable object store that is capable of withstanding misbehavior by its constituent nodes. DNA uses NeighborhoodWatch to store receiver-specific requests block individual senders. The third contribution is VanGuard, an accountable capability architecture. Capabilities are small, receiver-generated tokens that grant the sender permission to send traffic to receiver. Existing capability architectures are not accountable, assume a protected channel for obtaining capabilities, and allow on-path devices to steal capabilities. VanGuard builds a capability architecture on top of DNA, preventing capability theft and protecting the capability request channel by allowing receivers to block senders that flood the channel. Once a sender obtains capabilities, it no longer needs to sign traffic, thus allowing greater efficiency than DNA alone. The DNA architecture demonstrates that it is possible to create an accountable network architecture in which none of the devices on the forwarding path must be trusted. DNA holds senders responsible for their traffic by allowing receivers to block senders; to store this blocking state, DNA relies on the NeighborhoodWatch DHT. VanGuard extends DNA and reduces its overhead by incorporating capabilities, which gives destinations further control over the traffic that sources send to them

Packet filter performance monitor (anti-DDOS algorithm for hybrid topologies)

DDoS attacks are increasingly becoming a major problem. According to Arbor Networks, the largest DDoS attack reported by a respondent in 2015 was 500 Gbps. Hacker News stated that the largest DDoS attack as of March 2016 was over 600 Gbps, and the attack targeted the entire BBC website. With this increasing frequency and threat, and the average DDoS attack duration at about 16 hours, we know for certain that DDoS attacks will not be going away anytime soon. Commercial companies are not effectively providing mitigation techniques against these attacks, considering that major corporations face the same challenges. Current security appliances are not strong enough to handle the overwhelming traffic that accompanies current DDoS attacks. There is also a limited research on solutions to mitigate DDoS attacks. Therefore, there is a need for a means of mitigating DDoS attacks in order to minimize downtime. One possible solution is for organizations to implement their own architectures that are meant to mitigate DDoS attacks. In this dissertation, we present and implement an architecture that utilizes an activity monitor to change the states of firewalls based on their performance in a hybrid network. Both firewalls are connected inline. The monitor is mirrored to monitor the firewall states. The monitor reroutes traffic when one of the firewalls become overwhelmed due to a HTTP DDoS flooding attack. The monitor connects to the API of both firewalls. The communication between the rewalls and monitor is encrypted using AES, based on PyCrypto Python implementation. This dissertation is structured in three parts. The first found the weakness of the hardware firewall and determined its threshold based on spike and endurance tests. This was achieved by flooding the hardware firewall with HTTP packets until the firewall became overwhelmed and unresponsive. The second part implements the same test as the first, but targeted towards the virtual firewall. The same parameters, test factors, and determinants were used; however a different load tester was utilized. The final part was the implementation and design of the firewall performance monitor. The main goal of the dissertation is to minimize downtime when network firewalls are overwhelmed as a result of a DDoS attack

The User Attribution Problem and the Challenge of Persistent Surveillance of User Activity in Complex Networks

In the context of telecommunication networks, the user attribution problem refers to the challenge faced in recognizing communication traffic as belonging to a given user when information needed to identify the user is missing. This is analogous to trying to recognize a nameless face in a crowd. This problem worsens as users move across many mobile networks (complex networks) owned and operated by different providers. The traditional approach of using the source IP address, which indicates where a packet comes from, does not work when used to identify mobile users. Recent efforts to address this problem by exclusively relying on web browsing behavior to identify users were limited to a small number of users (28 and 100 users). This was due to the inability of solutions to link up multiple user sessions together when they rely exclusively on the web sites visited by the user. This study has tackled this problem by utilizing behavior based identification while accounting for time and the sequential order of web visits by a user. Hierarchical Temporal Memories (HTM) were used to classify historical navigational patterns for different users. Each layer of an HTM contains variable order Markov chains of connected nodes which represent clusters of web sites visited in time order by the user (user sessions). HTM layers enable inference generalization by linking Markov chains within and across layers and thus allow matching longer sequences of visited web sites (multiple user sessions). This approach enables linking multiple user sessions together without the need for a tracking identifier such as the source IP address. Results are promising. HTMs can provide high levels of accuracy using synthetic data with 99% recall accuracy for up to 500 users and good levels of recall accuracy of 95 % and 87% for 5 and 10 users respectively when using cellular network data. This research confirmed that the presence of long tail web sites (rarely visited) among many repeated destinations can create unique differentiation. What was not anticipated prior to this research was the very high degree of repetitiveness of some web destinations found in real network data