Biblio

With the pretty prompt growth in Internet content, the main usage pattern of internet is shifting from traditional host-to-host model to content dissemination model. To support content distribution, content delivery networks (CDNs) gives an ad-hoc solution and some of future internet projects suggest a clean-slate design. Web applications have become one of the fundamental internet services. How to effectively support the popular browser-based web application is one of keys to success for future internet projects. This paper proposes the IDNet-based web applications. IDNet consists of id/locator separation scheme and domain-insulated autonomous network architecture (DIANA) which redesign the future internet in the clean slate basis. We design and develop an IDNet Browser based on the open source Qt. IDNet browser enables ID fetching and rendering by both `idp:/' schemes URID (Universal Resource Identifier) and `http:/' schemes URI in HTML The experiment shows that it can well be applicable to the IDNet test topology.

In response to the critical challenges of the current Internet architecture and its protocols, a set of so-called clean slate designs has been proposed. Common among them is an addressing scheme that separates location and identity with self-certifying, flat and non-aggregatable address components. Each component is long, reaching a few kilobits, and would consume an amount of fast memory in data plane devices (e.g., routers) that is far beyond existing capacities. To address this challenge, we present Caesar, a high-speed and length-agnostic forwarding engine for future border routers, performing most of the lookups within three fast memory accesses. To compress forwarding states, Caesar constructs scalable and reliable Bloom filters in Ternary Content Addressable Memory (TCAM). To guarantee correctness, Caesar detects false positives at high speed and develops a blacklisting approach to handling them. In addition, we optimize our design by introducing a hashing scheme that reduces the number of hash computations from k to log(k) per lookup based on hash coding theory. We handle routing updates while keeping filters highly utilized in address removals. We perform extensive analysis and simulations using real traffic and routing traces to demonstrate the benefits of our design. Our evaluation shows that Caesar is more energy-efficient and less expensive (in terms of total cost) compared to optimized IPv6 TCAM-based solutions by up to 67% and 43% respectively. In addition, the total cost of our design is approximately the same for various address lengths.

Content delivery such as P2P or video streaming generates the main part of the Internet traffic and Content Centric Network (CCN) appears as an appropriate architecture to satisfy the user needs. However, the lack of scalable routing scheme is one of the main obstacles that slows down a large deployment of CCN at an Internet-scale. In this paper we propose to use the Software-Defined Networking (SDN) paradigm to decouple data plane and control plane and present SRSC, a new routing scheme for CCN. Our solution is a clean-slate approach using only CCN messages and the SDN paradigm. We implemented our solution into the NS-3 simulator and perform simulations of our proposal. SRSC shows better performances than the flooding scheme used by default in CCN: it reduces the number of messages, while still improves CCN caching performances.

We propose a clean-slate network architecture called Centralized Identifier Network (CIN) which jointly considers the ideas of both control plane/forwarding plane separation and identifier/locator separation. In such an architecture, a controller cluster is designed to perform routers' link states gathering and routing calculation/handing out. Meanwhile, a tailor-made router without routing calculation function is designed to forward packets and communicate with its controller. Furthermore, A router or a host owns a globally unique ID and a host should be registered to a router whose ID will be the host's location. Control plane/forwarding plane separation enables CIN easily re-splitting the network functions into finer optional building blocks for sufficient flexibility and adaptability. Identifier/locator separation helps CIN deal with serious scaling problems and offer support for host mobility. This article mainly shows the routing mechanism of CIN. Furthermore, numerical results are presented to demonstrate the performance of the proposed mechanism.

In this paper, we present the design, architecture, and implementation of a novel analysis engine, called Feature Collection and Correlation Engine (FCCE), that finds correlations across a diverse set of data types spanning over large time windows with very small latency and with minimal access to raw data. FCCE scales well to collecting, extracting, and querying features from geographically distributed large data sets. FCCE has been deployed in a large production network with over 450,000 workstations for 3 years, ingesting more than 2 billion events per day and providing low latency query responses for various analytics. We explore two security analytics use cases to demonstrate how we utilize the deployment of FCCE on large diverse data sets in the cyber security domain: 1) detecting fluxing domain names of potential botnet activity and identifying all the devices in the production network querying these names, and 2) detecting advanced persistent threat infection. Both evaluation results and our experience with real-world applications show that FCCE yields superior performance over existing approaches, and excels in the challenging cyber security domain by correlating multiple features and deriving security intelligence.

During an advanced persistent threat (APT), an attacker group usually establish more than one C&C server and these C&C servers will change their domain names and corresponding IP addresses over time to be unseen by anti-virus software or intrusion prevention systems. For this reason, discovering and catching C&C sites becomes a big challenge in information security. Based on our observations and deductions, a malware tends to contain a fixed user agent string, and the connection behaviors generated by a malware is different from that by a benign service or a normal user. This paper proposed a new method comprising filtering and clustering methods to detect C&C servers with a relatively higher coverage rate. The experiments revealed that the proposed method can successfully detect C&C Servers, and the can provide an important clue for detecting APT.

Advanced Persistent Threat (APT) attacks, which have become prevalent in recent years, are classified into four phases. These are initial compromise phase, attacking infrastructure building phase, penetration and exploration phase, and mission execution phase. The malware on infected terminals attempts various communications on and after the attacking infrastructure building phase. In this research, using OpenFlow technology for virtual networks, we developed a system of identifying infected terminals by detecting communication events of malware communications in APT attacks. In addition, we prevent information fraud by using OpenFlow, which works as real-time path control. To evaluate our system, we executed malware infection experiments with a simulation tool for APT attacks and malware samples. In these experiments, an existing network using only entry control measures was prepared. As a result, we confirm the developed system is effective.

Recent years have seen the rise of sophisticated attacks including advanced persistent threats (APT) which pose severe risks to organizations and governments. Additionally, new malware strains appear at a higher rate than ever before. Since many of these malware evade existing security products, traditional defenses deployed by enterprises today often fail at detecting infections at an early stage. We address the problem of detecting early-stage APT infection by proposing a new framework based on belief propagation inspired from graph theory. We demonstrate that our techniques perform well on two large datasets. We achieve high accuracy on two months of DNS logs released by Los Alamos National Lab (LANL), which include APT infection attacks simulated by LANL domain experts. We also apply our algorithms to 38TB of web proxy logs collected at the border of a large enterprise and identify hundreds of malicious domains overlooked by state-of-the-art security products.

Advanced Persistent Threat (APT) is a complex (Advanced) cyber-attack (Threat) against specific targets over long periods of time (Persistent) carried out by nation states or terrorist groups with highly sophisticated levels of expertise to establish entries into organizations, which are critical to a country's socio-economic status. The key identifier in such persistent threats is that patterns are long term, could be high priority, and occur consistently over a period of time. This paper focuses on identifying persistent threat patterns in network data, particularly data collected from Intrusion Detection Systems. We utilize Association Rule Mining (ARM) to detect persistent threat patterns on network data. We identify potential persistent threat patterns, which are frequent but at the same time unusual as compared with the other frequent patterns.

In this paper, we present a formal model for the verification of the DNSsec Protocol in the interactive theorem prover Isabelle/HOL. Relying on the inductive approach to security protocol verification, this formal analysis provides a more expressive representation than the widely accepted model checking analysis. Our mechanized model allows to represent the protocol, all its possible traces and the attacker and his knowledge. The fine grained model allows to show origin authentication, and replay attack prevention. Most prominently, we succeed in expressing Delegation Signatures and proving their authenticity formally.

For efficient deployment of sensor nodes required in many logistic applications, it's necessary to build security mechanisms for a secure wireless communication. End-to-end security plays a crucial role for the communication in these networks. This provides the confidentiality, the authentication and mostly the prevention from many attacks at high level. In this paper, we propose a lightweight key exchange protocol WSKE (Wireless Sensor Key Exchange) for IP-based wireless sensor networks. This protocol proposes techniques that allows to adapt IKEv2 (Internet Key Exchange version 2) mechanisms of IPSEC/6LoWPAN networks. In order to check these security properties, we have used a formal verification tools called AVISPA.
 

The significant dependence on cyberspace has indeed brought new risks that often compromise, exploit and damage invaluable data and systems. Thus, the capability to proactively infer malicious activities is of paramount importance. In this context, inferring probing events, which are commonly the first stage of any cyber attack, render a promising tactic to achieve that task. We have been receiving for the past three years 12 GB of daily malicious real darknet data (i.e., Internet traffic destined to half a million routable yet unallocated IP addresses) from more than 12 countries. This paper exploits such data to propose a novel approach that aims at capturing the behavior of the probing sources in an attempt to infer their orchestration (i.e., coordination) pattern. The latter defines a recently discovered characteristic of a new phenomenon of probing events that could be ominously leveraged to cause drastic Internet-wide and enterprise impacts as precursors of various cyber attacks. To accomplish its goals, the proposed approach leverages various signal and statistical techniques, information theoretical metrics, fuzzy approaches with real malware traffic and data mining methods. The approach is validated through one use case that arguably proves that a previously analyzed orchestrated probing event from last year is indeed still active, yet operating in a stealthy, very low rate mode. We envision that the proposed approach that is tailored towards darknet data, which is frequently, abundantly and effectively used to generate cyber threat intelligence, could be used by network security analysts, emergency response teams and/or observers of cyber events to infer large-scale orchestrated probing events for early cyber attack warning and notification.
 

Mobile Ad-hoc Network is highly susceptible towards the security attacks due to its dynamic topology, resource constraint, energy constraint operations, limited physical security and lack of infrastructure. Misleading routing attack (MIRA) in MANET intend to delay packet to its fullest in order to generate time outs at the source as packets will not reach in time. Its main objective is to generate delay and increase network overhead. It is a variation to the sinkhole attack. In this paper, we have proposed a detection scheme to detect the malicious nodes at route discovery as well as at packet transmissions. The simulation results of MIRA attack indicate that though delay is increased by 91.30% but throughput is not affected which indicates that misleading routing attack is difficult to detect. The proposed detection scheme when applied to misleading routing attack suggests a significant decrease in delay.

The key challenge to a datacenter network is its scalability to handle many customers and their applications. In a datacenter network, packet classification plays an important role in supporting various network services. Previous algorithms store classification rules with the same length combinations in a hash table to simplify the search procedure. The search performance of hash-based algorithms is tied to the number of hash tables. To achieve fast and scalable packet classification, we propose an algorithm, encoded rule expansion, to transform rules into an equivalent set of rules with fewer distinct length combinations, without affecting the classification results. The new algorithm can minimize the storage penalty of transformation and achieve a short search time. In addition, the scheme supports fast incremental updates. Our simulation results show that more than 90% hash tables can be eliminated. The reduction of length combinations leads to an improvement on speed performance of packet classification by an order of magnitude. The results also show that the software implementation of our scheme without using any hardware parallelism can support up to one thousand customer VLANs and one million rules, where each rule consumes less than 60 bytes and each packet classification can be accomplished under 50 memory accesses.
 

High-speed IP address lookup is essential to achieve wire-speed packet forwarding in Internet routers. Ternary content addressable memory (TCAM) technology has been adopted to solve the IP address lookup problem because of its ability to perform fast parallel matching. However, the applicability of TCAMs presents difficulties due to cost and power dissipation issues. Various algorithms and hardware architectures have been proposed to perform the IP address lookup using ordinary memories such as SRAMs or DRAMs without using TCAMs. Among the algorithms, we focus on two efficient algorithms providing high-speed IP address lookup: parallel multiple-hashing (PMH) algorithm and binary search on level algorithm. This paper shows how effectively an on-chip Bloom filter can improve those algorithms. A performance evaluation using actual backbone routing data with 15,000-220,000 prefixes shows that by adding a Bloom filter, the complicated hardware for parallel access is removed without search performance penalty in parallel-multiple hashing algorithm. Search speed has been improved by 30-40 percent by adding a Bloom filter in binary search on level algorithm.
 

Skype has been a typical choice for providing VoIP service nowadays and is well-known for its broad range of features, including voice-calls, instant messaging, file transfer and video conferencing, etc. Considering its wide application, from the viewpoint of ISPs, it is essential to identify Skype flows and thus optimize network performance and forecast future needs. However, in general, a host is likely to run multiple network applications simultaneously, which makes it much harder to classify each and every Skype flow from mixed traffic exactly. Especially, current techniques usually focus on host-level identification and do not have the ability to identify Skype traffic at the flow-level. In this paper, we first reveal the unique sequence signatures of Skype UDP flows and then implement a practical online system named SkyTracer for precise Skype traffic identification. To the best of our knowledge, this is the first time to utilize the strong sequence signatures to carry out early identification of Skype traffic. The experimental results show that SkyTracer can achieve very high accuracy at fine-grained level in identifying Skype traffic.

This work proposes a novel approach to infer and characterize Internet-scale DNS amplification DDoS attacks by leveraging the darknet space. Complementary to the pioneer work on inferring Distributed Denial of Service (DDoS) using darknet, this work shows that we can extract DDoS activities without relying on backscattered analysis. The aim of this work is to extract cyber security intelligence related to DNS Amplification DDoS activities such as detection period, attack duration, intensity, packet size, rate and geo- location in addition to various network-layer and flow-based insights. To achieve this task, the proposed approach exploits certain DDoS parameters to detect the attacks. We empirically evaluate the proposed approach using 720 GB of real darknet data collected from a /13 address space during a recent three months period. Our analysis reveals that the approach was successful in inferring significant DNS amplification DDoS activities including the recent prominent attack that targeted one of the largest anti-spam organizations. Moreover, the analysis disclosed the mechanism of such DNS amplification DDoS attacks. Further, the results uncover high-speed and stealthy attempts that were never previously documented. The case study of the largest DDoS attack in history lead to a better understanding of the nature and scale of this threat and can generate inferences that could contribute in detecting, preventing, assessing, mitigating and even attributing of DNS amplification DDoS activities.
 

With the global widespread usage of the Internet, more and more cyber-attacks are being performed. Many of these attacks utilize IP address spoofing. This paper describes IP spoofing attacks and the proposed methods currently available to detect or prevent them. In addition, it presents a statistical analysis of the Hop Count parameter used in our proposed IP spoofing detection algorithm. We propose an algorithm, inspired by the Hop Count Filtering (HCF) technique, that changes the learning phase of HCF to include all the possible available Hop Count values. Compared to the original HCF method and its variants, our proposed method increases the true positive rate by at least 9% and consequently increases the overall accuracy of an intrusion detection system by at least 9%. Our proposed method performs in general better than HCF method and its variants.
 

The Domain Name System (DNS) is widely seen as a vital protocol of the modern Internet. For example, popular services like load balancers and Content Delivery Networks heavily rely on DNS. Because of its important role, DNS is also a desirable target for malicious activities such as spamming, phishing, and botnets. To protect networks against these attacks, a number of DNS-based security approaches have been proposed. The key insight of our study is to measure the effectiveness of security approaches that rely on DNS in large-scale networks. For this purpose, we answer the following questions, How often is DNS used? Are most of the Internet flows established after contacting DNS? In this study, we collected data from the University of Auckland campus network with more than 33,000 Internet users and processed it to find out how DNS is being used. Moreover, we studied the flows that were established with and without contacting DNS. Our results show that less than 5 percent of the observed flows use DNS. Therefore, we argue that those security approaches that solely depend on DNS are not sufficient to protect large-scale networks.

Botnets are one of the most destructive threats against the cyber security. Recently, HTTP protocol is frequently utilized by botnets as the Command and Communication (C&C) protocol. In this work, we aim to detect HTTP based botnet activity based on botnet behaviour analysis via machine learning approach. To achieve this, we employ flow-based network traffic utilizing NetFlow (via Softflowd). The proposed botnet analysis system is implemented by employing two different machine learning algorithms, C4.5 and Naive Bayes. Our results show that C4.5 learning algorithm based classifier obtained very promising performance on detecting HTTP based botnet activity.

Botnets are the most common vehicle of cyber-criminal activity. They are used for spamming, phishing, denial-of-service attacks, brute-force cracking, stealing private information, and cyber warfare. Botnets carry out network scans for several reasons, including searching for vulnerable machines to infect and recruit into the botnet, probing networks for enumeration or penetration, etc. We present the measurement and analysis of a horizontal scan of the entire IPv4 address space conducted by the Sality botnet in February 2011. This 12-day scan originated from approximately 3 million distinct IP addresses and used a heavily coordinated and unusually covert scanning strategy to try to discover and compromise VoIP-related (SIP server) infrastructure. We observed this event through the UCSD Network Telescope, a /8 darknet continuously receiving large amounts of unsolicited traffic, and we correlate this traffic data with other public sources of data to validate our inferences. Sality is one of the largest botnets ever identified by researchers. Its behavior represents ominous advances in the evolution of modern malware: the use of more sophisticated stealth scanning strategies by millions of coordinated bots, targeting critical voice communications infrastructure. This paper offers a detailed dissection of the botnet's scanning behavior, including general methods to correlate, visualize, and extrapolate botnet behavior across the global Internet.
 

Law enforcement employs an investigative approach based on marked money bills to track illegal drug dealers. In this paper we discuss research that aims at providing law enforcement with the cyber counterpart of that approach in order to track perpetrators that operate botnets. We have devised a novel steganographic approach that generates a watermark hidden within a honey token, i.e. A decoy Word document. The covert bits that comprise the watermark are carried via secret interpretation of object properties in the honey token. The encoding and decoding of object properties into covert bits follow a scheme based on bijective functions generated via a chaotic logistic map. The watermark is retrievable via a secret cryptographic key, which is generated and held by law enforcement. The honey token is leaked to a botmaster via a honey net. In the paper, we elaborate on possible means by which law enforcement can track the leaked honey token to the IP address of a botmaster's machine.

In the paper a programmable management framework for SDN networks is presented. The concept is in-line with SDN philosophy - it can be programmed from scratch. The implemented management functions can be case dependent. The concept introduces a new node in the SDN architecture, namely the SDN manager. In compliance with the latest trends in network management the approach allows for embedded management of all network nodes and gradual implementation of management functions providing their code lifecycle management as well as the ability to on-the-fly code update. The described concept is a bottom-up approach, which key element is distributed execution environment (PDEE) that is based on well-established technologies like OSGI and FIPA. The described management idea has strong impact on the evolution of the SDN architecture, because the proposed distributed execution environment is a generic one, therefore it can be used not only for the management, but also for distributing of control or application functions.
 

The hardware and low-level software in many mobile devices are capable of mobile-to-mobile communication, including ad-hoc 802.11, Bluetooth, and cognitive radios. We have started to leverage this capability to provide interpersonal communication both over infrastructure networks (the Internet), and over ad-hoc and delay-tolerant networks composed of the mobile devices themselves. This network is decentralized in the sense that it can function without any infrastructure, but does take advantage of infrastructure connections when available. All interpersonal communication is encrypted and authenticated so packets may be carried by devices belonging to untrusted others. The decentralized model of security builds a flexible trust network on top of the social network of communicating individuals. This social network can be used to prioritize packets to or from individuals closely related by the social network. Other packets are prioritized to favor packets likely to consume fewer network resources. Each device also has a policy that determines how many packets may be forwarded, with the goal of providing useful interpersonal communications using at most 1% of any given resource on mobile devices. One challenge in a fully decentralized network is routing. Our design uses Rendezvous Points (RPs) and Distributed Hash Tables (DHTs) for delivery over infrastructure networks, and hop-limited broadcast and Delay Tolerant Networking (DTN) within the wireless ad-hoc network.

We introduce a cloud-enabled defense mechanism for Internet services against network and computational Distributed Denial-of-Service (DDoS) attacks. Our approach performs selective server replication and intelligent client re-assignment, turning victim servers into moving targets for attack isolation. We introduce a novel system architecture that leverages a "shuffling" mechanism to compute the optimal re-assignment strategy for clients on attacked servers, effectively separating benign clients from even sophisticated adversaries that persistently follow the moving targets. We introduce a family of algorithms to optimize the runtime client-to-server re-assignment plans and minimize the number of shuffles to achieve attack mitigation. The proposed shuffling-based moving target mechanism enables effective attack containment using fewer resources than attack dilution strategies using pure server expansion. Our simulations and proof-of-concept prototype using Amazon EC2 [1] demonstrate that we can successfully mitigate large-scale DDoS attacks in a small number of shuffles, each of which incurs a few seconds of user-perceived latency.