Patterns and Interactions in Network Security

Networks play a central role in cyber-security: networks deliver security attacks, suffer from them, defend against them, and sometimes even cause them. This article is a concise tutorial on the large subject of networks and security, written for all those interested in networking, whether their specialty is security or not. To achieve this goal, we derive our focus and organization from two perspectives. The first perspective is that, although mechanisms for network security are extremely diverse, they are all instances of a few patterns. Consequently, after a pragmatic classification of security attacks, the main sections of the tutorial cover the four patterns for providing network security, of which the familiar three are cryptographic protocols, packet filtering, and dynamic resource allocation. Although cryptographic protocols hide the data contents of packets, they cannot hide packet headers. When users need to hide packet headers from adversaries, which may include the network from which they are receiving service, they must resort to the pattern of compound sessions and overlays. The second perspective comes from the observation that security mechanisms interact in important ways, with each other and with other aspects of networking, so each pattern includes a discussion of its interactions.Comment: 63 pages, 28 figures, 56 reference

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

Distributed Denial of Service Attack Detection

Distributed Denial of Service (DDoS) attacks on web applications has been a persistent threat. Successful attacks can lead to inaccessible service to legitimate users in time and loss of business reputation. Most research effort on DDoS focused on network layer attacks. Existing approaches on application layer DDoS attack mitigation have limitations such as the lack of detection ability for low rate DDoS and not being able to detect attacks targeting resource files. In this work, we propose DDoS attack detection using concepts from information retrieval and machine learning. We include two popular concepts from information retrieval: Term Frequency (TF)-Inverse Document Frequency (IDF) and Latent Semantic Indexing (LSI). We analyzed web server log data generated in a distributed environment. Our evaluation results indicate that while all the approaches can detect various ranges of attacks, information retrieval approaches can identify attacks ongoing in a given session. All the approaches can detect three well known application level DDoS attacks (trivial, intermediate, advanced). Further, these approaches can enable an administrator identifying new pattern of DDoS attacks

A Study on Intrusion Detection System in Wireless Sensor Networks

The technology of Wireless Sensor Networks (WSNs) has become most significant in present day. WSNs are extensively used in applications like military, industry, health, smart homes and smart cities. All the applications of WSN require secure communication between the sensor nodes and the base station. Adversary compromises at the sensor nodes to introduce different attacks into WSN. Hence, suitable Intrusion Detection System (IDS) is essential in WSN to defend against the security attack. IDS approaches for WSN are classified based on the mechanism used to detect the attacks. In this paper, we present the taxonomy of security attacks, different IDS mechanisms for detecting attacks and performance metrics used to assess the IDS algorithm for WSNs. Future research directions on IDS in WSN are also discussed

A Survey on Enterprise Network Security: Asset Behavioral Monitoring and Distributed Attack Detection

Enterprise networks that host valuable assets and services are popular and frequent targets of distributed network attacks. In order to cope with the ever-increasing threats, industrial and research communities develop systems and methods to monitor the behaviors of their assets and protect them from critical attacks. In this paper, we systematically survey related research articles and industrial systems to highlight the current status of this arms race in enterprise network security. First, we discuss the taxonomy of distributed network attacks on enterprise assets, including distributed denial-of-service (DDoS) and reconnaissance attacks. Second, we review existing methods in monitoring and classifying network behavior of enterprise hosts to verify their benign activities and isolate potential anomalies. Third, state-of-the-art detection methods for distributed network attacks sourced from external attackers are elaborated, highlighting their merits and bottlenecks. Fourth, as programmable networks and machine learning (ML) techniques are increasingly becoming adopted by the community, their current applications in network security are discussed. Finally, we highlight several research gaps on enterprise network security to inspire future research.Comment: Journal paper submitted to Elseive

DoS and DDoS Attacks: Defense, Detection and Traceback Mechanisms - A Survey

Denial of Service (DoS) or Distributed Denial of Service (DDoS) attacks are typically explicit attempts to exhaust victim2019;s bandwidth or disrupt legitimate users2019; access to services. Traditional architecture of internet is vulnerable to DDoS attacks and it provides an opportunity to an attacker to gain access to a large number of compromised computers by exploiting their vulnerabilities to set up attack networks or Botnets. Once attack network or Botnet has been set up, an attacker invokes a large-scale, coordinated attack against one or more targets. Asa result of the continuous evolution of new attacks and ever-increasing range of vulnerable hosts on the internet, many DDoS attack Detection, Prevention and Traceback mechanisms have been proposed, In this paper, we tend to surveyed different types of attacks and techniques of DDoS attacks and their countermeasures. The significance of this paper is that the coverage of many aspects of countering DDoS attacks including detection, defence and mitigation, traceback approaches, open issues and research challenges

A composable approach to design of newer techniques for large-scale denial-of-service attack attribution

Since its early days, the Internet has witnessed not only a phenomenal growth, but also a large number of security attacks, and in recent years, denial-of-service (DoS) attacks have emerged as one of the top threats. The stateless and destination-oriented Internet routing combined with the ability to harness a large number of compromised machines and the relative ease and low costs of launching such attacks has made this a hard problem to address. Additionally, the myriad requirements of scalability, incremental deployment, adequate user privacy protections, and appropriate economic incentives has further complicated the design of DDoS defense mechanisms. While the many research proposals to date have focussed differently on prevention, mitigation, or traceback of DDoS attacks, the lack of a comprehensive approach satisfying the different design criteria for successful attack attribution is indeed disturbing. Our first contribution here has been the design of a composable data model that has helped us represent the various dimensions of the attack attribution problem, particularly the performance attributes of accuracy, effectiveness, speed and overhead, as orthogonal and mutually independent design considerations. We have then designed custom optimizations along each of these dimensions, and have further integrated them into a single composite model, to provide strong performance guarantees. Thus, the proposed model has given us a single framework that can not only address the individual shortcomings of the various known attack attribution techniques, but also provide a more wholesome counter-measure against DDoS attacks. Our second contribution here has been a concrete implementation based on the proposed composable data model, having adopted a graph-theoretic approach to identify and subsequently stitch together individual edge fragments in the Internet graph to reveal the true routing path of any network data packet. The proposed approach has been analyzed through theoretical and experimental evaluation across multiple metrics, including scalability, incremental deployment, speed and efficiency of the distributed algorithm, and finally the total overhead associated with its deployment. We have thereby shown that it is realistically feasible to provide strong performance and scalability guarantees for Internet-wide attack attribution. Our third contribution here has further advanced the state of the art by directly identifying individual path fragments in the Internet graph, having adopted a distributed divide-and-conquer approach employing simple recurrence relations as individual building blocks. A detailed analysis of the proposed approach on real-life Internet topologies with respect to network storage and traffic overhead, has provided a more realistic characterization. Thus, not only does the proposed approach lend well for simplified operations at scale but can also provide robust network-wide performance and security guarantees for Internet-wide attack attribution. Our final contribution here has introduced the notion of anonymity in the overall attack attribution process to significantly broaden its scope. The highly invasive nature of wide-spread data gathering for network traceback continues to violate one of the key principles of Internet use today - the ability to stay anonymous and operate freely without retribution. In this regard, we have successfully reconciled these mutually divergent requirements to make it not only economically feasible and politically viable but also socially acceptable. This work opens up several directions for future research - analysis of existing attack attribution techniques to identify further scope for improvements, incorporation of newer attributes into the design framework of the composable data model abstraction, and finally design of newer attack attribution techniques that comprehensively integrate the various attack prevention, mitigation and traceback techniques in an efficient manner

Impact of denial of service solutions on network quality of service

The Internet has become a universal communication network tool. It has evolved from a platform that supports best-effort traffic to one that now carries different traffic types including those involving continuous media with quality of service (QoS) requirements. As more services are delivered over the Internet, we face increasing risk to their availability given that malicious attacks on those Internet services continue to increase. Several networks have witnessed denial of service (DoS) and distributed denial of service (DDoS) attacks over the past few years which have disrupted QoS of network services, thereby violating the Service Level Agreement (SLA) between the client and the Internet Service Provider (ISP). Hence DoS or DDoS attacks are major threats to network QoS. In this paper we survey techniques and solutions that have been deployed to thwart DoS and DDoS attacks and we evaluate them in terms of their impact on network QoS for Internet services. We also present vulnerabilities that can be exploited for QoS protocols and also affect QoS if exploited. In addition, we also highlight challenges that still need to be addressed to achieve end-to-end QoS with recently proposed DoS/DDoS solutions

On mitigating distributed denial of service attacks

Denial of service (DoS) attacks and distributed denial of service (DDoS) attacks are probably the most ferocious threats in the Internet, resulting in tremendous economic and social implications/impacts on our daily lives that are increasingly depending on the wellbeing of the Internet. How to mitigate these attacks effectively and efficiently has become an active research area. The critical issues here include 1) IP spoofing, i.e., forged source lIP addresses are routinely employed to conceal the identities of the attack sources and deter the efforts of detection, defense, and tracing; 2) the distributed nature, that is, hundreds or thousands of compromised hosts are orchestrated to attack the victim synchronously. Other related issues are scalability, lack of incentives to deploy a new scheme, and the effectiveness under partial deployment. This dissertation investigates and proposes effective schemes to mitigate DDoS attacks. It is comprised of three parts. The first part introduces the classification of DDoS attacks and the evaluation of previous schemes. The second part presents the proposed IP traceback scheme, namely, autonomous system-based edge marking (ASEM). ASEM enhances probabilistic packet marking (PPM) in several aspects: (1) ASEM is capable of addressing large-scale DDoS attacks efficiently; (2) ASEM is capable of handling spoofed marking from the attacker and spurious marking incurred by subverted routers, which is a unique and critical feature; (3) ASEM can significantly reduce the number of marked packets required for path reconstruction and suppress false positives as well. The third part presents the proposed DDoS defense mechanisms, including the four-color-theorem based path marking, and a comprehensive framework for DDoS defense. The salient features of the framework include (1) it is designed to tackle a wide spectrum of DDoS attacks rather than a specified one, and (2) it can differentiate malicious traffic from normal ones. The receiver-center design avoids several related issues such as scalability, and lack of incentives to deploy a new scheme. Finally, conclusions are drawn and future works are discussed

Multi-agent-based DDoS detection on big data systems

The Hadoop framework has become the most deployed platform for processing Big Data. Despite its advantages, Hadoop s infrastructure is still deployed within the secured network perimeter because the framework lacks adequate inherent security mechanisms against various security threats. However, this approach is not sufficient for providing adequate security layer against attacks such as Distributed Denial of Service. Furthermore, current work to secure Hadoop s infrastructure against DDoS attacks is unable to provide a distributed node-level detection mechanism. This thesis presents a software agent-based framework that allows distributed, real-time intelligent monitoring and detection of DDoS attack at Hadoop s node-level. The agent s cognitive system is ingrained with cumulative sum statistical technique to analyse network utilisation and average server load and detect attacks from these measurements. The framework is a multi-agent architecture with transducer agents that interface with each Hadoop node to provide real-time detection mechanism. Moreover, the agents contextualise their beliefs by training themselves with the contextual information of each node and monitor the activities of the node to differentiate between normal and anomalous behaviours. In the experiments, the framework was exposed to TCP SYN and UDP flooding attacks during a legitimate MapReduce job on the Hadoop testbed. The experimental results were evaluated regarding performance metrics such as false-positive ratio, false-negative ratio and response time to attack. The results show that UDP and TCP SYN flooding attacks can be detected and confirmed on multiple nodes in nineteen seconds with 5.56% false-positive ration, 7.70% false-negative ratio and 91.5% success rate of detection. The results represent an improvement compare to the state-of the-ar