Distributed Denial of Service (DDoS) flooding attacks are one of the biggest concerns for security professionals. DDoS flooding attacks are typically explicit attempts to disrupt legitimate users' access to services. Attackers usually gain access to a large number of computers by exploiting their vulnerabilities to set up attack armies (i.e., Botnets). Once an attack army has been set up, an attacker can invoke a coordinated, large-scale attack against one or more targets. Developing a comprehensive defense mechanism against identified and anticipated DDoS flooding attacks is a desired goal of the intrusion detection and prevention research community. However, the development of such a mechanism requires a comprehensive understanding of the problem and the techniques that have been used thus far in preventing, detecting, and responding to various DDoS flooding attacks. In this paper, we explore the scope of the DDoS flooding attack problem and attempts to combat it. We categorize the DDoS flooding attacks and classify existing countermeasures based on where and when they prevent, detect, and respond to the DDoS flooding attacks. Moreover, we highlight the need for a comprehensive distributed and collaborative defense approach. Our primary intention for this work is to stimulate the research community into developing creative, effective, efficient, and comprehensive prevention, detection, and response mechanisms that address the DDoS flooding problem before, during and after an actual attack.

/pdf/a-survey-of-defense-mechanisms-against-distributed-denial-of-36i5dbdzy6.pdf

A Survey of Defense Mechanisms Against Distributed Denial of Service (DDoS) Flooding Attacks

https://research.ece.ncsu.edu/netwis/papers/13wl-comnet.pdf

Survey Cyber security in the Smart Grid: Survey and challenges

A smart grid is a new form of electricity network with high fidelity power-flow control, self-healing, and energy reliability and energy security using digital communications and control technology. To upgrade an existing power grid into a smart grid, it requires significant dependence on intelligent and secure communication infrastructures. It requires security frameworks for distributed communications, pervasive computing and sensing technologies in smart grid. However, as many of the communication technologies currently recommended to use by a smart grid is vulnerable in cyber security, it could lead to unreliable system operations, causing unnecessary expenditure, even consequential disaster to both utilities and consumers. In this paper, we summarize the cyber security requirements and the possible vulnerabilities in smart grid communications and survey the current solutions on cyber security for smart grid communications.

/pdf/a-survey-on-cyber-security-for-smart-grid-communications-3ah12orzeu.pdf

A Survey on Cyber Security for Smart Grid Communications

IP spoofing has been exploited by Distributed Denial of Service (DDoS) attacks to (1) conceal flooding sources and localities in flooding traffic, and (2) coax legitimate hosts into becoming reflectors, redirecting and amplifying flooding traffic. Thus, the ability to filter spoofed IP packets near victims is essential to their own protection as well as to their avoidance of becoming involuntary DoS reflectors. Although an attacker can forge any field in the IP header, he or she cannot falsify the number of hops an IP packet takes to reach its destination. This hop-count information can be inferred from the Time-to-Live (TTL) value in the IP header. Using a mapping between IP addresses and their hop-counts to an Internet server, the server can distinguish spoofed IP packets from legitimate ones. Base on this observation, we present a novel filtering technique that is immediately deployable to weed out spoofed IP packets. Through analysis using network measurement data, we show that Hop-Count Filtering (HCF) can identify close to 90% of spoofed IP packets, and then discard them with little collateral damage. We implement and evaluate HCF in the Linux kernel, demonstrating its benefits using experimental measurements.

https://users.cs.jmu.edu/aboutams/Public/IP%20TraceBack/Hop-Count%20Filtering%20-%20Defense%20against%20DDoS%20Traffic.pdf

Hop-count filtering: an effective defense against spoofed DDoS traffic

Recent advances have given rise to the popularity and success of cloud computing. However, when outsourcing the data and business application to a third party causes the security and privacy issues to become a critical concern. Throughout the study at hand, the authors obtain a common goal to provide a comprehensive review of the existing security and privacy issues in cloud environments. We have identified five most representative security and privacy attributes (i.e., confidentiality, integrity, availability, accountability, and privacy-preservability). Beginning with these attributes, we present the relationships among them, the vulnerabilities that may be exploited by attackers, the threat models, as well as existing defense strategies in a cloud scenario. Future research directions are previously determined for each attribute.

/pdf/security-and-privacy-in-cloud-computing-bwdkcm2e30.pdf

Security and Privacy in Cloud Computing

Distributed denial of service (DDoS) attacks continue to plague the Internet Defense against these attacks is complicated by spoofed source IP addresses, which make it difficult to determine a packet's true origin We propose Pi (short for path identifier), a new packet marking approach in which a path fingerprint is embedded in each packet, enabling a victim to identify packets traversing the same paths through the Internet on a per packet basis, regardless of source IP address spoofing Pi features many unique properties It is a per-packet deterministic mechanism: each packet traveling along the same path carries the same identifier This allows the victim to take a proactive role in defending against a DDoS attack by using the Pi mark to filter out packets matching the attackers' identifiers on a per packet basis The Pi scheme performs well under large-scale DDoS attacks consisting of thousands of attackers, and is effective even when only half the routers in the Internet participate in packet marking Pi marking and filtering are both extremely lightweight and require negligible state We use traceroute maps of real Internet topologies (eg CAIDA's Skitter (2000) and Burch and Cheswick's Internet Map (1999, 2002)) to simulate DDoS attacks and validate our design

Pi: a path identification mechanism to defend against DDoS attacks

Traceback mechanisms are a critical part of the defense against IP spoofing and DoS attacks, as well as being of forensic value to law enforcement. Currently proposed IP traceback mechanisms are inadequate to address the traceback problem for the following reasons: they require DDoS victims to gather thousands of packets to reconstruct a single attack path; they do not scale to large scale distributed DoS attacks; and they do not support incremental deployment. We propose fast Internet traceback (FIT), a new packet marking approach that significantly improves IP traceback in several dimensions: (1) victims can identify attack paths with high probability after receiving only tens of packets, a reduction of 1-3 orders of magnitude compared to previous packet marking schemes; (2) FIT performs well even in the presence of legacy routers, allowing every FIT-enabled router in path to be identified; and (3) FIT scales to large distributed attacks with thousands of attackers. Compared with previous packet marking schemes, FIT represents a step forward in performance and deployability.

/pdf/fit-fast-internet-traceback-1a8gm3t534.pdf

FIT: fast Internet traceback

Today's Internet hosts are threatened by large-scale distributed denial-of-service (DDoS) attacks. The path identification (Pi) DDoS defense scheme has recently been proposed as a deterministic packet marking scheme that allows a DDoS victim to filter out attack packets on a per packet basis with high accuracy after only a few attack packets are received (Yaar , 2003). In this paper, we propose the StackPi marking, a new packet marking scheme based on Pi, and new filtering mechanisms. The StackPi marking scheme consists of two new marking methods that substantially improve Pi's incremental deployment performance: Stack-based marking and write-ahead marking. Our scheme almost completely eliminates the effect of a few legacy routers on a path, and performs 2-4 times better than the original Pi scheme in a sparse deployment of Pi-enabled routers. For the filtering mechanism, we derive an optimal threshold strategy for filtering with the Pi marking. We also develop a new filter, the PiIP filter, which can be used to detect Internet protocol (IP) spoofing attacks with just a single attack packet. Finally, we discuss in detail StackPi's compatibility with IP fragmentation, applicability in an IPv6 environment, and several other important issues relating to potential deployment of StackPi

/pdf/stackpi-new-packet-marking-and-filtering-mechanisms-for-ddos-3k8mwosgj2.pdf

A. Yaar

Papers

Pi: a path identification mechanism to defend against DDoS attacks

FIT: fast Internet traceback

StackPi: New Packet Marking and Filtering Mechanisms for DDoS and IP Spoofing Defense