Despite continuous defense efforts, DDoS attacks are still very prevalent on the Internet. In such arms races, attackers are becoming more agile and their strategies are more sophisticated to escape from detection. Effective defenses demand in-depth understanding of such strategies. In this paper, we set to investigate the DDoS landscape from the perspective of the attackers. We focus on the dynamics of the attacking force, aiming to explore the strategies behind the scenes, if any. Our study is based on 50,704 different Internet DDoS attacks across the globe in a seven-month period. Our results indicate that attackers deliberately schedule their controlled bots in a dynamic fashion, and such dynamics can be well captured by statistical distributions. Furthermore, different botnet families exhibit similar scheduling patterns, strongly suggesting their close relationship and potential collaborations. Such collaborations are further confirmed by bots rotating in multiple families, and such rotation patterns are examined and confirmed at various levels. These findings lay a promising foundation for predicting DDoS attacks in the future and aid mitigation efforts.

Internet distributed denial of service (DDoS) attacks are prevalent but hard to defend against, partially due to the volatility of the attacking methods and patterns used by attackers. Understanding the latest DDoS attacks can provide new insights for effective defense. But most of existing understandings are based on indirect traffic measures (e.g., backscatters) or traffic seen locally. In this paper, we present an in-depth analysis based on 50 704 different Internet DDoS attacks directly observed in a seven-month period. These attacks were launched by 674 botnets from 23 different botnet families with a total of 9026 victim IPs belonging to 1074 organizations in 186 countries. Our analysis reveals several interesting findings about today's Internet DDoS attacks. Some highlights include: 1) geolocation analysis shows that the geospatial distribution of the attacking sources follows certain patterns, which enables very accurate source prediction of future attacks for most active botnet families; 2) from the target perspective, multiple attacks to the same target also exhibit strong patterns of inter-attack time interval, allowing accurate start time prediction of the next anticipated attacks from certain botnet families; and 3) there is a trend for different botnets to launch DDoS attacks targeting the same victim, simultaneously or in turn. These findings add to the existing literature on the understanding of today's Internet DDoS attacks and offer new insights for designing new defense schemes at different levels.

Today, botnets are still responsible for most large scale attacks on the Internet. Botnets are versatile, they remain the most powerful attack platform by constantly and continuously adopting new techniques and strategies in the arms race against various detection schemes. Thus, it is essential to understand the latest of the botnets in a timely manner so that the insights can be utilized in developing more efficient defenses. In this work, we conduct a measurement study on some of the most active botnets on the Internet based on a public dataset collected over a period of seven months by a monitoring entity. We first examine and compare the attacking capabilities of different families of today's active botnets. Our analysis clearly shows that different botnets start to collaborate when launching DDoS attacks.

Internet Distributed Denial of Service (DDoS) at- tacks are prevalent but hard to defend against, partially due to the volatility of the attacking methods and patterns used by attackers. Understanding the latest DDoS attacks can provide new insights for effective defense. But most of existing understandings are based on indirect traffic measures (e.g., backscatters) or traffic seen locally. In this study, we present an in-depth analysis based on 50,704 different Internet DDoS attacks directly observed in a seven-month period. These attacks were launched by 674 botnets from 23 different botnet families with a total of 9,026 victim IPs belonging to 1,074 organizations in 186 countries. Our analysis reveals several interesting findings about today's Internet DDoS attacks. Some highlights include: (1) geolocation analysis shows that the geospatial distribution of the attacking sources follows certain patterns, which enables very accurate source prediction of future attacks for most active botnet families, (2) from the target perspective, multiple attacks to the same target also exhibit strong patterns of inter-attack time interval, allowing accurate start time prediction of the next anticipated attacks from certain botnet families, (3) there is a trend for different botnets to launch DDoS attacks targeting the same victim, simultaneously or in turn. These findings add to the existing literature on the understanding of today's Internet DDoS attacks, and offer new insights for designing new defense schemes at different levels.

Today botnets are responsible for most of the DDoS attacks on the Internet. Understanding the characteristics of such DDoS attacks is critical to develop effective DDoS mitigation schemes. In this poster, we present some preliminary findings, mainly concerning the distribution of the attackers, of today's DDoS attacks. Our investigation is based on 50,704 different Internet DDoS attacks collected within a seven-month period for activities across the globe. These attacks were launched by 674 botnet generations from 23 different bonet families with a total of 9026 victim IPs belonging to 1074 organizations that are collectively located in 186 countries. We find that different from the traditional widely distributed intuition, most of these DDoS attacks are not widely distributed as the attackers are mostly from the same region, i.e., highly regionalized. We also find that different botnet families have strong target preferences in the same area as well. These findings refresh our understanding on the modern DDoS attacks.

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Malwares are becoming increasingly stealthy, more and more malwares are using cryptographic algorithms (e.g., packing, encrypting C&C communication) to protect themselves from being analyzed. The use of cryptographic algorithms and truly transient cryptographic secrets inside the malware binary imposes a key obstacle to effective malware analysis and defense. To enable more effective malware analysis, forensics, and reverse engineering, we have developed CipherXRay - a novel binary analysis framework that can automatically identify and recover the cryptographic operations and transient secrets from the execution of potentially obfuscated binary executables. Based on the avalanche effect of cryptographic functions, CipherXRay is able to accurately pinpoint the boundary of cryptographic operation and recover truly transient cryptographic secrets that only exist in memory for one instant in between multiple nested cryptographic operations. CipherXRay can further identify certain operation modes (e.g., ECB, CBC, CFB) of the identified block cipher and tell whether the identified block cipher operation is encryption or decryption in certain cases. We have empirically validated CipherXRay with OpenSSL, popular password safe KeePassX, the ciphers used by malware Stuxnet, Kraken and Agobot, and a number of third party softwares with built-in compression and checksum. CipherXRay is able to identify various cryptographic operations and recover cryptographic secrets that exist in memory for only a few microseconds. Our results demonstrate that current software implementations of cryptographic algorithms hardly achieve any secrecy if their execution can be monitored.

Reuse signature, or reuse distance pattern, is an accurate model for program memory accessing behaviors. It has been studied and shown to be effective in program analysis and optimizations by many recent works. However, the high overhead associated with reuse distance measurement restricts the scope of its application. This paper explores applying sampling in reuse signature collection to reduce the time overhead. We compare different sampling strategies and show that an enhanced systematic sampling with a uniform coverage of all distance ranges can be used to extrapolate the reuse distance distribution. Based on that analysis, we present a novel sampling method with a measurement accuracy of more than 99%. Our average speedup of reuse signature collection is 7.5 while the best improvement observed is 34. This is the first attempt to utilize sampling in measuring reuse signatures. Experiments with varied programs and instrumentation tools show that sampling has great potential in promoting the practical uses of reuse signatures and enabling more optimization opportunities.