Bayesian bot detection based on dns traffic similarity

ABSTRACT Bots often are detected by their communication with a command and control (C&C) infrastructure. To evade detection, botmasters are increasingly obfuscating C&C communications, e.g., by using fastflux or peer-to-peer protocols. However, commands tend to elicit similar actions in bots of a same botnet. We propose and evaluate a Bayesian approach for detecting bots based on the similarity of their DNS traffic to that of known bots. Experimental results and sensitivity analysis suggest that the proposed method is effective and robust

Detection of Behavioral Malware in Delay Tolerant Networks

Disruption-tolerant networking has gained currency in the United States due to support from DARPA, which has funded many DTN projects. Disruption may occur because of the limits of wireless radio range, sparsity of mobile nodes, energy resources, attack, and noise. The delay-tolerant-network (DTN) model is becoming a viable communication alternative to the traditional infrastructural model for modern mobile consumer electronics equipped with short-range communication technologies such as Bluetooth, NFC, and Wi-Fi Direct. Proximity malware is a class of malware that exploits the opportunistic contacts and distributed nature of DTNs for propagation. Behavioral characterization of malware is an effective alternative to pattern matching in detecting malware, especially when dealing with polymorphic or obfuscated malware. In this paper, we first propose a general behavioral characterization of proximity malware which based on Naive Bayesian model, which has been successfully applied in non-DTN settings such as filtering email spams and detecting bonnets. We identify two unique challenges for extending Bayesian malware detection to DTNs (“insufficient evidence vs. evidence collection risk” and “filtering false evidence sequentially and distributedly”), and propose a simple yet effective method, look-ahead, to address the challenges. Furthermore, we propose two extensions to look-ahead, dogmatic filtering and adaptive look-ahead, to address the challenge of “malicious nodes sharing false evidence”. Real mobile network traces are used to verify the effectiveness of the proposed methods

A P2P BOTNET VIRUS DETECTION SYSTEM BASED ON DATA-MINING ALGORITHMS

ABSTRACT A P2P botnet virus detection system based on data-mining algorithms is proposed in this study to detect the infected computers quickly using Bayes Classifier and Neural Network (NN) Classifier. The system can detect P2P botnet viruses in the early stage of infection and report to network managers to avoid further infection. The system adopts real-time flow identification techniques to detect traffic flows produced by P2P application programs and botnet viruses by comparing with the known flow patterns in the database. After trained by adjusting the system parameters using test samples, the experimental results show that the accuracy of Bayes Classifier is 95.78% and that of NN Classifier is 98.71% in detecting P2P botnet viruses and suspected flows to achieve the goal of infection control in a short time

An Effective Conversation-Based Botnet Detection Method

A botnet is one of the most grievous threats to network security since it can evolve into many attacks, such as Denial-of-Service (DoS), spam, and phishing. However, current detection methods are inefficient to identify unknown botnet. The high-speed network environment makes botnet detection more difficult. To solve these problems, we improve the progress of packet processing technologies such as New Application Programming Interface (NAPI) and zero copy and propose an efficient quasi-real-time intrusion detection system. Our work detects botnet using supervised machine learning approach under the high-speed network environment. Our contributions are summarized as follows: (1) Build a detection framework using PF_RING for sniffing and processing network traces to extract flow features dynamically. (2) Use random forest model to extract promising conversation features. (3) Analyze the performance of different classification algorithms. The proposed method is demonstrated by well-known CTU13 dataset and nonmalicious applications. The experimental results show our conversation-based detection approach can identify botnet with higher accuracy and lower false positive rate than flow-based approach

Имитационное моделирование механизмов защиты от бот-сетей.

To create effective mechanisms of protection against botnets, it is necessary to investigate the behavior of botnets and their impact on the operation of computer networks, as well as methods for botnet detection and counteraction to them. The paper investigates protection mechanisms against botnets, which are proliferated by worm propagation techniques and carry out DDoS attacks. As a toolkit to study of botnets and protect mechanisms we developed the simulation environment. The paper considers the architecture of the simulation environment implemented and a multitude of experiments which show ample opportunities of the developed simulation environment for research of botnets and protection mechanisms.Для создания эффективных механизмов защиты от бот-сетей необходимо исследовать поведение бот-сетей, их влияние на работу компьютерных сетей, а также методы детектирования бот-сетей и противодействия им. В данной статье исследуются механизмы защиты от бот-сетей, распространяющихся с помощью технологии компьютерных червей и выполняющих распределенные атаки типа «отказ в обслуживании». В качестве инструмента для исследования бот-сетей и механизмов защиты предлагается программно-инструментальная среда имитационного моделирования, разработанная авторами статьи. Описывается общая архитектура среды моделирования и представлены эксперименты, которые показывают возможности разработанной среды имитационного моделирования для исследования бот-сетей и механизмов защиты от них