4,544 research outputs found
Detecting Botnets Using Hidden Markov Model, Profile Hidden Markov Model and Network Flow Analysis
Botnet is a network of infected computer systems called bots managed remotely by an attacker using bot controllers. Using distributed systems, botnets can be used for large-scale cyber attacks to execute unauthorized actions on the targeted system like phishing, distributed denial of service (DDoS), data theft, and crashing of servers. Common internet protocols used by normal systems for regular communication like hypertext transfer (HTTP) and internet relay chat (IRC) are also used by botnets. Thus, distinguishing botnet activity from normal activity can be challenging. To address this issue, this project proposes an approach to detect botnets using peculiar traits in the communication between command and control servers and bots. Patterns can be observed in botnet behavior like orchestrated attacks, heartbeat signals, or periodic distribution of commands. Hidden Markov Models (HMM) and Profile Hidden Markov Model (PHMM) are probabilistic models that can be trained on network traffic data to identify activity patterns that suggest botnet activity. In this project, HMM and PHMM are used to detect and classify botnets using publicly available datasets for real network data consisting of botnet traffic mixed with normal and background traffic. A comparative analysis of performance of HMM and PHMM is conducted in this project and the results show that HMM and PHMM can be useful in detecting botnets. PHMM outperforms HMM in terms of accuracy of botnet detection
Detecting Botnets Through Log Correlation
Botnets, which consist of thousands of compromised machines, can cause a significant threat to other systems by launching Distributed Denial of Service attacks, keylogging, and backdoors. In response to this threat, new effective techniques are needed to detect the presence of botnets. In this paper, we have used an interception technique to monitor Windows Application Programming Interface system calls made by communication applications. Existing approaches for botnet detection are based on finding bot traffic patterns. Our approach does not depend on finding patterns but rather monitors the change of behaviour in the system. In addition, we will present our idea of detecting botnet based on log correlations from different hosts
Flooding attacks to internet threat monitors (ITM): Modeling and counter measures using botnet and honeypots
The Internet Threat Monitoring (ITM),is a globally scoped Internet monitoring
system whose goal is to measure, detect, characterize, and track threats such
as distribute denial of service(DDoS) attacks and worms. To block the
monitoring system in the internet the attackers are targeted the ITM system. In
this paper we address flooding attack against ITM system in which the attacker
attempt to exhaust the network and ITM's resources, such as network bandwidth,
computing power, or operating system data structures by sending the malicious
traffic. We propose an information-theoretic frame work that models the
flooding attacks using Botnet on ITM. Based on this model we generalize the
flooding attacks and propose an effective attack detection using Honeypots
OnionBots: Subverting Privacy Infrastructure for Cyber Attacks
Over the last decade botnets survived by adopting a sequence of increasingly
sophisticated strategies to evade detection and take overs, and to monetize
their infrastructure. At the same time, the success of privacy infrastructures
such as Tor opened the door to illegal activities, including botnets,
ransomware, and a marketplace for drugs and contraband. We contend that the
next waves of botnets will extensively subvert privacy infrastructure and
cryptographic mechanisms. In this work we propose to preemptively investigate
the design and mitigation of such botnets. We first, introduce OnionBots, what
we believe will be the next generation of resilient, stealthy botnets.
OnionBots use privacy infrastructures for cyber attacks by completely
decoupling their operation from the infected host IP address and by carrying
traffic that does not leak information about its source, destination, and
nature. Such bots live symbiotically within the privacy infrastructures to
evade detection, measurement, scale estimation, observation, and in general all
IP-based current mitigation techniques. Furthermore, we show that with an
adequate self-healing network maintenance scheme, that is simple to implement,
OnionBots achieve a low diameter and a low degree and are robust to
partitioning under node deletions. We developed a mitigation technique, called
SOAP, that neutralizes the nodes of the basic OnionBots. We also outline and
discuss a set of techniques that can enable subsequent waves of Super
OnionBots. In light of the potential of such botnets, we believe that the
research community should proactively develop detection and mitigation methods
to thwart OnionBots, potentially making adjustments to privacy infrastructure.Comment: 12 pages, 8 figure
PeerHunter: Detecting Peer-to-Peer Botnets through Community Behavior Analysis
Peer-to-peer (P2P) botnets have become one of the major threats in network
security for serving as the infrastructure that responsible for various of
cyber-crimes. Though a few existing work claimed to detect traditional botnets
effectively, the problem of detecting P2P botnets involves more challenges. In
this paper, we present PeerHunter, a community behavior analysis based method,
which is capable of detecting botnets that communicate via a P2P structure.
PeerHunter starts from a P2P hosts detection component. Then, it uses mutual
contacts as the main feature to cluster bots into communities. Finally, it uses
community behavior analysis to detect potential botnet communities and further
identify bot candidates. Through extensive experiments with real and simulated
network traces, PeerHunter can achieve very high detection rate and low false
positives.Comment: 8 pages, 2 figures, 11 tables, 2017 IEEE Conference on Dependable and
Secure Computin
- …