Recently, botnets such as Mirai and Persirai targeted IoT devices on a large scale. We consider attacks by botnets on cyber-physical systems (CPS), which require advanced capabilities such as controlling the physical processes in real-time. Traditional botnets are not suitable for this goal mainly because they lack process control capabilities, are not optimized for low latency communication, and bots generally do not leverage local resources. We argue that such attacks would require cyber-physical botnets. A cyber-physical botnet needs coordinated and heterogeneous bots, capable of performing adversarial control strategies while subject to the constraints of the target CPS. In this work, we present CPSBot, a framework to build cyber-physical botnets. We present an example of a centralized CPSBot targeting a centrally controlled system and a decentralized CPSBot targeting a system distributed control. We implemented the former CPSBot using MQTT for the C&C channel and Modbus TCP as the target network protocol and we used it to launch several attacks on real and simulated Water Distribution. We evaluate our implementation with distributed reply and distributed impersonation attacks on a CPS, and show that malicious control with negligible latency is possible.
There are several interesting analysis of traditional IT botnets. @cite_28 , the authors managed to act as fake servers and collected information about the Torpig botnet. @cite_48 , the authors presented a system able to capture and track more than 100 unique IRC-based botnets to measure the percentage of malicious traffic attributed to those botnets on the Internet. @cite_43 , the authors proposed a botnet take-down analysis and recommendation system. However, none of those papers analyzes a cyber-physical botnet with suitable quantitative metrics such as latency and size of the packets.
@cite_28 @cite_48 @cite_43
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Botnets are a significant source of abusive messaging (spam, phishing, etc) and other types of malicious traffic. A promising approach to help mitigate botnet-generated traffic is signal analysis of transport-layer (i.e. TCP IP) characteristics, e.g. timing, packet reordering, congestion, and flow-control. Prior work [4] shows that machine learning analysis of such traffic features on an SMTP MTA can accurately differentiate between botnet and legitimate sources. We make two contributions toward the real-world deployment of such techniques: i) an architecture for real-time on-line operation; and ii) auto-learning of the unsupervised model across different environments without human labeling (i.e. training). We present a "SpamFlow" SpamAssassin plugin and the requisite auxiliary daemons to integrate transport-layer signal analysis with a popular open-source spam filter. Using our system, we detail results from a production deployment where our auto-learning technique achieves better than 95 percent accuracy, precision, and recall after reception of ≈ 1,000 emails. To date, techniques to counter cyber-attacks have predominantly been reactive; they focus on monitoring network traffic, detecting anomalies and cyber-attack traffic patterns, and, a posteriori, combating the cyber-attacks and mitigating their effects. Contrary to such approaches, we advocate proactively detecting and identifying botnets prior to their being used as part of a cyber-attack (, 2006). In this paper, we present our work on using machine learning-based classification techniques to identify the command and control (C2) traffic of IRC-based botnets - compromised hosts that are collectively commanded using Internet relay chat (IRC). We split this task into two stages: (I) distinguishing between IRC and non-IRC traffic, and (II) distinguishing between botnet and real IRC traffic. For stage I, we compare the performance of J48, naive Bayes, and Bayesian network classifiers, identify the features that achieve good overall classification accuracy, and determine the classification sensitivity to the training set size. While sensitive to the training data and the attributes used to characterize communication flows, machine learning-based classifiers show promise in identifying IRC traffic. Using classification in stage II is trickier, since accurately labeling IRC traffic as botnet and non-botnet is challenging. We are currently exploring labeling flows as suspicious and non-suspicious based on telltales of hosts being compromised Botnets play major roles in a vast number of threats to network security, such as DDoS attacks, generation of spam emails, information theft. Detecting Botnets is a difficult task in due to the complexity and performance issues when analyzing the huge amount of data from real large-scale networks. In major Botnet malware, the use of Domain Generation Algorithms allows to decrease possibility to be detected using white list - blacklist scheme and thus DGA Botnets have higher survival. This paper proposes a DGA Botnet detection scheme based on DNS traffic analysis which utilizes semantic measures such as entropy, meaning the level of the domain, frequency of n-gram appearances and Mahalanobis distance for domain classification. The proposed method is an improvement of Phoenix botnet detection mechanism, where in the classification phase, the modified Mahalanobis distance is used instead of the original for classification. The clustering phase is based on modified k-means algorithm for archiving better effectiveness. The effectiveness of the proposed method was measured and compared with Phoenix, Linguistic and SVM Light methods. The experimental results show the accuracy of proposed Botnet detection scheme ranges from 90 to 99,97 depending on Botnet type. The academic community has long acknowledged the existence of malicious botnets, however to date, very little is known about the behavior of these distributed computing platforms. To the best of our knowledge, botnet behavior has never been methodically studied, botnet prevalence on the Internet is mostly a mystery, and the botnet life cycle has yet to be modeled. Uncertainty abounds. In this paper, we attempt to clear the fog surrounding botnets by constructing a multifaceted and distributed measurement infrastructure. Throughout a period of more than three months, we used this infrastructure to track 192 unique IRC botnets of size ranging from a few hundred to several thousand infected end-hosts. Our results show that botnets represent a major contributor to unwanted Internet traffic - 27 of all malicious connection attempts observed from our distributed darknet can be directly attributed to botnet-related spreading activity. Furthermore, we discovered evidence of botnet infections in 11 of the 800,000 DNS domains we examined, indicating a high diversity among botnet victims. Taken as a whole, these results not only highlight the prominence of botnets, but also provide deep insights that may facilitate further research to curtail this phenomenon.