Biblio

The botnet is a serious network security threat that can cause servers crash, so how to detect the behavior of Botnet has already become an important part of the research of network security. DNS(Domain Name System) request is the first step for most of the mainframe computers controlled by Botnet to communicate with the C&C(command; control) server. The detection of DNS request domain names is an important way for mainframe computers controlled by Botnet. However, the detection method based on fixed rules is hard to take effect for botnet based on DGA(Domain Generation Algorithm) because malicious domain names keep evolving and derive many different generation methods. Contrasted with the traditional methods, the method based on machine learning is a better way to detect it by learning and modeling the DGA. This paper presents a method based on the Naive Bayes model, the XGBoost model, the SVM(Support Vector Machine) model, and the MLP(Multi-Layer Perceptron) model, and tests it with real data sets collected from DGA, Alexa, and Secrepo. The experimental results show the precision score, the recall score, and the F1 score for each model.

The progress made in terms of controller technologies with the introduction of remotely-accessibility capacity in the digital controllers has opened the door to new cybersecurity threats on the Industrial Control Systems (ICSs). Among them, some aim at damaging the ICS's physical system. In this paper, a corrupted controller emitting a non-legitimate Pulse Width Modulation control signal to an Electro-Mechanical Actuator (EMA) is considered. The attacker's capabilities for accelerating the EMA's aging by inducing Partial Discharges (PDs) are investigated. A simplified model is considered for highlighting the influence of the carrier frequency of the control signal over the amplitude and the repetition of the PDs involved in the EMA's aging.

Zero dynamics attack is lethal to cyber-physical systems in the sense that it is stealthy and there is no way to detect it. Fortunately, if the given continuous-time physical system is of minimum phase, the effect of the attack is negligible even if it is not detected. However, the situation becomes unfavorable again if one uses digital control by sampling the sensor measurement and using the zero-order-hold for actuation because of the `sampling zeros.' When the continuous-time system has relative degree greater than two and the sampling period is small, the sampled-data system must have unstable zeros (even if the continuous-time system is of minimum phase), so that the cyber-physical system becomes vulnerable to `sampling zero dynamics attack.' In this paper, we begin with its demonstration by a few examples. Then, we present an idea to protect the system by allocating those discrete-time zeros into stable ones. This idea is realized by employing the so-called `generalized hold' which replaces the zero-order-hold.

The digital forensics refers to the application of scientific techniques in investigation of a crime, specifically to identify or validate involvement of some suspect in an activity leading towards that crime. Network forensics particularly deals with the monitoring of network traffic with an aim to trace some suspected activity from normal traffic or to identify some abnormal pattern in the traffic that may give clue towards some attack. Network forensics, quite valuable phenomenon in investigation process, presents certain challenges including problems in accessing network devices of cloud architecture, handling large amount network traffic, and rigorous processing required to analyse the huge volume of data, of which large proportion may prove to be irrelevant later on. Cloud Computing technology offers services to its clients remotely from a shared pool of resources, as per clients customized requirement, any time, from anywhere. Cloud Computing has attained tremendous popularity recently, leading to its vast and rapid deployment, however Privacy and Security concerns have also increased in same ratio, since data and application is outsourced to a third party. Security concerns about cloud architecture have come up as the prime barrier hindering the major shift of industry towards cloud model, despite significant advantages of cloud architecture. Cloud computing architecture presents aggravated and specific challenges in the network forensics. In this paper, I have reviewed challenges and issues faced in conducting network forensics particularly in the cloud computing environment. The study covers limitations that a network forensic expert may confront during investigation in cloud environment. I have categorized challenges presented to network forensics in cloud computing into various groups. Challenges in each group can be handled appropriately by either Forensic experts, Cloud service providers or Forensic tools whereas leftover challenges are declared as be- ond the control.