4,165 research outputs found
Machine Learning DDoS Detection for Consumer Internet of Things Devices
An increasing number of Internet of Things (IoT) devices are connecting to
the Internet, yet many of these devices are fundamentally insecure, exposing
the Internet to a variety of attacks. Botnets such as Mirai have used insecure
consumer IoT devices to conduct distributed denial of service (DDoS) attacks on
critical Internet infrastructure. This motivates the development of new
techniques to automatically detect consumer IoT attack traffic. In this paper,
we demonstrate that using IoT-specific network behaviors (e.g. limited number
of endpoints and regular time intervals between packets) to inform feature
selection can result in high accuracy DDoS detection in IoT network traffic
with a variety of machine learning algorithms, including neural networks. These
results indicate that home gateway routers or other network middleboxes could
automatically detect local IoT device sources of DDoS attacks using low-cost
machine learning algorithms and traffic data that is flow-based and
protocol-agnostic.Comment: 7 pages, 3 figures, 3 tables, appears in the 2018 Workshop on Deep
Learning and Security (DLS '18
Run-time risk management in adaptive ICT systems
We will present results of the SERSCIS project related to risk management and mitigation strategies in adaptive multi-stakeholder ICT systems. The SERSCIS approach involves using semantic threat models to support automated design-time threat identification and mitigation analysis. The focus of this paper is the use of these models at run-time for automated threat detection and diagnosis. This is based on a combination of semantic reasoning and Bayesian inference applied to run-time system monitoring data. The resulting dynamic risk management approach is compared to a conventional ISO 27000 type approach, and validation test results presented from an Airport Collaborative Decision Making (A-CDM) scenario involving data exchange between multiple airport service providers
Knowledge-based Decision Making for Simulating Cyber Attack Behaviors
Computer networks are becoming more complex as the reliance on these network increases in this era of exponential technological growth. This makes the potential gains for criminal activity on these networks extremely serious and can not only devastate organizations or enterprises but also the general population. As complexity of the network increases so does the difficulty to protect the networks as more potential vulnerabilities are introduced. Despite best efforts, traditional defenses like Intrusion Detection Systems and penetration tests are rendered ineffective to even amateur cyber adversaries. Networks now need to be analyzed at all times to preemptively detect weaknesses which harbored a new research field called Cyber Threat Analytics. However, current techniques for cyber threat analytics typically perform static analysis on the network and system vulnerabilities but few address the most variable and most critical piece of the puzzle -- the attacker themselves.
This work focuses on defining a baseline framework for modeling a wide variety of cyber attack behaviors which can be used in conjunction with a cyber attack simulator to analyze the effects of individual or multiple attackers on a network. To model a cyber attacker\u27s behaviors with reasonable accuracy and flexibility, the model must be based on aspects of an attacker that are used in real scenarios. Real cyber attackers base their decisions on what they know and learn about the network, vulnerabilities, and targets. This attacker behavior model introduces the aspect of knowledge-based decision making to cyber attack behavior modeling with the goal of providing user configurable options. This behavior model employs Cyber Attack Kill Chain along with an ensemble of the attacker capabilities, opportunities, intent, and preferences. The proposed knowledge-based decision making model is implemented to enable the simulation of a variety of network attack behaviors and their effects. This thesis will show a number of simulated attack scenarios to demonstrate the capabilities and limitations of the proposed model
Smart Intrusion Detection System for DMZ
Prediction of network attacks and machine understandable security vulnerabilities are complex tasks for current available Intrusion Detection System [IDS]. IDS software is important for an enterprise network. It logs security information occurred in the network. In addition, IDSs are useful in recognizing malicious hack attempts, and protecting it without the need for change to
client‟s software. Several researches in the field of machine learning have been applied to make these IDSs better a d smarter. In our work, we propose approach for making IDSs more analytical, using semantic technology. We made a useful semantic connection between IDSs and National Vulnerability Databases [NVDs], to make the system semantically analyzed each attack logged, so it can perform prediction about incoming attacks or services that might be in danger. We built our ontology skeleton based on standard network security. Furthermore, we added useful classes and relations that are specific for DMZ network services. In addition, we made an option to mallow the user to update the ontology skeleton automatically according to the network needs. Our work is evaluated and validated using four different methods: we presented a prototype that works over the web. Also, we applied KDDCup99 dataset to the prototype. Furthermore,we modeled our system using queuing model, and simulated it using Anylogic simulator. Validating the system using KDDCup99 benchmark shows good results law false positive attacks prediction. Modeling the system in a queuing model allows us to predict the behavior of the system in a multi-users system for heavy network traffic
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