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2021-03-04
Nugraha, B., Nambiar, A., Bauschert, T..  2020.  Performance Evaluation of Botnet Detection using Deep Learning Techniques. 2020 11th International Conference on Network of the Future (NoF). :141—149.

Botnets are one of the major threats on the Internet. They are used for malicious activities to compromise the basic network security goals, namely Confidentiality, Integrity, and Availability. For reliable botnet detection and defense, deep learning-based approaches were recently proposed. In this paper, four different deep learning models, namely Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), hybrid CNN-LSTM, and Multi-layer Perception (MLP) are applied for botnet detection and simulation studies are carried out using the CTU-13 botnet traffic dataset. We use several performance metrics such as accuracy, sensitivity, specificity, precision, and F1 score to evaluate the performance of each model on classifying both known and unknown (zero-day) botnet traffic patterns. The results show that our deep learning models can accurately and reliably detect both known and unknown botnet traffic, and show better performance than other deep learning models.

2020-05-08
Ming, Liang, Zhao, Gang, Huang, Minhuan, Kuang, Xiaohui, Li, Hu, Zhang, Ming.  2018.  Security Analysis of Intelligent Transportation Systems Based on Simulation Data. 2018 1st International Conference on Data Intelligence and Security (ICDIS). :184—187.

Modern vehicles in Intelligent Transportation Systems (ITS) can communicate with each other as well as roadside infrastructure units (RSUs) in order to increase transportation efficiency and road safety. For example, there are techniques to alert drivers in advance about traffic incidents and to help them avoid congestion. Threats to these systems, on the other hand, can limit the benefits of these technologies. Securing ITS itself is an important concern in ITS design and implementation. In this paper, we provide a security model of ITS which extends the classic layered network security model with transportation security and information security, and gives a reference for designing ITS architectures. Based on this security model, we also present a classification of ITS threats for defense. Finally a proof-of-concept example with malicious nodes in an ITS system is also given to demonstrate the impact of attacks. We analyzed the threat of malicious nodes and their effects to commuters, like increasing toll fees, travel distances, and travel times etc. Experimental results from simulations based on Veins shows the threats will bring about 43.40% more total toll fees, 39.45% longer travel distances, and 63.10% more travel times.

2019-10-22
Khelf, Roumaissa, Ghoualmi-Zine, Nacira.  2018.  IPsec/Firewall Security Policy Analysis: A Survey. 2018 International Conference on Signal, Image, Vision and their Applications (SIVA). :1–7.
As the technology reliance increases, computer networks are getting bigger and larger and so are threats and attacks. Therefore Network security becomes a major concern during this last decade. Network Security requires a combination of hardware devices and software applications. Namely, Firewalls and IPsec gateways are two technologies that provide network security protection and repose on security policies which are maintained to ensure traffic control and network safety. Nevertheless, security policy misconfigurations and inconsistency between the policy's rules produce errors and conflicts, which are often very hard to detect and consequently cause security holes and compromise the entire system functionality. In This paper, we review the related approaches which have been proposed for security policy management along with surveying the literature for conflicts detection and resolution techniques. This work highlights the advantages and limitations of the proposed solutions for security policy verification in IPsec and Firewalls and gives an overall comparison and classification of the existing approaches.
2018-06-11
Cai, Y., Huang, H., Cai, H., Qi, Y..  2017.  A K-nearest neighbor locally search regression algorithm for short-term traffic flow forecasting. 2017 9th International Conference on Modelling, Identification and Control (ICMIC). :624–629.

Accurate short-term traffic flow forecasting is of great significance for real-time traffic control, guidance and management. The k-nearest neighbor (k-NN) model is a classic data-driven method which is relatively effective yet simple to implement for short-term traffic flow forecasting. For conventional prediction mechanism of k-NN model, the k nearest neighbors' outputs weighted by similarities between the current traffic flow vector and historical traffic flow vectors is directly used to generate prediction values, so that the prediction results are always not ideal. It is observed that there are always some outliers in k nearest neighbors' outputs, which may have a bad influences on the prediction value, and the local similarities between current traffic flow and historical traffic flows at the current sampling period should have a greater relevant to the prediction value. In this paper, we focus on improving the prediction mechanism of k-NN model and proposed a k-nearest neighbor locally search regression algorithm (k-LSR). The k-LSR algorithm can use locally search strategy to search for optimal nearest neighbors' outputs and use optimal nearest neighbors' outputs weighted by local similarities to forecast short-term traffic flow so as to improve the prediction mechanism of k-NN model. The proposed algorithm is tested on the actual data and compared with other algorithms in performance. We use the root mean squared error (RMSE) as the evaluation indicator. The comparison results show that the k-LSR algorithm is more successful than the k-NN and k-nearest neighbor locally weighted regression algorithm (k-LWR) in forecasting short-term traffic flow, and which prove the superiority and good practicability of the proposed algorithm.

2018-05-09
Lokananta, F., Hartono, D., Tang, C. M..  2017.  A Scalable and Reconfigurable Verification and Benchmark Environment for Network on Chip Architecture. 2017 4th International Conference on New Media Studies (CONMEDIA). :6–10.

To reduce the complex communication problem that arise as the number of on-chip component increases, the use of Network-on-Chip (NoC) as interconnection architectures have become more promising to solve complex on-chip communication problems. However, providing a suitable test base to measure and verify functionality of any NoC is a compulsory. Universal Verification Methodology (UVM) is introduced as a standardized and reusable methodology for verifying integrated circuit design. In this research, a scalable and reconfigurable verification and benchmark environment for NoC is proposed.

2015-04-30
Foroushani, V.A., Zincir-Heywood, A.N..  2014.  TDFA: Traceback-Based Defense against DDoS Flooding Attacks. Advanced Information Networking and Applications (AINA), 2014 IEEE 28th International Conference on. :597-604.

Distributed Denial of Service (DDoS) attacks are one of the challenging network security problems to address. The existing defense mechanisms against DDoS attacks usually filter the attack traffic at the victim side. The problem is exacerbated when there are spoofed IP addresses in the attack packets. In this case, even if the attacking traffic can be filtered by the victim, the attacker may reach the goal of blocking the access to the victim by consuming the computing resources or by consuming a big portion of the bandwidth to the victim. This paper proposes a Trace back-based Defense against DDoS Flooding Attacks (TDFA) approach to counter this problem. TDFA consists of three main components: Detection, Trace back, and Traffic Control. In this approach, the goal is to place the packet filtering as close to the attack source as possible. In doing so, the traffic control component at the victim side aims to set up a limit on the packet forwarding rate to the victim. This mechanism effectively reduces the rate of forwarding the attack packets and therefore improves the throughput of the legitimate traffic. Our results based on real world data sets show that TDFA is effective to reduce the attack traffic and to defend the quality of service for the legitimate traffic.