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A Novel Approach for Detecting DGA-Based Botnets in DNS Queries Using Machine Learning Techniques
In today’s security landscape, advanced threats are becoming increasingly difficult to detect as the pattern of attacks expands. Classical approaches that rely heavily on static matching, such as blacklisting or regular expression patterns, may be limited in flexibility or uncertainty in detecting malicious data in system data. This is where machine learning techniques can show their value and provide new insights and higher detection rates. The behavior of botnets that use domain-flux techniques to hide command and control channels was investigated in this research. The machine learning algorithm and text mining used to analyze the network DNS protocol and identify botnets were also described. For this purpose, extracted and labeled domain name datasets containing healthy and infected DGA botnet data were used. Data preprocessing techniques based on a text-mining approach were applied to explore domain name strings with n-gram analysis and PCA. Its performance is improved by extracting statistical features by principal component analysis. The performance of the proposed model has been evaluated using different classifiers of machine learning algorithms such as decision tree, support vector machine, random forest, and logistic regression. Experimental results show that the random forest algorithm can be used effectively in botnet detection and has the best botnet detection accuracy.
Performance Analysis of Identifier Locator Communication Cache Effects on ILNPv6 Stack
Identifier-locator network protocol (ILNP) is a host-based identifier/locator split architecture scheme (ILSA), which depends on address rewriting to support end-to-end mobility and multihoming. The address rewriting is performed by hosts using a network layer logical cache that stores state information related to the communicated hosts, which is called identifier-locator communication cache (ILCC). Since address rewriting is executed on a packet basis in ILNP, ILCC lookups are required at each packet reception and transmission. This leads to a strong correlation between the host’s network stack performance and ILCC performance. This paper presents a study of the effect of ILCC size on network stack performance. Within this paper, a direct comparison of the performance of two ILNP prototypes that differ by ILCC management mechanism is conducted. We present ILCC size measurements and study their effects on the host’s network stack performance. The results show that ILCC growth caused by correspondents increase has a significant effect on the latency of both network and transport layers. The obtained results show that controlling ILCC size through an effective policy strongly enhances ILNP network stack performance.
An Efficient Data Analysis Framework for Online Security Processing
Industrial cloud security and internet of things security represent the most important research directions of cyberspace security. Most existing studies on traditional cloud data security analysis were focused on inspecting techniques for block storage data in the cloud. None of them consider the problem that multidimension online temp data analysis in the cloud may appear as continuous and rapid streams, and the scalable analysis rules are continuous online rules generated by deep learning models. To address this problem, in this paper we propose a new LCN-Index data security analysis framework for large scalable rules in the industrial cloud. LCN-Index uses the MapReduce computing paradigm to deploy large scale online data analysis rules: in the mapping stage, it divides each attribute into a batch of analysis predicate sets which are then deployed onto a mapping node using interval predicate index. In the reducing stage, it merges results from the mapping nodes using multiattribute hash index. By doing so, a stream tuple can be efficiently evaluated by going over the LCN-Index framework. Experiments demonstrate the utility of the proposed method.
: MCDM-Based Interest Forwarding and Cooperative Data Caching for Named Data Networking
Named data networking (NDN), as a specific architecture design of information-centric networking (ICN), has quickly became a promising candidate for future Internet architecture, where communications are driven by data names instead of IP addresses. To realize the NDN communication paradigm in the future Internet, two important features, stateful forwarding and in-network caching, have been proposed to cope with drawbacks of host-based communication protocols. The stateful forwarding is designed to maintain the state of pending Interest packets to guide Data packets back to requesting consumers, while the in-network caching is used to reduce both network traffic and data access delay to improve the overall performance of data access. However, the conventional stateful forwarding approach is not adaptive and responsive to diverse network conditions because it fails to consider multiple network metrics to make Interest forwarding decision. In addition, the default in-network caching strategy relies on storing each received Data packet regardless of various caching constraints and criteria, which causes the routers in the vicinity of data producers to suffer from excessive caching overhead. In this paper, we propose the , a novel stateful forwarding and in-network caching strategy for NDN networks. The consists of multicriteria decision-making (MCDM) based interest forwarding and cooperative data caching. The basic idea of the MCDM-based interest forwarding is to employ Technique for Order Performance by Similarity to Idea Solution (TOPSIS) to dynamically evaluate outgoing interface alternatives based on multiple network metrics and objectively select an optimal outgoing interface to forward the Interest packet. In addition, the cooperative data caching consists of two schemes: CacheData, which caches the data, and CacheFace, which caches the outgoing interface. We conduct extensive simulation experiments for performance evaluation and comparison with prior schemes. The simulation results show that the can improve Interest satisfaction ratio and Interest satisfaction latency as well as reduce hop count and Content Store utilization ratio.
Towards a Scalable and Adaptive Learning Approach for Network Intrusion Detection
This paper introduces a new integrated learning approach towards developing a new network intrusion detection model that is scalable and adaptive nature of learning. The approach can improve the existing trends and difficulties in intrusion detection. An integrated approach of machine learning with knowledge-based system is proposed for intrusion detection. While machine learning algorithm is used to construct a classifier model, knowledge-based system makes the model scalable and adaptive. It is empirically tested with NSL-KDD dataset of 40,558 total instances, by using ten-fold cross validation. Experimental result shows that 99.91% performance is registered after connection. Interestingly, significant knowledge rich learning for intrusion detection differs as a fundamental feature of intrusion detection and prevention techniques. Therefore, security experts are recommended to integrate intrusion detection in their network and computer systems, not only for well-being of their computer systems but also for the sake of improving their working process.
Scalable THz Network-On-Chip Architecture for Multichip Systems
While THz wireless network-on-chip (WiNoC) introduces considerably high bandwidth, due to the high path loss, it cannot be used for communication between far apart nodes, especially in a multichip architecture. In this paper, we introduce a cellular and scalable architecture to reuse the frequencies of the chips. Moreover, we use a novel structure called parallel-plate waveguide (PPW) that is suitable for interchip communication. The low-loss property of this waveguide lets us increase the number of chips. Each chip has a wireless node as a gateway for communicating with other chips. To shorten the length of intra- and interchip THz links, the optimum configuration is determined by leveraging the multiobjective simulating annealing (SA) algorithm. Finally, we compare the performance of the proposed THz multichip NoC with a conventional millimeter-wave one. Our simulation results indicate that when the system scales up from four to sixteen chips, the throughput of our design is decreased about , while for millimeter-wave NoC, this reduction is about . Furthermore, the average latency growth of our system is only compared with about increase for the millimeter-wave NoC.