Biblio

With the advancement of network physical social system (npss), a large amount of data privacy has become the targets of hacker attacks. Due to the complex and changeable attack methods of hackers, network security threats are becoming increasingly severe. As an important type of active defense, honeypots use the npss as a carrier to ensure the security of npss. However, traditional honeynet structures are relatively fixed, and it is difficult to trap hackers in a targeted manner. To bridge this gap, this paper proposes a recommendation method for LSTM prediction trap components based on attention mechanism. Its characteristic lies in the ability to predict hackers' attack interest, which increases the active trapping ability of honeynets. The experimental results show that the proposed prediction method can quickly and effectively predict the attacking behavior of hackers and promptly provide the trapping components that hackers are interested in.

The extensive utilization of network by smart devices, computers and servers makes it vulnerable to malicious activities where intruders and attackers tends to violate system security policies and authenticity to slither essential information. Honeypots are designed to create a virtual trap against hackers. The trap is to attract intruders and gather information about attackers and attack features. Honeypots mimics as a computer application, billing systems, webpages and client server-based applications to understand attackers behavior by gathering attack features and common foot prints used by hackers to forge information. In this papers, authors analyse amazon web services honeypot (AWSH) data to determine geo-spatial distribution of targeted attacks originated from different locations. The categorization of attacks is made on the basis of internet protocols and frequency of attack occurrences worldwide.

This paper presents an experimental analysis of current Distributed Denial of Service attacks. Our analysis is based on real data collected by a honeynet system that was installed on an ISP edge router, for a four-month period. In the examined scenario, we identify and analyze malicious activities based on packets captured and analyzed by a network protocol sniffer and signature-based attack analysis tools. Our analysis shows that IoT-based DDoS attacks are one of the latest and most proliferating attack trends in network security. Based on the analysis of the attacks, we describe some mitigation techniques that can be applied at the providers' network to mitigate the trending attack vectors.

The Internet has gradually penetrated into the national economy, politics, culture, military, education and other fields. Due to its openness, interconnectivity and other characteristics, the Internet is vulnerable to all kinds of malicious attacks. The research uses a honeynet to collect attacker information, and proposes a network penetration recognition technology based on interactive behavior analysis. Using Sebek technology to capture the attacker's keystroke record, time series modeling of the keystroke sequences of the interaction behavior is proposed, using a Recurrent Neural Network. The attack recognition method is constructed by using Long Short-Term Memory that solves the problem of gradient disappearance, gradient explosion and long-term memory shortage in ordinary Recurrent Neural Network. Finally, the experiment verifies that the short-short time memory network has a high accuracy rate for the recognition of penetration attacks.

Malicious software or malware is one of the most significant dangers facing the Internet today. In the fight against malware, users depend on anti-malware and anti-virus products to proactively detect threats before damage is done. Those products rely on static signatures obtained through malware analysis. Unfortunately, malware authors are always one step ahead in avoiding detection. This research deals with dynamic malware analysis, which emphasizes on: how the malware will behave after execution, what changes to the operating system, registry and network communication take place. Dynamic analysis opens up the doors for automatic generation of anomaly and active signatures based on the new malware's behavior. The research includes a design of honeypot to capture new malware and a complete dynamic analysis laboratory setting. We propose a standard analysis methodology by preparing the analysis tools, then running the malicious samples in a controlled environment to investigate their behavior. We analyze 173 recent Phishing emails and 45 SPIM messages in search for potentially new malwares, we present two malware samples and their comprehensive dynamic analysis.

Honeynet is deployed to trap attackers and learn their behavior patterns and motivations. Conventional honeynet is implemented by dedicated hardware and software. It suffers from inflexibility, high CAPEX and OPEX. There have been several virtualized honeynet architectures to solve those problems. But they lack a standard operating environment and common architecture for dynamic scheduling and adaptive resource allocation. Software Defined Security (SDS) framework has a centralized control mechanism and intelligent decision making ability for different security functions. In this paper, we present a new intelligent honeynet architecture based on SDS framework. It implements security functions over Network Function Virtualization Infrastructure (NFVI). Under uniform and intelligent control, security functional modules can be dynamically deployed and collaborated to complete different tasks. It migrates resources according to the workloads of each honeypot and power off unused modules. Simulation results show that intelligent honeynet has a better performance in conserving resources and reducing energy consumption. The new architecture can fit the needs of future honeynet development and deployment.

A honeypot is a deception tool for enticing attackers to make efforts to compromise the electronic information systems of an organization. A honeypot can serve as an advanced security surveillance tool for use in minimizing the risks of attacks on information technology systems and networks. Honeypots are useful for providing valuable insights into potential system security loopholes. The current research investigated the effectiveness of the use of centralized system management technologies called Puppet and Virtual Machines in the implementation automated honeypots for intrusion detection, correction and prevention. A centralized logging system was used to collect information of the source address, country and timestamp of intrusions by attackers. The unique contributions of this research include: a demonstration how open source technologies is used to dynamically add or modify hacking incidences in a high-interaction honeynet system; a presentation of strategies for making honeypots more attractive for hackers to spend more time to provide hacking evidences; and an exhibition of algorithms for system and network intrusion prevention.

A honeypot is a deception tool for enticing attackers to make efforts to compromise the electronic information systems of an organization. A honeypot can serve as an advanced security surveillance tool for use in minimizing the risks of attacks on information technology systems and networks. Honeypots are useful for providing valuable insights into potential system security loopholes. The current research investigated the effectiveness of the use of centralized system management technologies called Puppet and Virtual Machines in the implementation automated honeypots for intrusion detection, correction and prevention. A centralized logging system was used to collect information of the source address, country and timestamp of intrusions by attackers. The unique contributions of this research include: a demonstration how open source technologies is used to dynamically add or modify hacking incidences in a high-interaction honeynet system; a presentation of strategies for making honeypots more attractive for hackers to spend more time to provide hacking evidences; and an exhibition of algorithms for system and network intrusion prevention.

Cyber Threat Intelligence (CTI) sharing facilitates a comprehensive understanding of adversary activity and enables enterprise networks to prioritize their cyber defense technologies. To that end, we introduce HogMap, a novel software-defined infrastructure that simplifies and incentivizes collaborative measurement and monitoring of cyber-threat activity. HogMap proposes to transform the cyber-threat monitoring landscape by integrating several novel SDN-enabled capabilities: (i) intelligent in-place filtering of malicious traffic, (ii) dynamic migration of interesting and extraordinary traffic and (iii) a software-defined marketplace where various parties can opportunistically subscribe to and publish cyber-threat intelligence services in a flexible manner. We present the architectural vision and summarize our preliminary experience in developing and operating an SDN-based HoneyGrid, which spans three enterprises and implements several of the enabling capabilities (e.g., traffic filtering, traffic forwarding and connection migration). We find that SDN technologies greatly simplify the design and deployment of such globally distributed and elastic HoneyGrids.

Honeynet is a collection of honeypots that are set up to attract as many attackers as possible to learn about their patterns, tactics, and behaviors. However, existing honeypots suffer from a variety of fingerprinting techniques, and the current honeynet architecture does not fully utilize features of residing honeypots due to its coarse-grained data control mechanisms. To address these challenges, we propose an SDN-based intelligent honeynet called HoneyMix. HoneyMix leverages the rich programmability of SDN to circumvent attackers' detection mechanisms and enables fine-grained data control for honeynet. To do this, HoneyMix simultaneously establishes multiple connections with a set of honeypots and selects the most desirable connection to inspire attackers to remain connected. In this paper, we present the HoneyMix architecture and a description of its core components.

A trap set to detect attempts at unauthorized use of information systems. But setting up these honeypots and keep these guzzling electricity 24X7 is rather expensive. Plus there is always a risk of a skillful hacker or a deadly malware may break through this and compromise the whole system. Honeypot name suggest, a pot that contents full of honey to allure beers, but in networks Scenario honeypot is valuable tool that helps to allure attackers. It helps to detect and analyze malicious activity over your network. However honeypots used for commercial organization do not share data and large honeypot gives read only data. We propose an Arm based device having all capability of honeypots to allure attackers. Current honeypots are based on large Network but we are trying to make s device which have the capabilities to establish in small network and cost effective. This research helps us to make a device based on arm board and CCFIS Software to allure attackers which is easy to install and cost effective. CCFIS Sensor helps us to Capture malware and Analysis the attack. In this we did reverse Engineering of honeypots to know about how it captures malware. During reverse engineering we know about pros and cons of honeypots that are mitigated in CCFIS Sensor. After Completion of device we compared honeypots and CCFIS Sensor to check the effectiveness of device.

Honey pots and honey nets are popular tools in the area of network security and network forensics. The deployment and usage of these tools are influenced by a number of technical and legal issues, which need to be carefully considered together. In this paper, we outline privacy issues of honey pots and honey nets with respect to technical aspects. The paper discusses the legal framework of privacy, legal ground to data processing, and data collection. The analysis of legal issues is based on EU law and is supported by discussions on privacy and related issues. This paper is one of the first papers which discuss in detail privacy issues of honey pots and honey nets in accordance with EU law.