Botnet attacks Research Papers - Academia.edu (original) (raw)

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- Information Security, Collaborative Networks, Botnet attacks, Computer Network

–The Spam Emails are regularly causing huge losses to business on a regular basis. The Spam filtering is an automated technique to identity SPAM and HAM (Non-Spam). The Web Spam filters can be categorized as: Content based spam filters and List based spam filters. In this research work, we have studied the spam statistics of a famous Spambot 'Srizbi'. We have also discussed different approaches for Spam Filtering and finally proposed a new algorithm which is made on the basis of behavioral approaches of Spammers and to restrict the budding economical growth of Spam generating company's. We have used the hidden Honeypot and a Honeytrap module to minimize the spam generated from Contact and Feedback forms on public and social networking CMS websites.

- by Dr. Manish Saxena
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- SPAM, Botnet attacks, Botnets, Anti spam
- by Armando Marques-Guedes
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- History, Cultural History, Economic History, Sociology

The corporation is currently operating in a hyper-connected world in which scores of heterogeneous devices are constantly sharing information in a variety of application contexts such as wellness, improved communications, digital companies, and so on. However, in this case, the wider the genuine wide range of devices and connections, the greater the risk of security risks. Network Intrusion Detection Systems (NIDSs) will be the most popular line of defence in communications networks to combat malicious behaviour and preserve important security services. Nonetheless, there is no standard process for evaluating and comparing NIDSs. Almost all of the ideas fail to disclose critical NIDS validation procedures, making comparison difficult, if not impossible. In this research, an optimization-based method for detecting Botnet attacks in IoT environments is proposed. Botnet detection based on the Genetic Algorithm is proposed, with dynamic thresholds depending on the GA.

- by IAEME Publication
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- Genetic Algorithms, Internet of Things, Optimisation techniques, Botnet attacks

Detecting and classifying new malicious network traffic is a high priority concern for cybersecurity practitioners. New stealth or zero-day attack can make companies go out of businesses in the digital transformation era. Despite the plethora of studies that have explored different machine-learning (ML) techniques to address this issue, the most popular used approach remains traditional ML with legacy datasets and small campus network. The difficulty in data collection considers the biggest impediment of using ML. This paper examines the possibility of exposing zero-day malicious network traffic in large campus networks based on cloud environments by presenting a lightweight framework. An experiment was devised for the analysis. However, before that, the characteristics of the network were examined based on the flow level. The framework showed an outperformed accuracy rate of 100% for a specific type of attack and 97.97% as a comprehensive detection mechanism.

- by Mohammad Al-Fawa'reh
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- Botnet attacks, DDOS Attack, Stealthy attacks, Web Attacks

Over the past decade, the digitization of services transformed the healthcare sector leading to a sharp rise in cybersecurity threats. Poor cybersecurity in the healthcare sector, coupled with high value of patient records attracted the attention of hackers. Sophisticated advanced persistent threats and malware have significantly contributed to increasing risks to the health sector. Many recent attacks are attributed to the spread of malicious software, e.g., ransomware or bot malware. Machines infected with bot malware can be used as tools for remote attack or even cryptomining. This paper presents a novel approach, called BotDet, for botnet Command and Control (C&C) traffic detection to defend against malware attacks in critical ultrastructure systems. There are two stages in the development of the proposed system: 1) we have developed four detection modules to detect different possible techniques used in botnet C&C communications and 2) we have designed a correlation framework to reduce the rate of false alarms raised by individual detection modules. Evaluation results show that BotDet balances the true positive rate and the false positive rate with 82.3% and 13.6%, respectively. Furthermore, it proves BotDet capability of real time detection. INDEX TERMS Critical infrastructure security, healthcare cyber attacks, malware, botnet, command and control server, intrusion detection system, alert correlation.

- by Sardar Jaf
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- Intrusion Detection Systems, Malware Analysis, Botnet attacks, Malware

Even though promising results have been obtained from existing research on bots and associated command and control channels, there is little research in exploring the ways on how bots are created and distributed by adversaries. Consequently, innovative methods that help determine the linkage between the rogue programs and adversaries are imperative for mitigating and combating botnet attacks. Recent study discovers that rogue programs are sold in black markets in online social networks and adversaries use online social networks to coordinate attacks. Correlation of botnet attacks and activities in online underground social networks is crucial to tactically cope with net-centric threats. In this paper, we take the first step toward adversarial behavior identification by modeling social dynamics of underground adversarial communities and tracing the origin of certain malwares and attack events in underground communities. We also describe our evaluation to demonstrate the effectiveness of our approach.

- by Ziming Zhao
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- Social Networks, Cybercrimes, Network Analysis, Strategic Communication