Determining Viability of Deep Learning on Cybersecurity Log Analytics

Lorenzen, Casey; Agrawal, Rajeev; King, Jason

doi:10.1109/bigdata.2018.8622165

Cited by 10 publications

(4 citation statements)

References 11 publications

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“…By understanding these vulnerabilities, it is possible to develop better defenses against social engineering attacks. According to Symantec Security Response, just 4 % of cyber-attacks are brought on by technical flaws and software exploitation methods [5]. Although our findings show that some measures are being taken to protect social media data, the vast majority of the analyzed posts indicated a need for further improvement in data security.…”

Section: Introductionmentioning

confidence: 69%

The Use of AI to Analyze Social Media Attacks for Predictive Analytics

Adekunle,

Lawrence,

Alabi

et al. 2024

AJOMIS

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Social engineering, on the other hand, presents weaknesses that are difficult to directly quantify in penetration testing. The majority of expert social engineers utilize phishing and adware tactics to convince victims to provide information voluntarily. Social Engineering (SE) in social media has a similar structural layout to regular postings but has a malevolent intrinsic purpose. Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) was used to train a novel SE model to recognize covert SE threats in communications on social networks. The dataset includes a variety of posts, including text, images, and videos. It was compiled over a period of several months and was carefully curated to ensure that it is representative of the types of content that is typically posted on social media. First, by using domain heuristics, the social engineering assaults detection (SEAD) pipeline is intended to weed out social posts with malevolent intent. After tokenizing each social media post into sentences, each post is examined using a sentiment analyzer to determine whether it is a training data normal or an abnormality. Subsequently, an RNN-LSTM model is trained to detect five categories of social engineering assaults, some of which may involve information-gathering signals. Comparing the experimental findings to the ground truth labeled by network experts, the SEA model achieved 0.82 classification precision and 0.79 recall.

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Section: Introductionmentioning

confidence: 69%

The Use of AI to Analyze Social Media Attacks for Predictive Analytics

Adekunle,

Lawrence,

Alabi

et al. 2024

AJOMIS

View full text Add to dashboard Cite

show abstract

“…Understanding these vulnerabilities makes it possible to develop better defenses against social engineering attacks. According to Symantec Security Response, just 4% of cyber-attacks are brought on by technical flaws and software exploitation methods [6]. Although our findings show that some measures are being taken to protect social media data, the vast majority of the analyzed posts indicated a need for further improvement in data security.…”

Section: Introductionmentioning

confidence: 69%

The Use of AI to Analyze Social Media Attacks for Predictive Analytics

Adekunle,

Alabi,

Lawrence

et al. 2024

J. Comput. Theor. Appl.

View full text Add to dashboard Cite

Social engineering (SE) presents weaknesses that are difficult to quantify in penetration testing directly. The majority of expert social engineers utilize phishing and adware tactics to convince victims to provide information voluntarily. SE in social media has a similar structural layout to regular postings but has a malevolent intrinsic purpose. Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) was used to train a novel SE model to recognize covert SE threats in communications on social networks. The dataset includes various posts, including text, images, and videos. It was compiled over a period of several months. Then carefully curated to ensure that it is representative of the types of content that are typically posted on social media. First, using domain heuristics, the social engineering assaults detection (SEAD) pipeline is intended to weed out social posts with malevolent intent. After tokenizing each social media post into sentences, each post is examined using a sentiment analyzer to determine whether it is a training data normal or an abnormality. Subsequently, an RNN-LSTM model is trained to detect five categories of social engineering assaults, some of which may involve information-gathering signals. Thus, the proposed SEA model yielded a classification precision of 0.82 and a recall of 0.79.

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“…The Federal Energy Management Committee examines and publishes this email dataset to the network. In the research work (Lorenzen et al, 2018), data was collected from "Cybersecurity Environment for Detection, Analysis, and Reporting" (CEDAR).…”

Section: P Other Datasetsmentioning

confidence: 99%

Cyber Security Threats and Countermeasures using Machine and Deep Learning Approaches: A Survey

Manjula¹,

Venkatesh²,

Venugopal³

2023

Journal of Computer Science

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Recent advancements in e-business, e-healthcare, e-governance, and online digital transactions have brought valuable benefits. Unfortunately, it raises severe cyber-attacks. Cyberattacks disrupt normal operations, try to retrieve confidential information and defense secrets, and subvert the nation's defense systems and Internet-connected devices. Cyber security solutions are required to detect, analyze, defend against threats and protect sensitive data from unauthorized access. This study gives a detailed survey of different cybersecurity attacks, like Denial-of-service attacks, Botnet Evasion Attacks, Malware invasions, Spam and phishing invasion, Spoofing, Domain Generation algorithms, Probing attacks, R2L, and U2R attacks. This research review emphasizes Machine Learning and Deep Learning-based approaches to Cybersecurity problems. This study's key highlights are the research challenges, cybersecurity issues, cyber security domains, and tools for the Intrusion detection system. Data sets play a vital role in cybersecurity research; hence, Private and Publically available datasets are reviewed in this study. Various performance matrices are discussed in this survey which can be used to evaluate the effectiveness of cybersecurity solutions.

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Determining Viability of Deep Learning on Cybersecurity Log Analytics

Cited by 10 publications

References 11 publications

The Use of AI to Analyze Social Media Attacks for Predictive Analytics

The Use of AI to Analyze Social Media Attacks for Predictive Analytics

The Use of AI to Analyze Social Media Attacks for Predictive Analytics

Cyber Security Threats and Countermeasures using Machine and Deep Learning Approaches: A Survey

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