Deep Learning-based Framework for Detecting Malicious Insider-Inspired Cyberattacks Activities in Organisations


  • Gibson Chengetanai BAC
  • Teandai R. Chandigere BAC
  • Pepukai Chengetanai
  • Rachna Verma



intrusion detection, deep learning, cyberattacks, malicious users


Abstract— Cyberattacks are happening at an alarming rate both in developed and developing countries. This is due to more users now being connected to the global village (internet). Significant strides have been taken by organisations to protect information technology assets together with data, by doing defense-in-depth, using firewalls and access control approaches collectively. These approaches work well in detecting attacks by outsider cyber-attackers. In recent cyberattacks the perpetrators have been those within the organisation, as they can easily bypass security measures especially those with high privileges and they can go undetected for quite a long time. We propose a deep learning approach termed Automatic_ IDS_ Deep model (framework) that is infused with intrusion detection systems to give timely detection of malicious activities by those within the organisation. Experiments were conducted and averaging of results was done to determine accuracy, recall, and precision of the proposed model. The model (framework) offers better results on its performance in detecting attacks that are perpetrated  within the organisation.


Author Biographies

Pepukai Chengetanai

Pepukai Chengetanai is a lecturer with GIPS college. Her research interest is on areas around cyber security and auditing in business. She is a holder of ACCA and Business studies degree.

Rachna Verma

Rachna has a BSc in Maths (Hons) and a Masters in Computer Applications. She started her career as a Systems Analyst but soon moved to lecturing. She has over 20 years of experience in teaching and learning. Her purpose is to inspire and motivate the young generation