In recent years, several existing techniques have been developed to solve security issues in cloud systems. The proposed study intends develop an effective deep-learning mechanism for detecting network intrusions. involves three stages pre-processing, feature selection and classification. Initially, the available noises input data are eliminated by pre-processing via cleaning, discretization no...

Automated rule induction procedures like machine learning and statistical techniques result in rules that lack generalization and maintainability. Developing rules manually through incorporation of attack signatures results in meaningful but weak rules as it is difficult to define thresholds. This paper utilizes a hybrid procedure for developing rules by combining signature analysis with automa...

Currently, signature based Intrusion Detection Systems (IDS) approaches are inadequate to address threats posed to networked systems by zero-day exploits. Statistical machine learning techniques offer a great opportunity to mitigate these threats. However, at this point, statistical based IDS systems are not mature enough to be implemented in realtime systems and the techniques to be used are n...

Abstract In recent years, due to the rapid growth in network technology, numerous types of intrusions have been uncovered that differ from existing ones, and conventional firewalls with specific rule sets policies are incapable identifying those real-time. Therefore, demands requirement a real-time intrusion detection system (RT-IDS). The ultimate purpose this research is construct an RT-IDS ca...

Malware detection plays a crucial role in cyber-security with the increase malware growth and advancements cyber-attacks. Malicious software applications, or malware, are primary source of many security problems. These intentionally manipulative malicious applications intend to perform unauthorized activities on behalf their originators host machines for various reasons such as stealing advance...

An incursion into the computer network or system in issue occurs whenever there is an attempt made to circumvent defences that are place. Training and examination two basic components make up intrusion detection (IDS) each one may be analysed separately. During training, a number of distinct models built, which able distinguish between normal abnormal behaviours included within dataset. This ar...

Unbalanced datasets are a common problem in supervised machine learning. It leads to deeper understanding of the majority classes Therefore, learning model is more effective at recognizing than minority classes. Naturally, imbalanced data, such as disease data and networking, has emerged real life. DDOS one network intrusions found happen often R2L. There an imbalance composition attacks Intrus...