In recent years, machine learning-based cyber intrusion detection methods have gained increasing popularity. The number and complexity of new attacks continue to rise; therefore, effective intelligent solutions are necessary. Unsupervised learning techniques particularly appealing systems since they can detect known unknown types as well zero-day attacks. the current paper, we present an unsupe...

Network intrusion detection is an important research direction in the field of network security. The traditional algorithm based on feature extraction and separation, which has problems low accuracy high false alarm rate. In order to improve detection, this paper proposes model deep asymmetric convolutional encoder Random Forest(RF). First, use DACAE extract features from preprocessed data, the...

Wireless networks are increasingly overwhelmed by Distributed Denial of Service (DDoS) attacks by generating flooding packets that exhaust critical computing and communication resources of a victim’s mobile device within a very short period of time. This must be protected. Effective detection of DDoS attacks requires an adaptive learning classifier, with less computational complexity, and an ac...

Anomaly detection is one approach in intrusion detection systems (IDSs) which aims at capturing any deviation from the profiles of normal network activities. However, it suffers from high false alarm rate since it has impediment to distinguish the boundaries between normal and attack profiles. In this paper, we propose an effective anomaly detection approach by hybridizing three techniques, i.e...

Wireless Sensor Networks (WSNs) have become a key technology for the IoT and despite obvious benefits, challenges still exist regarding security. As more devices are connected to the internet, new cyber attacks are emerging which join well-known attacks posing significant threats to the confidentiality, integrity and availability of data in WSNs. In this work, we investigated two computational ...

Network security is a growing issue, with the evolution of computer systems and expansion of attacks. Biological systems have been inspiring scientists and designs for new adaptive solutions, such as genetic algorithms. In this paper, we present an approach that uses the genetic algorithm to generate anomaly network intrusion detectors. In this paper, an algorithm propose use a discretization m...

Intrusion detection system (IDS) is the system which identifies malicious activity on the network. As the Internet volume is expanding quickly, security against the constant assaults and their quick discovery issues pick up consideration of numerous specialists. Information mining techniques can be adequately connected to IDS to handle the issues of IDS. It can lessen the time intricacy by sele...

Data Security has become a very serious part of any organizational information system. Internet threats have become more intelligent so it can deceive the basic security solutions such as firewalls and antivirus scanners. To enhance the overall security of the network an additional security layer such as intrusion detection system (IDS) has to be added. The anomaly detection IDS is a type of ID...

Security issues have resulted in severe damage to the cloud computing environment, adversely affecting healthy and sustainable development of computing. Intrusion detection is one technologies for protecting environment from malicious attacks. However, network traffic characterized by large scale, high dimensionality, redundancy, these characteristics pose serious challenges intrusion systems. ...