Computational Intelligence and Security 

About: Computational Intelligence and Security is an academic conference. The conference publishes majorly in the area(s): Feature extraction & Algorithm design. Over the lifetime, 4353 publications have been published by the conference receiving 27833 citations.

A detailed analysis of the KDD CUP 99 data set

Abstract: During the last decade, anomaly detection has attracted the attention of many researchers to overcome the weakness of signature-based IDSs in detecting novel attacks, and KDDCUP'99 is the mostly widely used data set for the evaluation of these systems. Having conducted a statistical analysis on this data set, we found two important issues which highly affects the performance of evaluated systems, and results in a very poor evaluation of anomaly detection approaches. To solve these issues, we have proposed a new data set, NSL-KDD, which consists of selected records of the complete KDD data set and does not suffer from any of mentioned shortcomings.

A Survey on the Internet of Things Security

Abstract: The security issues of the Internet of Things (IoT) are directly related to the wide application of its system. Beginning with introducing the architecture and features of IoT security, this paper expounds several security issues of IoT that exist in the three-layer system structure, and comes up with solutions to the issues above coupled with key technologies involved. Among these safety measures concerned, the ones about perception layer are particularly elaborated, including key management and algorithm, security routing protocol, data fusion technology, as well as authentication and access control, etc.

Kernel-based Behavior Analysis for Android Malware Detection

Abstract: The most major threat of Android users is malware infection via Android application markets. In case of the Android Market, as security inspections are not applied for many users have uploaded applications. Therefore, malwares, e.g., Geimini and Droid Dream will attempt to leak personal information, getting root privilege, and abuse functions of the smart phone. An audit framework called log cat is implemented on the Dalvik virtual machine to monitor the application behavior. However, only the limited events are dumped, because an application developers use the log cat for debugging. The behavior monitoring framework that can audit all activities of applications is important for security inspections on the market places. In this paper, we propose a kernel-base behavior analysis for android malware inspection. The system consists of a log collector in the Linux layer and a log analysis application. The log collector records all system calls and filters events with the target application. The log analyzer matches activities with signatures described by regular expressions to detect a malicious activity. Here, signatures of information leakage are automatically generated using the smart phone IDs, e.g., phone number, SIM serial number, and Gmail accounts. We implement a prototype system and evaluate 230 applications in total. The result shows that our system can effectively detect malicious behaviors of the unknown applications.

Self-adaptive differential evolution

Abstract: Differential Evolution (DE) is generally considered as a reliable, accurate, robust and fast optimization technique. DE has been successfully applied to solve a wide range of numerical optimization problems. However, the user is required to set the values of the control parameters of DE for each problem. Such parameter tuning is a time consuming task. In this paper, a self-adaptive DE (SDE) is proposed where parameter tuning is not required. The performance of SDE is investigated and compared with other versions of DE. The experiments conducted show that SDE outperformed the other DE versions in all the benchmark functions.

A Revised Discrete Particle Swarm Optimization for Cloud Workflow Scheduling

Abstract: A cloud workflow system is a type of platform service which facilitates the automation of distributed applications based on the novel cloud infrastructure. Compared with grid environment, data transfer is a big overhead for cloud workflows due to the market-oriented business model in the cloud environments. In this paper, a Revised Discrete Particle Swarm Optimization (RDPSO) is proposed to schedule applications among cloud services that takes both data transmission cost and computation cost into account. Experiment is conducted with a set of workflow applications by varying their data communication costs and computation costs according to a cloud price model. Comparison is made on make span and cost optimization ratio and the cost savings with RDPSO, the standard PSO and BRS (Best Resource Selection) algorithm. Experimental results show that the proposed RDPSO algorithm can achieve much more cost savings and better performance on make span and cost optimization.