No abstract

The incessant destruction and harmful tendency of malware on mobile devices has made malware detection an indispensable continuous field of research. Different matching/mismatching approaches have been adopted in the detection of malware which includes anomaly detection technique, misuse detection, or hybrid detection technique. In order to improve the detection rate of malicious application on the Android platform, a novel knowledge-based database discovery model that improves apriori association rule mining of a priori algorithm with Particle Swarm Optimization (PSO) is proposed. Particle swarm optimization (PSO) is used to optimize the random generation of candidate detectors and parameters associated with apriori algorithm (AA) for features selection. In this method, the candidate detectors generated by particle swarm optimization form rules using apriori association rule. These rule models are used together with extraction algorithm to classify and detect malicious android application. Using a number of rule detectors, the true positive rate of detecting malicious code is maximized, while the false positive rate of wrongful detection is minimized. The results of the experiments show that the proposed a priori association rule with Particle Swarm Optimization model has remarkable improvement over the existing contemporary detection models.

The demand of network infrastructure and services is ever increasing. The network architecture and related technology must be flexible enough to accommodate the ever-growing number of users. Software-Defined Networking (SDN) is an approach of networking architecture that improvise conventional network in terms of scalability, security, and availability. At the same time, SDN is vulnerable to security threats as well. This paper studies on SDN architecture, the improvement of SDN from conventional network, the vulnerability and threats in SDN, and possible solutions to some security threats examples. It gives an overview of SDN and security-the architecture advantages that can be leveraged to secure network systems, and the security threats that may occur if improper design and deployment of SDN take place.

Clinicians could intervene during what may be a crucial stage for preventing permanent kidney injury if patients with incipient Acute Kidney Injury (AKI) and those at high risk of developing AKI could be identified. This paper proposes an improved mechanism to machine learning imputation algorithms by introducing the Particle Swarm Levy Flight algorithm. We improve the algorithms by modifying the Particle Swarm Optimization Algorithm (PSO), by enhancing the algorithm with levy flight (PSOLF). The creatinine dataset that we collected, including AKI diagnosis and staging, mortality at hospital discharge, and renal recovery, are tested and compared with other machine learning algorithms such as Genetic Algorithm and traditional PSO. The proposed algorithms' performances are validated with a statistical significance test. The results show that SVMPSOLF has better performance than the other method. This research could be useful as an important tool of prognostic capabilities for determining which patients are likely to suffer from AKI, potentially allowing clinicians to intervene before kidney damage manifests.

The advancement of wireless technology and mobile devices have change our life tremendously. The number of smartphone users increases and majority people rely on it for communication and business related matters. While smartphones are used for positive aspects of our life, it is also used by criminals as medium for their modus operandi.