Keamanan Sistem Perangkat Lunak dengan Secure Software Development Lifecycle

Saat ini, pengembangan perangkat lunak lebih kompleks daripada sebelumnya di mana keamanan menjadi salah satu yang paling krusial. Masalah keamanan menjadi bagian penting untuk developer perangkat lunak.Kebutuhan keamanan dalam pengembangan perangkat lunak menghasilkanpenciptaan yang disebut Secure Software Development Life Cycle (SSDLC). Paper ini menyoroti kerentanan perangkat lunak dan pendekatan untuk mengatasinya. Untuk itu akan dibahas beberapa tool keamanan seperti OWASP dan ISSAF. Tujuannya agar dapat mengetahui sejauh mana tool-tool tersebut meminimalkan kerentanan dalam pengembangan perangkat lunak

Proactive controller assignment schemes in SDN for fast recovery

​© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A sizeable software defined network with a single controller responsible for all forwarding elements is potentially failure-prone and inadequate for dynamic network loads. To this end, having multiple controllers improves resilience and distributes network control overhead. However, when there is a disruption in the control plane, a rapid and performant controller-switch assignment is critical, which is a challenging technical question. In this work, we propose a proactive switch assignment approach in case of controller failures using a genetic algorithm based heuristic that considers controller load distribution, reassignment cost and probability of failure. Moreover, we compare the performance of our scheme with random and greedy algorithms. Experiment results show that our proposed PREFCP framework has better performance in terms of probability of failure and controller load distributio

Evaluation of Machine Learning Algorithms for Intrusion Detection System

Intrusion detection system (IDS) is one of the implemented solutions against harmful attacks. Furthermore, attackers always keep changing their tools and techniques. However, implementing an accepted IDS system is also a challenging task. In this paper, several experiments have been performed and evaluated to assess various machine learning classifiers based on KDD intrusion dataset. It succeeded to compute several performance metrics in order to evaluate the selected classifiers. The focus was on false negative and false positive performance metrics in order to enhance the detection rate of the intrusion detection system. The implemented experiments demonstrated that the decision table classifier achieved the lowest value of false negative while the random forest classifier has achieved the highest average accuracy rate