The Experts below are selected from a list of 69 Experts worldwide ranked by ideXlab platform

Mohamed Rida - One of the best experts on this subject based on the ideXlab platform.

  • Novel Framework Based on Genetic Algorithm and Simulated Annealing Algorithm for Optimization of BP Neural Network Applied to Network IDS
    Proceedings of the 3rd International Conference on Smart City Applications - SCA '18, 2018
    Co-Authors: Zouhair Chiba, Noreddine Abghour, Khalid Moussaid, Amina El Omri, Mohamed Rida
    Abstract:

    Nowadays, network security is a world hot topic in computer security and defense. Intrusions, attacks or anomalies in network infrastructures lead mostly in great financial losses, massive sensitive data leaks, thereby decreasing efficiency and the quality of productivity of an Organization. Network Intrusion Detection System (NIDS) is an effective countermeasure and high-profile method to detect the unauthorized use of computer network and to provide the security for information. Thus, the presence of NIDS in an Organization plays a vital part in attack mitigation, and it has become an integral part of a Secure Organization. In this paper, we propose to optimize a very popular soft computing tool widely used for intrusion detection namely Back Propagation Neural Network (BPNN) using a novel hybrid Framework (GASAA) based on Genetic Algorithm (GA) and Simulated Annealing Algorithm (SAA). Experimental results on KDD CUP' 99 dataset show that our optimized ANIDS (Anomaly NIDS) based BPNN, called "ANIDS BPNN-GASAA" outperforms the original ANIDS BPNN, ANIDS BPNN optimized by using only GA and several traditional and new techniques in terms of detection rate and false positive rate, and it is very much suitable for network anomaly detection.

  • A New Hybrid Framework Based on Improved Genetic Algorithm and Simulated Annealing Algorithm for Optimization of Network IDS Based on BP Neural Network
    Innovations in Smart Cities Applications Edition 2, 2018
    Co-Authors: Zouhair Chiba, Noreddine Abghour, Khalid Moussaid, Amina El Omri, Mohamed Rida
    Abstract:

    Nowadays, network security is a world hot topic in computer security and defense. Intrusions, attacks or anomalies in network infrastructures lead mostly in great financial losses, massive sensitive data leaks, thereby decreasing efficiency and the quality of productivity of an Organization. Network Intrusion Detection System (NIDS) is an effective countermeasure and high-profile method to detect the unauthorized use of computer network and to provide the security for information. Thus, the presence of NIDS in an Organization plays a vital part in attack mitigation, and it has become an integral part of a Secure Organization. In this chapter, we propose to optimize a very popular soft computing tool widely used for intrusion detection namely, Back Propagation Neural Network (BPNN) using a novel hybrid framework (IGASAA) based on Improved Genetic Algorithm (IGA) and Simulated Annealing Algorithm (SAA). Genetic Algorithm (GA) is improved through optimization strategies, namely Parallel Processing and Fitness Value Hashing, which reduce execution time, convergence time and save processing power. Experimental results on KDD CUP’99 dataset show that our optimized ANIDS (Anomaly NIDS) based BPNN, called “ANIDS BPNN-IGASAA” outperforms the original ANIDS BPNN, ANIDS BPNN optimized by using only GA and several traditional and new techniques in terms of detection rate, false positive rate and it is very much appropriate for network anomaly detection.

  • A Hybrid Optimization Framework Based on Genetic Algorithm and Simulated Annealing Algorithm to Enhance Performance of Anomaly Network Intrusion Detection System Based on BP Neural Network
    2018 International Symposium on Advanced Electrical and Communication Technologies (ISAECT), 2018
    Co-Authors: Zouhair Chiba, Noreddine Abghour, Khalid Moussaid, Amina El Omri, Mohamed Rida
    Abstract:

    Today, network security is a world hot topic in computer security and defense. Intrusions and attacks in network infrastructures lead mostly in huge financial losses, massive sensitive data leaks, thus decreasing efficiency, competitiveness and the quality of productivity of an Organization. Network Intrusion Detection System (NIDS) is valuable tool for the defense-in-depth of computer networks. It is widely deployed in network architectures in order to monitor, to detect and eventually respond to any anomalous behavior and misuse which can threat confidentiality, integrity and availability of network resources and services. Thus, the presence of NIDS in an Organization plays a vital part in attack mitigation, and it has become an integral part of a Secure Organization. In this paper, we propose to optimize a very popular soft computing tool widely used for intrusion detection namely Back Propagation Neural Network (BPNN) using a novel hybrid Framework (GASAA) based on improved Genetic Algorithm (GA) and Simulated Annealing Algorithm (SAA). GA is improved through an optimization strategy, namely Fitness Value Hashing (FVH), which reduce execution time, convergence time and save processing power. Experimental results on KDD CUP' 99 dataset show that our optimized ANIDS (Anomaly NIDS) based BPNN, called “ANIDS BPNN-GASAA” outperforms several state-of-art approaches in terms of detection rate and false positive rate. In addition, improvement of GA through FVH has saved processing power and execution time. Thereby, our proposed IDS is very much suitable for network anomaly detection.

John Mallery - One of the best experts on this subject based on the ideXlab platform.

  • building a Secure Organization
    Computer and Information Security Handbook (Second Edition), 2013
    Co-Authors: John Mallery
    Abstract:

    This chapter provides guidelines for building effective security assessment plans and a comprehensive set of procedures for assessing the effectiveness of security controls employed in information systems. Today’s information systems are complex assemblages of technology (hardware, software, and firmware), processes, and people, working together to provide Organizations with the capability to process, store, and transmit information in a timely manner to support various missions and business functions. The degree to which Organizations have come to depend on these information systems to conduct routine, important, and critical missions and business functions means that protection of the underlying systems is paramount to the success of the Organization. The selection of appropriate security controls for an information system is an important task that can have major implications for the operations and assets of an Organization, as well as, the welfare of individuals. Security controls are the management, operational, and technical safeguards or countermeasures prescribed for an information system to protect the confidentiality, integrity (including nonrepudiation and authenticity), and availability of the system and its information. Once employed within an information system, security controls are assessed to provide the information necessary to determine their overall effectiveness—that is, the extent to which the controls are implemented correctly, operating as intended, and producing the desired outcome with respect to meeting the security requirements for the system. Understanding the overall effectiveness of the security controls implemented in the information system and its environment of operation is essential in determining the risk to the Organization’s operations and assets, to individuals, to other Organizations, and to the nation resulting from use of the system.

  • chapter 2 building a Secure Organization
    Computer and Information Security Handbook (Third Edition), 2013
    Co-Authors: John Mallery
    Abstract:

    This chapter provides guidelines for building effective security assessment plans and a comprehensive set of procedures to assess the effectiveness of security controls employed in information systems. Today’s information systems are complex assemblages of technology (hardware, software, and firmware), processes, and people working together to provide Organizations with the capability to process, store, and transmit information in a timely manner to support various missions and business functions. The degree to which Organizations have come to depend on these information systems to conduct routine, important, and critical missions and business functions means that the protection of underlying systems is paramount to the success of the Organization. The selection of appropriate security controls for an information system is an important task that can have major implications for the operations and assets of an Organization, as well as the welfare of individuals. Security controls are the management, operational, and technical safeguards or countermeasures prescribed for an information system to protect the confidentiality, integrity (including nonrepudiation and authenticity), and availability of the system and its information. Once employed within an information system, security controls are assessed to provide the information necessary to determine their overall effectiveness: that is, the extent to which the controls are implemented correctly, operating as intended, and producing the desired outcome with respect to meeting the security requirements for the system. Understanding the overall effectiveness of the security controls implemented in the information system and its environment of operation is essential in determining the risk to the Organization's operations and assets, to individuals, to other Organizations, and to the nation resulting from use of the system.

  • Chapter 2 – Building a Secure Organization
    Computer and Information Security Handbook, 2013
    Co-Authors: John Mallery
    Abstract:

    This chapter provides guidelines for building effective security assessment plans and a comprehensive set of procedures to assess the effectiveness of security controls employed in information systems. Today’s information systems are complex assemblages of technology (hardware, software, and firmware), processes, and people working together to provide Organizations with the capability to process, store, and transmit information in a timely manner to support various missions and business functions. The degree to which Organizations have come to depend on these information systems to conduct routine, important, and critical missions and business functions means that the protection of underlying systems is paramount to the success of the Organization. The selection of appropriate security controls for an information system is an important task that can have major implications for the operations and assets of an Organization, as well as the welfare of individuals. Security controls are the management, operational, and technical safeguards or countermeasures prescribed for an information system to protect the confidentiality, integrity (including nonrepudiation and authenticity), and availability of the system and its information. Once employed within an information system, security controls are assessed to provide the information necessary to determine their overall effectiveness: that is, the extent to which the controls are implemented correctly, operating as intended, and producing the desired outcome with respect to meeting the security requirements for the system. Understanding the overall effectiveness of the security controls implemented in the information system and its environment of operation is essential in determining the risk to the Organization's operations and assets, to individuals, to other Organizations, and to the nation resulting from use of the system.

  • chapter 1 building a Secure Organization
    Computer and Information Security Handbook, 2009
    Co-Authors: John Mallery
    Abstract:

    Publisher Summary Building a Secure Organization is important to long-term success. When a business implements and maintains a strong security posture, it can take advantage of numerous benefits. An Organization that can demonstrate an infrastructure protected by robust security mechanisms can potentially see a reduction in insurance premiums being paid. A Secure Organization can use its security program as a marketing tool, demonstrating to clients that it values their business so much that it takes a very aggressive stance on protecting their information. Security breaches can cost an Organization significantly through a tarnished reputation, lost business, and legal fees. Numerous regulations, such as the Health Insurance Portability and Accountability Act, the Gramm-Leach-Bliley Act, and the Sarbanes-Oxley Act, require businesses to maintain the security of information. Security, by its very nature, is inconvenient, and the more robust the security mechanisms, the more inconvenient the process becomes. Most security mechanisms, from passwords to multifactor authentication, are seen as roadblocks to productivity. Despite the benefits of maintaining a Secure Organization and the potentially devastating consequences of not doing so, many Organizations have poor security mechanisms, implementations, policies, and culture.

  • Chapter 1 – Building a Secure Organization
    Computer and Information Security Handbook, 2009
    Co-Authors: John Mallery
    Abstract:

    Publisher Summary Building a Secure Organization is important to long-term success. When a business implements and maintains a strong security posture, it can take advantage of numerous benefits. An Organization that can demonstrate an infrastructure protected by robust security mechanisms can potentially see a reduction in insurance premiums being paid. A Secure Organization can use its security program as a marketing tool, demonstrating to clients that it values their business so much that it takes a very aggressive stance on protecting their information. Security breaches can cost an Organization significantly through a tarnished reputation, lost business, and legal fees. Numerous regulations, such as the Health Insurance Portability and Accountability Act, the Gramm-Leach-Bliley Act, and the Sarbanes-Oxley Act, require businesses to maintain the security of information. Security, by its very nature, is inconvenient, and the more robust the security mechanisms, the more inconvenient the process becomes. Most security mechanisms, from passwords to multifactor authentication, are seen as roadblocks to productivity. Despite the benefits of maintaining a Secure Organization and the potentially devastating consequences of not doing so, many Organizations have poor security mechanisms, implementations, policies, and culture.

Zouhair Chiba - One of the best experts on this subject based on the ideXlab platform.

  • Novel Framework Based on Genetic Algorithm and Simulated Annealing Algorithm for Optimization of BP Neural Network Applied to Network IDS
    Proceedings of the 3rd International Conference on Smart City Applications - SCA '18, 2018
    Co-Authors: Zouhair Chiba, Noreddine Abghour, Khalid Moussaid, Amina El Omri, Mohamed Rida
    Abstract:

    Nowadays, network security is a world hot topic in computer security and defense. Intrusions, attacks or anomalies in network infrastructures lead mostly in great financial losses, massive sensitive data leaks, thereby decreasing efficiency and the quality of productivity of an Organization. Network Intrusion Detection System (NIDS) is an effective countermeasure and high-profile method to detect the unauthorized use of computer network and to provide the security for information. Thus, the presence of NIDS in an Organization plays a vital part in attack mitigation, and it has become an integral part of a Secure Organization. In this paper, we propose to optimize a very popular soft computing tool widely used for intrusion detection namely Back Propagation Neural Network (BPNN) using a novel hybrid Framework (GASAA) based on Genetic Algorithm (GA) and Simulated Annealing Algorithm (SAA). Experimental results on KDD CUP' 99 dataset show that our optimized ANIDS (Anomaly NIDS) based BPNN, called "ANIDS BPNN-GASAA" outperforms the original ANIDS BPNN, ANIDS BPNN optimized by using only GA and several traditional and new techniques in terms of detection rate and false positive rate, and it is very much suitable for network anomaly detection.

  • A New Hybrid Framework Based on Improved Genetic Algorithm and Simulated Annealing Algorithm for Optimization of Network IDS Based on BP Neural Network
    Innovations in Smart Cities Applications Edition 2, 2018
    Co-Authors: Zouhair Chiba, Noreddine Abghour, Khalid Moussaid, Amina El Omri, Mohamed Rida
    Abstract:

    Nowadays, network security is a world hot topic in computer security and defense. Intrusions, attacks or anomalies in network infrastructures lead mostly in great financial losses, massive sensitive data leaks, thereby decreasing efficiency and the quality of productivity of an Organization. Network Intrusion Detection System (NIDS) is an effective countermeasure and high-profile method to detect the unauthorized use of computer network and to provide the security for information. Thus, the presence of NIDS in an Organization plays a vital part in attack mitigation, and it has become an integral part of a Secure Organization. In this chapter, we propose to optimize a very popular soft computing tool widely used for intrusion detection namely, Back Propagation Neural Network (BPNN) using a novel hybrid framework (IGASAA) based on Improved Genetic Algorithm (IGA) and Simulated Annealing Algorithm (SAA). Genetic Algorithm (GA) is improved through optimization strategies, namely Parallel Processing and Fitness Value Hashing, which reduce execution time, convergence time and save processing power. Experimental results on KDD CUP’99 dataset show that our optimized ANIDS (Anomaly NIDS) based BPNN, called “ANIDS BPNN-IGASAA” outperforms the original ANIDS BPNN, ANIDS BPNN optimized by using only GA and several traditional and new techniques in terms of detection rate, false positive rate and it is very much appropriate for network anomaly detection.

  • A Hybrid Optimization Framework Based on Genetic Algorithm and Simulated Annealing Algorithm to Enhance Performance of Anomaly Network Intrusion Detection System Based on BP Neural Network
    2018 International Symposium on Advanced Electrical and Communication Technologies (ISAECT), 2018
    Co-Authors: Zouhair Chiba, Noreddine Abghour, Khalid Moussaid, Amina El Omri, Mohamed Rida
    Abstract:

    Today, network security is a world hot topic in computer security and defense. Intrusions and attacks in network infrastructures lead mostly in huge financial losses, massive sensitive data leaks, thus decreasing efficiency, competitiveness and the quality of productivity of an Organization. Network Intrusion Detection System (NIDS) is valuable tool for the defense-in-depth of computer networks. It is widely deployed in network architectures in order to monitor, to detect and eventually respond to any anomalous behavior and misuse which can threat confidentiality, integrity and availability of network resources and services. Thus, the presence of NIDS in an Organization plays a vital part in attack mitigation, and it has become an integral part of a Secure Organization. In this paper, we propose to optimize a very popular soft computing tool widely used for intrusion detection namely Back Propagation Neural Network (BPNN) using a novel hybrid Framework (GASAA) based on improved Genetic Algorithm (GA) and Simulated Annealing Algorithm (SAA). GA is improved through an optimization strategy, namely Fitness Value Hashing (FVH), which reduce execution time, convergence time and save processing power. Experimental results on KDD CUP' 99 dataset show that our optimized ANIDS (Anomaly NIDS) based BPNN, called “ANIDS BPNN-GASAA” outperforms several state-of-art approaches in terms of detection rate and false positive rate. In addition, improvement of GA through FVH has saved processing power and execution time. Thereby, our proposed IDS is very much suitable for network anomaly detection.

Noreddine Abghour - One of the best experts on this subject based on the ideXlab platform.

  • Novel Framework Based on Genetic Algorithm and Simulated Annealing Algorithm for Optimization of BP Neural Network Applied to Network IDS
    Proceedings of the 3rd International Conference on Smart City Applications - SCA '18, 2018
    Co-Authors: Zouhair Chiba, Noreddine Abghour, Khalid Moussaid, Amina El Omri, Mohamed Rida
    Abstract:

    Nowadays, network security is a world hot topic in computer security and defense. Intrusions, attacks or anomalies in network infrastructures lead mostly in great financial losses, massive sensitive data leaks, thereby decreasing efficiency and the quality of productivity of an Organization. Network Intrusion Detection System (NIDS) is an effective countermeasure and high-profile method to detect the unauthorized use of computer network and to provide the security for information. Thus, the presence of NIDS in an Organization plays a vital part in attack mitigation, and it has become an integral part of a Secure Organization. In this paper, we propose to optimize a very popular soft computing tool widely used for intrusion detection namely Back Propagation Neural Network (BPNN) using a novel hybrid Framework (GASAA) based on Genetic Algorithm (GA) and Simulated Annealing Algorithm (SAA). Experimental results on KDD CUP' 99 dataset show that our optimized ANIDS (Anomaly NIDS) based BPNN, called "ANIDS BPNN-GASAA" outperforms the original ANIDS BPNN, ANIDS BPNN optimized by using only GA and several traditional and new techniques in terms of detection rate and false positive rate, and it is very much suitable for network anomaly detection.

  • A New Hybrid Framework Based on Improved Genetic Algorithm and Simulated Annealing Algorithm for Optimization of Network IDS Based on BP Neural Network
    Innovations in Smart Cities Applications Edition 2, 2018
    Co-Authors: Zouhair Chiba, Noreddine Abghour, Khalid Moussaid, Amina El Omri, Mohamed Rida
    Abstract:

    Nowadays, network security is a world hot topic in computer security and defense. Intrusions, attacks or anomalies in network infrastructures lead mostly in great financial losses, massive sensitive data leaks, thereby decreasing efficiency and the quality of productivity of an Organization. Network Intrusion Detection System (NIDS) is an effective countermeasure and high-profile method to detect the unauthorized use of computer network and to provide the security for information. Thus, the presence of NIDS in an Organization plays a vital part in attack mitigation, and it has become an integral part of a Secure Organization. In this chapter, we propose to optimize a very popular soft computing tool widely used for intrusion detection namely, Back Propagation Neural Network (BPNN) using a novel hybrid framework (IGASAA) based on Improved Genetic Algorithm (IGA) and Simulated Annealing Algorithm (SAA). Genetic Algorithm (GA) is improved through optimization strategies, namely Parallel Processing and Fitness Value Hashing, which reduce execution time, convergence time and save processing power. Experimental results on KDD CUP’99 dataset show that our optimized ANIDS (Anomaly NIDS) based BPNN, called “ANIDS BPNN-IGASAA” outperforms the original ANIDS BPNN, ANIDS BPNN optimized by using only GA and several traditional and new techniques in terms of detection rate, false positive rate and it is very much appropriate for network anomaly detection.

  • A Hybrid Optimization Framework Based on Genetic Algorithm and Simulated Annealing Algorithm to Enhance Performance of Anomaly Network Intrusion Detection System Based on BP Neural Network
    2018 International Symposium on Advanced Electrical and Communication Technologies (ISAECT), 2018
    Co-Authors: Zouhair Chiba, Noreddine Abghour, Khalid Moussaid, Amina El Omri, Mohamed Rida
    Abstract:

    Today, network security is a world hot topic in computer security and defense. Intrusions and attacks in network infrastructures lead mostly in huge financial losses, massive sensitive data leaks, thus decreasing efficiency, competitiveness and the quality of productivity of an Organization. Network Intrusion Detection System (NIDS) is valuable tool for the defense-in-depth of computer networks. It is widely deployed in network architectures in order to monitor, to detect and eventually respond to any anomalous behavior and misuse which can threat confidentiality, integrity and availability of network resources and services. Thus, the presence of NIDS in an Organization plays a vital part in attack mitigation, and it has become an integral part of a Secure Organization. In this paper, we propose to optimize a very popular soft computing tool widely used for intrusion detection namely Back Propagation Neural Network (BPNN) using a novel hybrid Framework (GASAA) based on improved Genetic Algorithm (GA) and Simulated Annealing Algorithm (SAA). GA is improved through an optimization strategy, namely Fitness Value Hashing (FVH), which reduce execution time, convergence time and save processing power. Experimental results on KDD CUP' 99 dataset show that our optimized ANIDS (Anomaly NIDS) based BPNN, called “ANIDS BPNN-GASAA” outperforms several state-of-art approaches in terms of detection rate and false positive rate. In addition, improvement of GA through FVH has saved processing power and execution time. Thereby, our proposed IDS is very much suitable for network anomaly detection.

Khalid Moussaid - One of the best experts on this subject based on the ideXlab platform.

  • Novel Framework Based on Genetic Algorithm and Simulated Annealing Algorithm for Optimization of BP Neural Network Applied to Network IDS
    Proceedings of the 3rd International Conference on Smart City Applications - SCA '18, 2018
    Co-Authors: Zouhair Chiba, Noreddine Abghour, Khalid Moussaid, Amina El Omri, Mohamed Rida
    Abstract:

    Nowadays, network security is a world hot topic in computer security and defense. Intrusions, attacks or anomalies in network infrastructures lead mostly in great financial losses, massive sensitive data leaks, thereby decreasing efficiency and the quality of productivity of an Organization. Network Intrusion Detection System (NIDS) is an effective countermeasure and high-profile method to detect the unauthorized use of computer network and to provide the security for information. Thus, the presence of NIDS in an Organization plays a vital part in attack mitigation, and it has become an integral part of a Secure Organization. In this paper, we propose to optimize a very popular soft computing tool widely used for intrusion detection namely Back Propagation Neural Network (BPNN) using a novel hybrid Framework (GASAA) based on Genetic Algorithm (GA) and Simulated Annealing Algorithm (SAA). Experimental results on KDD CUP' 99 dataset show that our optimized ANIDS (Anomaly NIDS) based BPNN, called "ANIDS BPNN-GASAA" outperforms the original ANIDS BPNN, ANIDS BPNN optimized by using only GA and several traditional and new techniques in terms of detection rate and false positive rate, and it is very much suitable for network anomaly detection.

  • A New Hybrid Framework Based on Improved Genetic Algorithm and Simulated Annealing Algorithm for Optimization of Network IDS Based on BP Neural Network
    Innovations in Smart Cities Applications Edition 2, 2018
    Co-Authors: Zouhair Chiba, Noreddine Abghour, Khalid Moussaid, Amina El Omri, Mohamed Rida
    Abstract:

    Nowadays, network security is a world hot topic in computer security and defense. Intrusions, attacks or anomalies in network infrastructures lead mostly in great financial losses, massive sensitive data leaks, thereby decreasing efficiency and the quality of productivity of an Organization. Network Intrusion Detection System (NIDS) is an effective countermeasure and high-profile method to detect the unauthorized use of computer network and to provide the security for information. Thus, the presence of NIDS in an Organization plays a vital part in attack mitigation, and it has become an integral part of a Secure Organization. In this chapter, we propose to optimize a very popular soft computing tool widely used for intrusion detection namely, Back Propagation Neural Network (BPNN) using a novel hybrid framework (IGASAA) based on Improved Genetic Algorithm (IGA) and Simulated Annealing Algorithm (SAA). Genetic Algorithm (GA) is improved through optimization strategies, namely Parallel Processing and Fitness Value Hashing, which reduce execution time, convergence time and save processing power. Experimental results on KDD CUP’99 dataset show that our optimized ANIDS (Anomaly NIDS) based BPNN, called “ANIDS BPNN-IGASAA” outperforms the original ANIDS BPNN, ANIDS BPNN optimized by using only GA and several traditional and new techniques in terms of detection rate, false positive rate and it is very much appropriate for network anomaly detection.

  • A Hybrid Optimization Framework Based on Genetic Algorithm and Simulated Annealing Algorithm to Enhance Performance of Anomaly Network Intrusion Detection System Based on BP Neural Network
    2018 International Symposium on Advanced Electrical and Communication Technologies (ISAECT), 2018
    Co-Authors: Zouhair Chiba, Noreddine Abghour, Khalid Moussaid, Amina El Omri, Mohamed Rida
    Abstract:

    Today, network security is a world hot topic in computer security and defense. Intrusions and attacks in network infrastructures lead mostly in huge financial losses, massive sensitive data leaks, thus decreasing efficiency, competitiveness and the quality of productivity of an Organization. Network Intrusion Detection System (NIDS) is valuable tool for the defense-in-depth of computer networks. It is widely deployed in network architectures in order to monitor, to detect and eventually respond to any anomalous behavior and misuse which can threat confidentiality, integrity and availability of network resources and services. Thus, the presence of NIDS in an Organization plays a vital part in attack mitigation, and it has become an integral part of a Secure Organization. In this paper, we propose to optimize a very popular soft computing tool widely used for intrusion detection namely Back Propagation Neural Network (BPNN) using a novel hybrid Framework (GASAA) based on improved Genetic Algorithm (GA) and Simulated Annealing Algorithm (SAA). GA is improved through an optimization strategy, namely Fitness Value Hashing (FVH), which reduce execution time, convergence time and save processing power. Experimental results on KDD CUP' 99 dataset show that our optimized ANIDS (Anomaly NIDS) based BPNN, called “ANIDS BPNN-GASAA” outperforms several state-of-art approaches in terms of detection rate and false positive rate. In addition, improvement of GA through FVH has saved processing power and execution time. Thereby, our proposed IDS is very much suitable for network anomaly detection.