制御装置及び制御方法
    1.
    发明申请

    公开(公告)号:WO2023089783A1

    公开(公告)日:2023-05-25

    申请号:PCT/JP2021/042643

    申请日:2021-11-19

    发明人: 五嶋 数哉

    IPC分类号: G06F21/55 G05B19/05

    摘要: DoS攻撃等のリソースに過剰な負荷をかけて制御装置やネットワークの稼働を妨害する不正アクセスを容易に検知すること。 制御装置は、少なくとも1つの電子部品の物理状態を測定周期で測定する測定部と、正常状態で任意の動作プログラムそれぞれの実行期間及び待機中毎の電子部品の物理状態を測定周期に対応付けてモデル波形として記録する記録部と、任意の動作プログラムそれぞれの実行期間又は待機中に測定周期に対応付けて測定された電子部品の物理状態と、実行されている任意の動作プログラム又は待機中に対応するモデル波形のうち当該物理状態が測定された測定周期に対応する物理状態との差分の絶対値を、任意の動作プログラムの実行期間又は待機中の一定期間毎に加算し、リソースに過剰な負荷をかけて制御装置やネットワークの稼働を妨害する不正アクセスに対する予め定められた判定閾値を加算した値が超過したか否かを判定する検知部と、を備える。

    APPARATUS, METHODS, AND COMPUTER PROGRAMS
    3.
    发明申请

    公开(公告)号:WO2023082112A1

    公开(公告)日:2023-05-19

    申请号:PCT/CN2021/129895

    申请日:2021-11-10

    IPC分类号: G06F21/55

    摘要: An apparatus for an artificial intelligence, security risk and threat management function located outside at least one AI pipeline executing or configured to execute at least part of an AI model is caused to: perform a first security threat and risk analysis associated with executing the AI model by the at least one AI pipeline to obtain a first value for a security threat and/or risk parameter (901); perform a second security threat and risk analysis associated with executing the AI model by the at least one AI pipeline to obtain a second value for the security threat and/or risk parameter (902); determine whether the change from the first value to the second value causes and/or would cause a predetermined tolerable risk for the security threat and/or risk parameter to be exceeded (903).

    DETECTING INSIDER USER BEHAVIOR THREATS BY COMPARING A CURRENT (LATEST) USER ACTIVITY TO USER ACTIVITIES OF OTHERS

    公开(公告)号:WO2023076919A1

    公开(公告)日:2023-05-04

    申请号:PCT/US2022/078681

    申请日:2022-10-26

    申请人: PROOFPOINT, INC.

    IPC分类号: G06F11/34 G06F21/55 G06F21/57

    摘要: A computer method detect internal user behavior threats by recording user activity data at endpoints on a computer network associated with a tenant, generating a sampled activity matrix for each user, grouping users from the tenant into clusters based on similarity, assigning a user activity weight to each activity-set, creating a ranked list of the user activity-sets for all users within the tenant, computing a user behavior vector for each respective one of the users in the tenant, and comparing the user behavior vector for a particular one of the users in the tenant to other users in the tenant to determine whether the user behavior vector indicates that the user behavior deviates beyond a threshold amount from the other users in the tenant, and, if so, creating an internal user behavior threat notification that may, for example, prompt a real world response.

    ABNORMAL CROSS AUTHORIZATION DETECTION SYSTEMS

    公开(公告)号:WO2023076021A1

    公开(公告)日:2023-05-04

    申请号:PCT/US2022/046141

    申请日:2022-10-10

    摘要: A system to detect abnormal cross authorizations and take action is described. The system determines whether cross authorization event applied to a first trained anomaly detection model and activity post cross authorization event applied to a second trained anomaly detection model is suspicious. An indicator score is determined from rule-based security indications applied to the cross authorization. A security action is taken based on application of the indicator score applied to a threshold.

    MULTITENANT SHARING ANOMALY CYBERATTACK CAMPAIGN DETECTION

    公开(公告)号:WO2023059411A1

    公开(公告)日:2023-04-13

    申请号:PCT/US2022/042407

    申请日:2022-09-02

    摘要: Embodiments detect cyberattack campaigns against multiple cloud tenants by analyzing activity data to find sharing anomalies. Data that appears benign in a single tenant's activities may indicate an attack when the same or similar data is also found for additional tenants. Attack detection may depend on activity time frames, on how similar certain activities of different tenants are to one another, on how unusual it is for different tenants to share an activity, and on other factors. Sharing anomaly analysis may utilize hypergeometric probabilities or other statistical measures. Detection avoidance attempts using entity randomization are revealed and thwarted. Authorized vendors may be recognized, mooting anomalousness. Although data from multiple tenants is analyzed together for sharing anomalies while monitoring for attacks, tenant confidentiality and privacy are respected through technical and legal mechanisms. Mitigation is performed in response to an attack indication.

    制御装置
    7.
    发明申请
    制御装置 审中-公开

    公开(公告)号:WO2023058212A1

    公开(公告)日:2023-04-13

    申请号:PCT/JP2021/037278

    申请日:2021-10-08

    IPC分类号: G06F21/55 B60R25/30

    摘要: サイバー攻撃を受けても制御対象の異常を検知し、安全に制御することを可能とする。状態取得部(101)で取得した制御対象の状態遷移情報と記憶部(102)に記憶した正常時の通信データのリストの関係性から、制御対象の状態が切り替わる所定の時間よりも前に通信データを受信した場合に、リストを変えるまたは変えないことを決定する監視決定部(103)と、監視決定部(103)で決定した対象のリストの通信データを監視する通信監視部(104)と、通信監視部(104)の監視結果とリストを比較し、不正データであるか判定する異常判定部(105)を備えている。

    AUTOMATIC GRAPH-BASED DETECTION OF POTENTIAL SECURITY THREATS

    公开(公告)号:WO2023048921A1

    公开(公告)日:2023-03-30

    申请号:PCT/US2022/042279

    申请日:2022-09-01

    摘要: Techniques are described herein that are capable of performing automatic graph-based detection of potential security threats. A Bayesian network is initialized using an association graph to establish connections among network nodes in the Bayesian network. The network nodes are grouped among clusters that correspond to respective intents. Patterns in the Bayesian network are identified. At least one redundant connection, which is redundant with regard to one or more other connections, is removed from the patterns. Scores are assigned to the respective patterns in the Bayesian network, based on knowledge of historical patterns and historical security threats, such that each score indicates a likelihood of the respective pattern to indicate a security threat. An output graph is automatically generated. The output graph includes each pattern that has a score that is greater than or equal to a score threshold. Each pattern in the output graph represents a potential security threat.

    EFFICIENT VECTOR COMPARISON FOR EVENT IDENTIFICATION

    公开(公告)号:WO2023046404A1

    公开(公告)日:2023-03-30

    申请号:PCT/EP2022/073620

    申请日:2022-08-24

    IPC分类号: G06F21/55 H04L9/40

    摘要: A computer implemented method for detecting the existence of a condition indicated by a signature vector sequence of events in an input vector sequence of events, each of the signature and input vector sequences being constitute by an ordered sequence of vectors, the method comprising: converting the signature vector sequence into an signature ordered numerical sequence in which each vector in the signature vector sequence is converted to a number indicative of a magnitude of the vector such that the signature numerical sequence is a sequence of magnitudes in the order of the signature vector sequence; converting the input vector sequence into an input ordered numerical sequence in which each vector in the input vector sequence is converted to a number indicative of a magnitude of the vector such that the input numerical sequence is a sequence of magnitudes in the order of the input vector sequence; determining a degree of similarity of the signature numerical sequence and the input numerical sequence to detect the existence of the condition indicated by the input numerical sequence.