diff --git a/anomaly-detection/lib/__init__.py b/anomaly-detection/lib/__init__.py
new file mode 100644
index 0000000..f02a48a
--- /dev/null
+++ b/anomaly-detection/lib/__init__.py
@@ -0,0 +1,22 @@
+"""
+Process Anomaly Detection - Constants and Utilities
+"""
+
+import logging
+
+logger = logging.getLogger(__name__)
+MAX_SYSCALLS = 548
+
+
+def comm_for_pid(pid: int) -> bytes | None:
+    """Get process name from /proc."""
+    try:
+        with open(f"/proc/{pid}/comm", "rb") as f:
+            return f.read().strip()
+    except FileNotFoundError:
+        logger.warning(f"Process with PID {pid} not found.")
+    except PermissionError:
+        logger.warning(f"Permission denied when accessing /proc/{pid}/comm.")
+    except Exception as e:
+        logger.warning(f"Error reading /proc/{pid}/comm: {e}")
+    return None
diff --git a/anomaly-detection/lib/ml.py b/anomaly-detection/lib/ml.py
new file mode 100644
index 0000000..9b0c395
--- /dev/null
+++ b/anomaly-detection/lib/ml.py
@@ -0,0 +1,173 @@
+"""
+Autoencoder for Process Behavior Anomaly Detection
+
+Uses Keras/TensorFlow to train an autoencoder on syscall patterns.
+Anomalies are detected when reconstruction error exceeds threshold.
+"""
+
+import logging
+import os
+
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from tensorflow import keras
+
+from lib import MAX_SYSCALLS
+
+logger = logging.getLogger(__name__)
+
+
+def create_autoencoder(n_inputs: int = MAX_SYSCALLS) -> keras.Model:
+    """
+    Create the autoencoder architecture.
+
+    Architecture: input → encoder → bottleneck → decoder → output
+    """
+    inp = keras.Input(shape=(n_inputs,))
+
+    # Encoder
+    encoder = keras.layers.Dense(n_inputs)(inp)
+    encoder = keras.layers.ReLU()(encoder)
+
+    # Bottleneck (compressed representation)
+    bottleneck = keras.layers.Dense(n_inputs // 2)(encoder)
+
+    # Decoder
+    decoder = keras.layers.Dense(n_inputs)(bottleneck)
+    decoder = keras.layers.ReLU()(decoder)
+    output = keras.layers.Dense(n_inputs, activation="linear")(decoder)
+
+    model = keras.Model(inp, output)
+    model.compile(optimizer="adam", loss="mse")
+
+    return model
+
+
+class AutoEncoder:
+    """
+    Autoencoder for syscall pattern anomaly detection.
+
+    Usage:
+        # Training
+        ae = AutoEncoder('model.keras')
+        model, threshold = ae.train('data.csv', epochs=200)
+
+        # Inference
+        ae = AutoEncoder('model.keras', load=True)
+        _, errors, total_error = ae.predict([features])
+    """
+
+    def __init__(self, filename: str, load: bool = False):
+        self.filename = filename
+        self.model = None
+
+        if load:
+            self._load_model()
+
+    def _load_model(self) -> None:
+        """Load a trained model from disk."""
+        if not os.path.exists(self.filename):
+            raise FileNotFoundError(f"Model file not found: {self.filename}")
+
+        logger.info(f"Loading model from {self.filename}")
+        self.model = keras.models.load_model(self.filename)
+
+    def train(
+        self,
+        datafile: str,
+        epochs: int,
+        batch_size: int,
+        test_size: float = 0.1,
+    ) -> tuple[keras.Model, float]:
+        """
+        Train the autoencoder on collected data.
+
+        Args:
+            datafile: Path to CSV file with training data
+            epochs: Number of training epochs
+            batch_size: Training batch size
+            test_size: Fraction of data to use for validation
+
+        Returns:
+            Tuple of (trained model, error threshold)
+        """
+        if not os.path.exists(datafile):
+            raise FileNotFoundError(f"Data file not found: {datafile}")
+
+        logger.info(f"Loading training data from {datafile}")
+
+        # Load and prepare data
+        df = pd.read_csv(datafile)
+        features = df.drop(["sample_time"], axis=1).values
+
+        logger.info(f"Loaded {len(features)} samples with {features.shape[1]} features")
+
+        # Split train/test
+        train_data, test_data = train_test_split(
+            features,
+            test_size=test_size,
+            random_state=42,
+        )
+
+        logger.info(f"Training set: {len(train_data)} samples")
+        logger.info(f"Test set: {len(test_data)} samples")
+
+        # Create and train model
+        self.model = create_autoencoder()
+
+        if self.model is None:
+            raise RuntimeError("Failed to create the autoencoder model.")
+
+        logger.info("Training autoencoder...")
+        self.model.fit(
+            train_data,
+            train_data,
+            validation_data=(test_data, test_data),
+            epochs=epochs,
+            batch_size=batch_size,
+            verbose=1,
+        )
+
+        # Save model (use .keras format for Keras 3.x compatibility)
+        self.model.save(self.filename)
+        logger.info(f"Model saved to {self.filename}")
+
+        # Calculate error threshold from test data
+        threshold = self._calculate_threshold(test_data)
+
+        return self.model, threshold
+
+    def _calculate_threshold(self, test_data: np.ndarray) -> float:
+        """Calculate error threshold from test data."""
+        logger.info(f"Calculating error threshold from {len(test_data)} test samples")
+
+        if self.model is None:
+            raise RuntimeError("Model not loaded. Use load=True or train first.")
+
+        predictions = self.model.predict(test_data, verbose=0)
+        errors = np.abs(test_data - predictions).sum(axis=1)
+
+        return float(errors.max())
+
+    def predict(self, X: list | np.ndarray) -> tuple[np.ndarray, np.ndarray, float]:
+        """
+        Run prediction and return reconstruction error.
+
+        Args:
+            X: Input data (list of feature vectors)
+
+        Returns:
+            Tuple of (reconstructed, per_feature_errors, total_error)
+        """
+        if self.model is None:
+            raise RuntimeError("Model not loaded. Use load=True or train first.")
+
+        X = np.asarray(X, dtype=np.float32)
+        y = self.model.predict(X, verbose=0)
+
+        # Per-feature reconstruction error
+        errors = np.abs(X[0] - y[0])
+        total_error = float(errors.sum())
+
+        return y, errors, total_error
diff --git a/anomaly-detection/lib/platform.py b/anomaly-detection/lib/platform.py
new file mode 100644
index 0000000..5e996ee
--- /dev/null
+++ b/anomaly-detection/lib/platform.py
@@ -0,0 +1,448 @@
+# Copyright 2017 Sasha Goldshtein
+# Copyright 2018 Red Hat, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+syscall.py contains functions useful for mapping between syscall names and numbers
+"""
+
+# Syscall table for Linux x86_64, not very recent. Automatically generated from
+# https://git.kernel.org/pub/scm/linux/kernel/git/stable/linux.git/tree/arch/x86/entry/syscalls/syscall_64.tbl?h=linux-6.17.y
+# using the following command:
+#
+# cat arch/x86/entry/syscalls/syscall_64.tbl \
+#     | awk 'BEGIN    { print "syscalls = {"         }
+#            /^[0-9]/ { print "    "$1": b\""$3"\"," }
+#            END      { print "}"                    }'
+
+SYSCALLS = {
+    0: b"read",
+    1: b"write",
+    2: b"open",
+    3: b"close",
+    4: b"stat",
+    5: b"fstat",
+    6: b"lstat",
+    7: b"poll",
+    8: b"lseek",
+    9: b"mmap",
+    10: b"mprotect",
+    11: b"munmap",
+    12: b"brk",
+    13: b"rt_sigaction",
+    14: b"rt_sigprocmask",
+    15: b"rt_sigreturn",
+    16: b"ioctl",
+    17: b"pread64",
+    18: b"pwrite64",
+    19: b"readv",
+    20: b"writev",
+    21: b"access",
+    22: b"pipe",
+    23: b"select",
+    24: b"sched_yield",
+    25: b"mremap",
+    26: b"msync",
+    27: b"mincore",
+    28: b"madvise",
+    29: b"shmget",
+    30: b"shmat",
+    31: b"shmctl",
+    32: b"dup",
+    33: b"dup2",
+    34: b"pause",
+    35: b"nanosleep",
+    36: b"getitimer",
+    37: b"alarm",
+    38: b"setitimer",
+    39: b"getpid",
+    40: b"sendfile",
+    41: b"socket",
+    42: b"connect",
+    43: b"accept",
+    44: b"sendto",
+    45: b"recvfrom",
+    46: b"sendmsg",
+    47: b"recvmsg",
+    48: b"shutdown",
+    49: b"bind",
+    50: b"listen",
+    51: b"getsockname",
+    52: b"getpeername",
+    53: b"socketpair",
+    54: b"setsockopt",
+    55: b"getsockopt",
+    56: b"clone",
+    57: b"fork",
+    58: b"vfork",
+    59: b"execve",
+    60: b"exit",
+    61: b"wait4",
+    62: b"kill",
+    63: b"uname",
+    64: b"semget",
+    65: b"semop",
+    66: b"semctl",
+    67: b"shmdt",
+    68: b"msgget",
+    69: b"msgsnd",
+    70: b"msgrcv",
+    71: b"msgctl",
+    72: b"fcntl",
+    73: b"flock",
+    74: b"fsync",
+    75: b"fdatasync",
+    76: b"truncate",
+    77: b"ftruncate",
+    78: b"getdents",
+    79: b"getcwd",
+    80: b"chdir",
+    81: b"fchdir",
+    82: b"rename",
+    83: b"mkdir",
+    84: b"rmdir",
+    85: b"creat",
+    86: b"link",
+    87: b"unlink",
+    88: b"symlink",
+    89: b"readlink",
+    90: b"chmod",
+    91: b"fchmod",
+    92: b"chown",
+    93: b"fchown",
+    94: b"lchown",
+    95: b"umask",
+    96: b"gettimeofday",
+    97: b"getrlimit",
+    98: b"getrusage",
+    99: b"sysinfo",
+    100: b"times",
+    101: b"ptrace",
+    102: b"getuid",
+    103: b"syslog",
+    104: b"getgid",
+    105: b"setuid",
+    106: b"setgid",
+    107: b"geteuid",
+    108: b"getegid",
+    109: b"setpgid",
+    110: b"getppid",
+    111: b"getpgrp",
+    112: b"setsid",
+    113: b"setreuid",
+    114: b"setregid",
+    115: b"getgroups",
+    116: b"setgroups",
+    117: b"setresuid",
+    118: b"getresuid",
+    119: b"setresgid",
+    120: b"getresgid",
+    121: b"getpgid",
+    122: b"setfsuid",
+    123: b"setfsgid",
+    124: b"getsid",
+    125: b"capget",
+    126: b"capset",
+    127: b"rt_sigpending",
+    128: b"rt_sigtimedwait",
+    129: b"rt_sigqueueinfo",
+    130: b"rt_sigsuspend",
+    131: b"sigaltstack",
+    132: b"utime",
+    133: b"mknod",
+    134: b"uselib",
+    135: b"personality",
+    136: b"ustat",
+    137: b"statfs",
+    138: b"fstatfs",
+    139: b"sysfs",
+    140: b"getpriority",
+    141: b"setpriority",
+    142: b"sched_setparam",
+    143: b"sched_getparam",
+    144: b"sched_setscheduler",
+    145: b"sched_getscheduler",
+    146: b"sched_get_priority_max",
+    147: b"sched_get_priority_min",
+    148: b"sched_rr_get_interval",
+    149: b"mlock",
+    150: b"munlock",
+    151: b"mlockall",
+    152: b"munlockall",
+    153: b"vhangup",
+    154: b"modify_ldt",
+    155: b"pivot_root",
+    156: b"_sysctl",
+    157: b"prctl",
+    158: b"arch_prctl",
+    159: b"adjtimex",
+    160: b"setrlimit",
+    161: b"chroot",
+    162: b"sync",
+    163: b"acct",
+    164: b"settimeofday",
+    165: b"mount",
+    166: b"umount2",
+    167: b"swapon",
+    168: b"swapoff",
+    169: b"reboot",
+    170: b"sethostname",
+    171: b"setdomainname",
+    172: b"iopl",
+    173: b"ioperm",
+    174: b"create_module",
+    175: b"init_module",
+    176: b"delete_module",
+    177: b"get_kernel_syms",
+    178: b"query_module",
+    179: b"quotactl",
+    180: b"nfsservctl",
+    181: b"getpmsg",
+    182: b"putpmsg",
+    183: b"afs_syscall",
+    184: b"tuxcall",
+    185: b"security",
+    186: b"gettid",
+    187: b"readahead",
+    188: b"setxattr",
+    189: b"lsetxattr",
+    190: b"fsetxattr",
+    191: b"getxattr",
+    192: b"lgetxattr",
+    193: b"fgetxattr",
+    194: b"listxattr",
+    195: b"llistxattr",
+    196: b"flistxattr",
+    197: b"removexattr",
+    198: b"lremovexattr",
+    199: b"fremovexattr",
+    200: b"tkill",
+    201: b"time",
+    202: b"futex",
+    203: b"sched_setaffinity",
+    204: b"sched_getaffinity",
+    205: b"set_thread_area",
+    206: b"io_setup",
+    207: b"io_destroy",
+    208: b"io_getevents",
+    209: b"io_submit",
+    210: b"io_cancel",
+    211: b"get_thread_area",
+    212: b"lookup_dcookie",
+    213: b"epoll_create",
+    214: b"epoll_ctl_old",
+    215: b"epoll_wait_old",
+    216: b"remap_file_pages",
+    217: b"getdents64",
+    218: b"set_tid_address",
+    219: b"restart_syscall",
+    220: b"semtimedop",
+    221: b"fadvise64",
+    222: b"timer_create",
+    223: b"timer_settime",
+    224: b"timer_gettime",
+    225: b"timer_getoverrun",
+    226: b"timer_delete",
+    227: b"clock_settime",
+    228: b"clock_gettime",
+    229: b"clock_getres",
+    230: b"clock_nanosleep",
+    231: b"exit_group",
+    232: b"epoll_wait",
+    233: b"epoll_ctl",
+    234: b"tgkill",
+    235: b"utimes",
+    236: b"vserver",
+    237: b"mbind",
+    238: b"set_mempolicy",
+    239: b"get_mempolicy",
+    240: b"mq_open",
+    241: b"mq_unlink",
+    242: b"mq_timedsend",
+    243: b"mq_timedreceive",
+    244: b"mq_notify",
+    245: b"mq_getsetattr",
+    246: b"kexec_load",
+    247: b"waitid",
+    248: b"add_key",
+    249: b"request_key",
+    250: b"keyctl",
+    251: b"ioprio_set",
+    252: b"ioprio_get",
+    253: b"inotify_init",
+    254: b"inotify_add_watch",
+    255: b"inotify_rm_watch",
+    256: b"migrate_pages",
+    257: b"openat",
+    258: b"mkdirat",
+    259: b"mknodat",
+    260: b"fchownat",
+    261: b"futimesat",
+    262: b"newfstatat",
+    263: b"unlinkat",
+    264: b"renameat",
+    265: b"linkat",
+    266: b"symlinkat",
+    267: b"readlinkat",
+    268: b"fchmodat",
+    269: b"faccessat",
+    270: b"pselect6",
+    271: b"ppoll",
+    272: b"unshare",
+    273: b"set_robust_list",
+    274: b"get_robust_list",
+    275: b"splice",
+    276: b"tee",
+    277: b"sync_file_range",
+    278: b"vmsplice",
+    279: b"move_pages",
+    280: b"utimensat",
+    281: b"epoll_pwait",
+    282: b"signalfd",
+    283: b"timerfd_create",
+    284: b"eventfd",
+    285: b"fallocate",
+    286: b"timerfd_settime",
+    287: b"timerfd_gettime",
+    288: b"accept4",
+    289: b"signalfd4",
+    290: b"eventfd2",
+    291: b"epoll_create1",
+    292: b"dup3",
+    293: b"pipe2",
+    294: b"inotify_init1",
+    295: b"preadv",
+    296: b"pwritev",
+    297: b"rt_tgsigqueueinfo",
+    298: b"perf_event_open",
+    299: b"recvmmsg",
+    300: b"fanotify_init",
+    301: b"fanotify_mark",
+    302: b"prlimit64",
+    303: b"name_to_handle_at",
+    304: b"open_by_handle_at",
+    305: b"clock_adjtime",
+    306: b"syncfs",
+    307: b"sendmmsg",
+    308: b"setns",
+    309: b"getcpu",
+    310: b"process_vm_readv",
+    311: b"process_vm_writev",
+    312: b"kcmp",
+    313: b"finit_module",
+    314: b"sched_setattr",
+    315: b"sched_getattr",
+    316: b"renameat2",
+    317: b"seccomp",
+    318: b"getrandom",
+    319: b"memfd_create",
+    320: b"kexec_file_load",
+    321: b"bpf",
+    322: b"execveat",
+    323: b"userfaultfd",
+    324: b"membarrier",
+    325: b"mlock2",
+    326: b"copy_file_range",
+    327: b"preadv2",
+    328: b"pwritev2",
+    329: b"pkey_mprotect",
+    330: b"pkey_alloc",
+    331: b"pkey_free",
+    332: b"statx",
+    333: b"io_pgetevents",
+    334: b"rseq",
+    335: b"uretprobe",
+    424: b"pidfd_send_signal",
+    425: b"io_uring_setup",
+    426: b"io_uring_enter",
+    427: b"io_uring_register",
+    428: b"open_tree",
+    429: b"move_mount",
+    430: b"fsopen",
+    431: b"fsconfig",
+    432: b"fsmount",
+    433: b"fspick",
+    434: b"pidfd_open",
+    435: b"clone3",
+    436: b"close_range",
+    437: b"openat2",
+    438: b"pidfd_getfd",
+    439: b"faccessat2",
+    440: b"process_madvise",
+    441: b"epoll_pwait2",
+    442: b"mount_setattr",
+    443: b"quotactl_fd",
+    444: b"landlock_create_ruleset",
+    445: b"landlock_add_rule",
+    446: b"landlock_restrict_self",
+    447: b"memfd_secret",
+    448: b"process_mrelease",
+    449: b"futex_waitv",
+    450: b"set_mempolicy_home_node",
+    451: b"cachestat",
+    452: b"fchmodat2",
+    453: b"map_shadow_stack",
+    454: b"futex_wake",
+    455: b"futex_wait",
+    456: b"futex_requeue",
+    457: b"statmount",
+    458: b"listmount",
+    459: b"lsm_get_self_attr",
+    460: b"lsm_set_self_attr",
+    461: b"lsm_list_modules",
+    462: b"mseal",
+    463: b"setxattrat",
+    464: b"getxattrat",
+    465: b"listxattrat",
+    466: b"removexattrat",
+    467: b"open_tree_attr",
+    468: b"file_getattr",
+    469: b"file_setattr",
+    512: b"rt_sigaction",
+    513: b"rt_sigreturn",
+    514: b"ioctl",
+    515: b"readv",
+    516: b"writev",
+    517: b"recvfrom",
+    518: b"sendmsg",
+    519: b"recvmsg",
+    520: b"execve",
+    521: b"ptrace",
+    522: b"rt_sigpending",
+    523: b"rt_sigtimedwait",
+    524: b"rt_sigqueueinfo",
+    525: b"sigaltstack",
+    526: b"timer_create",
+    527: b"mq_notify",
+    528: b"kexec_load",
+    529: b"waitid",
+    530: b"set_robust_list",
+    531: b"get_robust_list",
+    532: b"vmsplice",
+    533: b"move_pages",
+    534: b"preadv",
+    535: b"pwritev",
+    536: b"rt_tgsigqueueinfo",
+    537: b"recvmmsg",
+    538: b"sendmmsg",
+    539: b"process_vm_readv",
+    540: b"process_vm_writev",
+    541: b"setsockopt",
+    542: b"getsockopt",
+    543: b"io_setup",
+    544: b"io_submit",
+    545: b"execveat",
+    546: b"preadv2",
+    547: b"pwritev2",
+}
diff --git a/anomaly-detection/lib/probe.py b/anomaly-detection/lib/probe.py
new file mode 100644
index 0000000..84353d9
--- /dev/null
+++ b/anomaly-detection/lib/probe.py
@@ -0,0 +1,117 @@
+"""
+PythonBPF eBPF Probe for Syscall Histogram Collection
+"""
+
+from vmlinux import struct_trace_event_raw_sys_enter
+from pythonbpf import bpf, map, section, bpfglobal, BPF
+from pythonbpf.helper import pid
+from pythonbpf.maps import HashMap
+from ctypes import c_int64
+from lib import MAX_SYSCALLS, comm_for_pid
+
+
+@bpf
+@map
+def histogram() -> HashMap:
+    return HashMap(key=c_int64, value=c_int64, max_entries=1024)
+
+
+@bpf
+@map
+def target_pid_map() -> HashMap:
+    return HashMap(key=c_int64, value=c_int64, max_entries=1)
+
+
+@bpf
+@section("tracepoint/raw_syscalls/sys_enter")
+def trace_syscall(ctx: struct_trace_event_raw_sys_enter) -> c_int64:
+    syscall_id = ctx.id
+    current_pid = pid()
+    target = target_pid_map.lookup(0)
+    if target:
+        if current_pid != target:
+            return 0  # type: ignore
+    if syscall_id < 0 or syscall_id >= 548:
+        return 0  # type: ignore
+    count = histogram.lookup(syscall_id)
+    if count:
+        histogram.update(syscall_id, count + 1)
+    else:
+        histogram.update(syscall_id, 1)
+    return 0  # type: ignore
+
+
+@bpf
+@bpfglobal
+def LICENSE() -> str:
+    return "GPL"
+
+
+ebpf_prog = BPF()
+
+
+class Probe:
+    """
+    Syscall histogram probe for a target process.
+
+    Usage:
+        probe = Probe(target_pid=1234)
+        probe.start()
+        histogram = probe.get_histogram()
+    """
+
+    def __init__(self, target_pid: int, max_syscalls: int = MAX_SYSCALLS):
+        self.target_pid = target_pid
+        self.max_syscalls = max_syscalls
+        self.comm = comm_for_pid(target_pid)
+
+        if self.comm is None:
+            raise ValueError(f"Cannot find process with PID {target_pid}")
+
+        self._bpf = None
+        self._histogram_map = None
+        self._target_map = None
+
+    def start(self):
+        """Compile, load, and attach the BPF probe."""
+        # Compile and load
+        self._bpf = ebpf_prog
+        self._bpf.load()
+        self._bpf.attach_all()
+
+        # Get map references
+        self._histogram_map = self._bpf["histogram"]
+        self._target_map = self._bpf["target_pid_map"]
+
+        # Set target PID in the map
+        self._target_map.update(0, self.target_pid)
+
+        return self
+
+    def get_histogram(self) -> list:
+        """Read current histogram values as a list."""
+        if self._histogram_map is None:
+            raise RuntimeError("Probe not started.  Call start() first.")
+
+        result = [0] * self.max_syscalls
+
+        for syscall_id in range(self.max_syscalls):
+            try:
+                count = self._histogram_map.lookup(syscall_id)
+                if count is not None:
+                    result[syscall_id] = int(count)
+            except Exception:
+                pass
+
+        return result
+
+    def __getitem__(self, syscall_id: int) -> int:
+        """Allow indexing: probe[syscall_id]"""
+        if self._histogram_map is None:
+            raise RuntimeError("Probe not started")
+
+        try:
+            count = self._histogram_map.lookup(syscall_id)
+            return int(count) if count is not None else 0
+        except Exception:
+            return 0
diff --git a/anomaly-detection/main.py b/anomaly-detection/main.py
new file mode 100644
index 0000000..fbcb2ae
--- /dev/null
+++ b/anomaly-detection/main.py
@@ -0,0 +1,335 @@
+#!/usr/bin/env python3
+"""
+Process Behavior Anomaly Detection using PythonBPF and Autoencoders
+
+Ported from evilsocket's BCC implementation to PythonBPF.
+https://github.com/evilsocket/ebpf-process-anomaly-detection
+
+Usage:
+    # 1.Learn normal behavior from a process
+    sudo python main.py --learn --pid 1234 --data normal.csv
+
+    # 2.Train the autoencoder (no sudo needed)
+    python main.py --train --data normal.csv --model model.h5
+
+    # 3.Monitor for anomalies
+    sudo python main.py --run --pid 1234 --model model.h5
+"""
+
+import argparse
+import logging
+import os
+import sys
+import time
+from collections import Counter
+
+from lib import MAX_SYSCALLS
+from lib.ml import AutoEncoder
+from lib.platform import SYSCALLS
+from lib.probe import Probe
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+)
+logger = logging.getLogger(__name__)
+
+
+def learn(pid: int, data_path: str, poll_interval_ms: int) -> None:
+    """
+    Capture syscall patterns from target process.
+
+    Args:
+        pid: Target process ID
+        data_path: Path to save CSV data
+        poll_interval_ms: Polling interval in milliseconds
+    """
+    if os.path.exists(data_path):
+        logger.error(
+            f"{data_path} already exists.Delete it or use a different filename."
+        )
+        sys.exit(1)
+
+    try:
+        probe = Probe(pid)
+    except ValueError as e:
+        logger.error(str(e))
+        sys.exit(1)
+
+    probe_comm = probe.comm.decode() if probe.comm else "unknown"
+
+    print(f"📊 Learning from process {pid} ({probe_comm})")
+    print(f"📁 Saving data to {data_path}")
+    print(f"⏱️  Polling interval: {poll_interval_ms}ms")
+    print("Press Ctrl+C to stop...\n")
+
+    probe.start()
+
+    prev_histogram = [0.0] * MAX_SYSCALLS
+    prev_report_time = time.time()
+    sample_count = 0
+    poll_interval_sec = poll_interval_ms / 1000.0
+
+    header = "sample_time," + ",".join(f"sys_{i}" for i in range(MAX_SYSCALLS))
+
+    with open(data_path, "w") as fp:
+        fp.write(header + "\n")
+
+        try:
+            while True:
+                histogram = [float(x) for x in probe.get_histogram()]
+
+                if histogram != prev_histogram:
+                    deltas = _compute_deltas(prev_histogram, histogram)
+                    prev_histogram = histogram.copy()
+
+                    row = f"{time.time()},{','.join(map(str, deltas))}"
+                    fp.write(row + "\n")
+                    fp.flush()
+                    sample_count += 1
+
+                now = time.time()
+                if now - prev_report_time >= 1.0:
+                    print(f"  {sample_count} samples saved...")
+                    prev_report_time = now
+
+                time.sleep(poll_interval_sec)
+
+        except KeyboardInterrupt:
+            print(f"\n✅ Stopped. Saved {sample_count} samples to {data_path}")
+
+
+def train(data_path: str, model_path: str, epochs: int, batch_size: int) -> None:
+    """
+    Train autoencoder on captured data.
+
+    Args:
+        data_path: Path to training CSV data
+        model_path: Path to save trained model
+        epochs: Number of training epochs
+        batch_size: Training batch size
+    """
+    if not os.path.exists(data_path):
+        logger.error(f"Data file {data_path} not found.Run --learn first.")
+        sys.exit(1)
+
+    print(f"🧠 Training autoencoder on {data_path}")
+    print(f"   Epochs: {epochs}")
+    print(f"   Batch size: {batch_size}")
+    print()
+
+    ae = AutoEncoder(model_path)
+    _, threshold = ae.train(data_path, epochs, batch_size)
+
+    print()
+    print("=" * 50)
+    print("✅ Training complete!")
+    print(f"   Model saved to: {model_path}")
+    print(f"   Error threshold: {threshold:.6f}")
+    print()
+    print(f"💡 Use --max-error {threshold:.4f} when running detection")
+    print("=" * 50)
+
+
+def run(pid: int, model_path: str, max_error: float, poll_interval_ms: int) -> None:
+    """
+    Monitor process and detect anomalies.
+
+    Args:
+        pid: Target process ID
+        model_path: Path to trained model
+        max_error: Anomaly detection threshold
+        poll_interval_ms: Polling interval in milliseconds
+    """
+    if not os.path.exists(model_path):
+        logger.error(f"Model file {model_path} not found. Run --train first.")
+        sys.exit(1)
+
+    try:
+        probe = Probe(pid)
+    except ValueError as e:
+        logger.error(str(e))
+        sys.exit(1)
+
+    ae = AutoEncoder(model_path, load=True)
+    probe_comm = probe.comm.decode() if probe.comm else "unknown"
+
+    print(f"🔍 Monitoring process {pid} ({probe_comm}) for anomalies")
+    print(f"   Error threshold: {max_error}")
+    print(f"   Polling interval: {poll_interval_ms}ms")
+    print("Press Ctrl+C to stop...\n")
+
+    probe.start()
+
+    prev_histogram = [0.0] * MAX_SYSCALLS
+    anomaly_count = 0
+    check_count = 0
+    poll_interval_sec = poll_interval_ms / 1000.0
+
+    try:
+        while True:
+            histogram = [float(x) for x in probe.get_histogram()]
+
+            if histogram != prev_histogram:
+                deltas = _compute_deltas(prev_histogram, histogram)
+                prev_histogram = histogram.copy()
+                check_count += 1
+
+                _, feat_errors, total_error = ae.predict([deltas])
+
+                if total_error > max_error:
+                    anomaly_count += 1
+                    _report_anomaly(anomaly_count, total_error, max_error, feat_errors)
+
+            time.sleep(poll_interval_sec)
+
+    except KeyboardInterrupt:
+        print("\n✅ Stopped.")
+        print(f"   Checks performed: {check_count}")
+        print(f"   Anomalies detected: {anomaly_count}")
+
+
+def _compute_deltas(prev: list[float], current: list[float]) -> list[float]:
+    """Compute rate of change between two histograms."""
+    deltas = []
+    for p, c in zip(prev, current):
+        if c != 0.0:
+            delta = 1.0 - (p / c)
+        else:
+            delta = 0.0
+        deltas.append(delta)
+    return deltas
+
+
+def _report_anomaly(
+    count: int,
+    total_error: float,
+    threshold: float,
+    feat_errors: list[float],
+) -> None:
+    """Print anomaly report with top offending syscalls."""
+    print(f"🚨 ANOMALY #{count} detected!")
+    print(f"   Total error: {total_error:.4f} (threshold: {threshold})")
+
+    errors_by_syscall = {idx: err for idx, err in enumerate(feat_errors)}
+    top3 = Counter(errors_by_syscall).most_common(3)
+
+    print("   Top anomalous syscalls:")
+    for idx, err in top3:
+        name = SYSCALLS.get(idx, f"syscall_{idx}")
+        print(f"     • {name!r}: {err:.4f}")
+    print()
+
+
+def parse_args() -> argparse.Namespace:
+    """Parse command line arguments."""
+    parser = argparse.ArgumentParser(
+        description="Process anomaly detection with PythonBPF and Autoencoders",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  # Learn from a process (e.g., Firefox) for a few minutes
+  sudo python main.py --learn --pid $(pgrep -o firefox) --data firefox.csv
+
+  # Train the model (no sudo needed)
+  python main.py --train --data firefox.csv --model firefox.h5
+
+  # Monitor the same process for anomalies
+  sudo python main.py --run --pid $(pgrep -o firefox) --model firefox.h5
+
+  # Full workflow for nginx:
+  sudo python main.py --learn --pid $(pgrep -o nginx) --data nginx_normal.csv
+  python main.py --train --data nginx_normal.csv --model nginx.h5 --epochs 100
+  sudo python main.py --run --pid $(pgrep -o nginx) --model nginx.h5 --max-error 0.05
+        """,
+    )
+
+    actions = parser.add_mutually_exclusive_group()
+    actions.add_argument(
+        "--learn",
+        action="store_true",
+        help="Capture syscall patterns from a process",
+    )
+    actions.add_argument(
+        "--train",
+        action="store_true",
+        help="Train autoencoder on captured data",
+    )
+    actions.add_argument(
+        "--run",
+        action="store_true",
+        help="Monitor process for anomalies",
+    )
+
+    parser.add_argument(
+        "--pid",
+        type=int,
+        default=0,
+        help="Target process ID",
+    )
+    parser.add_argument(
+        "--data",
+        default="data.csv",
+        help="CSV file for training data (default: data.csv)",
+    )
+    parser.add_argument(
+        "--model",
+        default="model.keras",
+        help="Model file path (default: model.h5)",
+    )
+    parser.add_argument(
+        "--time",
+        type=int,
+        default=100,
+        help="Polling interval in milliseconds (default: 100)",
+    )
+    parser.add_argument(
+        "--epochs",
+        type=int,
+        default=200,
+        help="Training epochs (default: 200)",
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=16,
+        help="Training batch size (default: 16)",
+    )
+    parser.add_argument(
+        "--max-error",
+        type=float,
+        default=0.09,
+        help="Anomaly detection threshold (default: 0.09)",
+    )
+
+    return parser.parse_args()
+
+
+def main() -> None:
+    """Main entry point."""
+    args = parse_args()
+
+    if not any([args.learn, args.train, args.run]):
+        print("No action specified.Use --learn, --train, or --run.")
+        print("Run with --help for usage information.")
+        sys.exit(0)
+
+    if args.learn:
+        if args.pid == 0:
+            logger.error("--pid required for --learn")
+            sys.exit(1)
+        learn(args.pid, args.data, args.time)
+
+    elif args.train:
+        train(args.data, args.model, args.epochs, args.batch_size)
+
+    elif args.run:
+        if args.pid == 0:
+            logger.error("--pid required for --run")
+            sys.exit(1)
+        run(args.pid, args.model, args.max_error, args.time)
+
+
+if __name__ == "__main__":
+    main()